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  • 151.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Improving Discriminative Correlation Filters for Visual Tracking2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Generic visual tracking is one of the classical problems in computer vision. In this problem, no prior knowledge of the target is available aside from a bounding box in the initial frame of the sequence. The generic visual tracking is a difficult task due to a number of factors such as momentary occlusions, target rotations, changes in target illumination and variations in the target size. In recent years, discriminative correlation filter (DCF) based trackers have shown promising results for visual tracking. These DCF based methods use the Fourier transform to efficiently calculate detection and model updates, allowing significantly higher frame rates than competing methods. However, existing DCF based methods only estimate translation of the object while ignoring changes in size.This thesis investigates the problem of accurately estimating the scale variations within a DCF based framework. A novel scale estimation method is proposed by explicitly constructing translation and scale filters. The proposed scale estimation technique is robust and significantly improve the tracking performance, while operating at real-time. In addition, a comprehensive evaluation of feature representations in a DCF framework is performed. Experiments are performed on the benchmark OTB-2015 dataset, as well as the VOT 2014 dataset. The proposed methods are shown to significantly improve the performance of existing DCF based trackers.

  • 152.
    Häger, Gustav
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Bhat, Goutam
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Danelljan, Martin
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Khan, Fahad Shahbaz
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Rudol, Piotr
    Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, The Institute of Technology.
    Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV2016In: Proceedings of the 12th International Symposium on Advances in Visual Computing, 2016Conference paper (Refereed)
    Abstract [en]

    Visual object tracking performance has improved significantly in recent years. Most trackers are based on either of two paradigms: online learning of an appearance model or the use of a pre-trained object detector. Methods based on online learning provide high accuracy, but are prone to model drift. The model drift occurs when the tracker fails to correctly estimate the tracked object’s position. Methods based on a detector on the other hand typically have good long-term robustness, but reduced accuracy compared to online methods.

    Despite the complementarity of the aforementioned approaches, the problem of fusing them into a single framework is largely unexplored. In this paper, we propose a novel fusion between an online tracker and a pre-trained detector for tracking humans from a UAV. The system operates at real-time on a UAV platform. In addition we present a novel dataset for long-term tracking in a UAV setting, that includes scenarios that are typically not well represented in standard visual tracking datasets.

  • 153.
    Häger, Gustav
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Countering bias in tracking evaluations2018In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications / [ed] Francisco Imai, Alain Tremeau and Jose Braz, Science and Technology Publications, Lda , 2018, Vol. 5, p. 581-587Conference paper (Refereed)
    Abstract [en]

    Recent years have witnessed a significant leap in visual object tracking performance mainly due to powerfulfeatures, sophisticated learning methods and the introduction of benchmark datasets. Despite this significantimprovement, the evaluation of state-of-the-art object trackers still relies on the classical intersection overunion (IoU) score. In this work, we argue that the object tracking evaluations based on classical IoU score aresub-optimal. As our first contribution, we theoretically prove that the IoU score is biased in the case of largetarget objects and favors over-estimated target prediction sizes. As our second contribution, we propose a newscore that is unbiased with respect to target prediction size. We systematically evaluate our proposed approachon benchmark tracking data with variations in relative target size. Our empirical results clearly suggest thatthe proposed score is unbiased in general.

  • 154.
    Härd, Victoria
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Automatic Alignment of 2D Cine Morphological Images Using 4D Flow MRI Data2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Cardiovascular diseases are among the most common causes of death worldwide. One of the recently developed flow analysis technique called 4D flow magnetic resonance imaging (MRI) allows an early detection of such diseases. Due to the limited resolution and contrast between blood pool and myocardium of 4D flow images, cine MR images are often used for cardiac segmentation. The delineated structures are then transferred to the 4D Flow images for cardiovascular flow analysis. Cine MR images are however acquired with multiple breath-holds, which can be challenging for some people, especially, when a cardiovascular disease is present. Consequently, unexpected breathing motion by a patient may lead to misalignments between the acquired cine MR images.

    The goal of the thesis is to test the feasibility of an automatic image registration method to correct the misalignment caused by respiratory motion in morphological 2D cine MR images by using the 4D Flow MR as the reference image. As a registration method relies on a set of optimal parameters to provide desired results, a comprehensive investigation was performed to find such parameters. Different combinations of registration parameters settings were applied on 20 datasets from both healthy volunteers and patients. The best combinations, selected on the basis of normalized cross-correlation, were evaluated using the clinical gold-standard by employing widely used geometric measures of spatial correspondence. The accuracy of the best parameters from geometric evaluation was finally validated by using simulated misalignments.

    Using a registration method consisting of only translation improved the results for both datasets from healthy volunteers and patients and the simulated misalignment data. For the datasets from healthy volunteers and patients, the registration improved the results from 0.7074 ± 0.1644 to 0.7551 ± 0.0737 in Dice index and from 1.8818 ± 0.9269 to 1.5953 ± 0.5192 for point-to-curve error. These values are a mean value for all the 20 datasets.

    The results from geometric evaluation on the data from both healthy volunteers and patients show that the developed correction method is able to improve the alignment of the cine MR images. This allows a reliable segmentation of 4D flow MR images for cardiac flow assessment.

  • 155.
    Höglund, Kristofer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Non-destructive Testing Using Thermographic Image Processing2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In certain industries, quality testing is crucial, to make sure that the components being manufactured do not contain any defects. One method to detect these defects is to heat the specimen being inspected and then to study the cooling process using infrared thermography. The explorations of non-destructive testing using thermography is at an early stage and therefore the purpose of this thesis is to analyse some of the existing techniques and to propose improvements.

    A test specimen containing several different defects was designed specifically for this thesis. A flash lamp was used to heat the specimen and a high-speed infrared camera was used to study both the spatial and temporal features of the cooling process. An algorithm was implemented to detect anomalies and different parameter settings were evaluated. The results show that the proposed method is successful at finding the searched for defects, and also outperforms one of the old methods.

  • 156.
    Ilestrand, Maja
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Automatic Eartag Recognition on Dairy Cows in Real Barn Environment2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    All dairy cows in Europe wear unique identification tags in their ears. These eartags are standardized and contains the cows identification numbers, today only used for visual identification by the farmer. The cow also needs to be identified by an automatic identification system connected to milk machines and other robotics used at the farm. Currently this is solved with a non-standardized radio transmitter which can be placed on different places on the cow and different receivers needs to be used on different farms. Other drawbacks with the currently used identification system are that it is expensive and unreliable. This thesis explores the possibility to replace this non standardized radio frequency based identification system with a standardized computer vision based system. The method proposed in this thesis uses a color threshold approach for detection, a flood fill approach followed by Hough transform and a projection method for segmentation and evaluates template matching, k-nearest neighbour and support vector machines as optical character recognition methods. The result from the thesis shows that the quality of the data used as input to the system is vital. By using good data, k-nearest neighbour, which showed the best results of the three OCR approaches, handles 98 % of the digits.

  • 157.
    Ingberg, Benjamin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Registration of 2D Objects in 3D data2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In the field of industrial automation large savings can be realized if position andorientation of an object is known. Knowledge about an objects position and orien-tation can be used by advanced robotic systems to be able to work with complexitems. Specifically 2D-objects are a big enough sub domain to motivate specialattention. Traditionally this problem has been solved with large mechanical sys-tems that forces the objects into specific configurations. Besides being expensive,taking up a lot of space and having great difficulty handling fragile items, thesemechanical systems have to be constructed for each particular type of object. Thisthesis explores the possibility of using registration algorithms from computer vi-sion based on 3D-data to find flat objects. While systems for locating 3D objectsalready exists they have issues with locating essentially flat objects since theirpositioning is mostly a function of their contour. The thesis consists of a briefexamination of 2D-algorithms and their extension to 3D as well as results fromthe most suitable algorithm.

  • 158.
    Ivarsson, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Evaluation of 3D MRI Image Registration Methods2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Image registration is the process of geometrically deforming a template image into a reference image. This technique is important and widely used within thefield of medical IT. The purpose could be to detect image variations, pathologicaldevelopment or in the company AMRA’s case, to quantify fat tissue in variousparts of the human body.From an MRI (Magnetic Resonance Imaging) scan, a water and fat tissue image isobtained. Currently, AMRA is using the Morphon algorithm to register and segment the water image in order to quantify fat and muscle tissue. During the firstpart of this master thesis, two alternative registration methods were evaluated.The first algorithm was Free Form Deformation which is a non-linear parametricbased method. The second algorithm was a non-parametric optical flow basedmethod known as the Demon algorithm. During the second part of the thesis,the Demon algorithm was used to evaluate the effect of using the fat images forregistrations.

  • 159.
    Johansson, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Elfving, Tommy
    Linköping University, Department of Mathematics, Scientific Computing. Linköping University, The Institute of Technology.
    Kozlov, Vladimir
    Linköping University, Department of Mathematics, Applied Mathematics. Linköping University, The Institute of Technology.
    Censor, Yair
    Department of Mathematics, University of Haifa, Mt. Carmel, Haifa 31905, Israel.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    The Application of an Oblique-Projected Landweber Method to a Model of Supervised Learning2004Report (Other academic)
    Abstract [en]

    This report brings together a novel approach to some computer vision problems and a particular algorithmic development of the Landweber iterative algorithm. The algorithm solves a class of high-dimensional, sparse, and constrained least-squares problems, which arise in various computer vision learning tasks, such as object recognition and object pose estimation. The algorithm has recently been applied to these problems, but it has been used rather heuristically. In this report we describe the method and put it on firm mathematical ground. We consider a convexly constrained weighted least-squares problem and propose for its solution a projected Landweber method which employs oblique projections onto the closed convex constraint set. We formulate the problem, present the algorithm and work out its convergence properties, including a rate-of-convergence result. The results are put in perspective of currently available projected Landweber methods. The application to supervised learning is described, and the method is evaluated in a function approximation experiment.

  • 160.
    Johansson, Fredrik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Visual Stereo Odometry for Indoor Positioning2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this master thesis a visual odometry system is implemented and explained. Visual odometry is a technique, which could be used on autonomous vehicles to determine its current position and is preferably used indoors when GPS is notworking. The only input to the system are the images from a stereo camera and the output is the current location given in relative position.

    In the C++ implementation, image features are found and matched between the stereo images and the previous stereo pair, which gives a range of 150-250 verified feature matchings. The image coordinates are triangulated into a 3D-point cloud. The distance between two subsequent point clouds is minimized with respect to rigid transformations, which gives the motion described with six parameters, three for the translation and three for the rotation.

    Noise in the image coordinates gives reconstruction errors which makes the motion estimation very sensitive. The results from six experiments show that the weakness of the system is the ability to distinguish rotations from translations. However, if the system has additional knowledge of how it is moving, the minimization can be done with only three parameters and the system can estimate its position with less than 5 % error.

  • 161.
    Johansson, Victor
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    3D Position Estimation of a Person of Interest in Multiple Video Sequences: Person of Interest Recognition2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Because of the increase in the number of security cameras, there is more video footage available than a human could efficiently process. In combination with the fact that computers are getting more efficient, it is getting more and more interesting to solve the problem of detecting and recognizing people automatically.

    Therefore a method is proposed for estimating a 3D-path of a person of interest in multiple, non overlapping, monocular cameras. This project is a collaboration between two master theses. This thesis will focus on recognizing a person of interest from several possible candidates, as well as estimating the 3D-position of a person and providing a graphical user interface for the system. The recognition of the person of interest includes keeping track of said person frame by frame, and identifying said person in video sequences where the person of interest has not been seen before.

    The final product is able to both detect and recognize people in video, as well as estimating their 3D-position relative to the camera. The product is modular and any part can be improved or changed completely, without changing the rest of the product. This results in a highly versatile product which can be tailored for any given situation.

  • 162.
    Johnander, Joakim
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Visual Tracking with Deformable Continuous Convolution Operators2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Visual Object Tracking is the computer vision problem of estimating a target trajectory in a video given only its initial state. A visual tracker often acts as a component in the intelligent vision systems seen in for instance surveillance, autonomous vehicles or robots, and unmanned aerial vehicles. Applications may require robust tracking performance on difficult sequences depicting targets undergoing large changes in appearance, while enforcing a real-time constraint. Discriminative correlation filters have shown promising tracking performance in recent years, and consistently improved state-of-the-art. With the advent of deep learning, new robust deep features have improved tracking performance considerably. However, methods based on discriminative correlation filters learn a rigid template describing the target appearance. This implies an assumption of target rigidity which is not fulfilled in practice. This thesis introduces an approach which integrates deformability into a stateof-the-art tracker. The approach is thoroughly tested on three challenging visual tracking benchmarks, achieving state-of-the-art performance.

  • 163.
    Johnander, Joakim
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    On the Optimization of Advanced DCF-Trackers2018In: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part I / [ed] Laura Leal-TaixéStefan Roth, Cham: Springer Publishing Company, 2018, p. 54-69Conference paper (Refereed)
    Abstract [en]

    Trackers based on discriminative correlation filters (DCF) have recently seen widespread success and in this work we dive into their numerical core. DCF-based trackers interleave learning of the target detector and target state inference based on this detector. Whereas the original formulation includes a closed-form solution for the filter learning, recently introduced improvements to the framework no longer have known closed-form solutions. Instead a large-scale linear least squares problem must be solved each time the detector is updated. We analyze the procedure used to optimize the detector and let the popular scheme introduced with ECO serve as a baseline. The ECO implementation is revisited in detail and several mechanisms are provided with alternatives. With comprehensive experiments we show which configurations are superior in terms of tracking capabilities and optimization performance.

  • 164.
    Johnander, Joakim
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Brissman, Emil
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision.
    A generative appearance model for end-to-end video object segmentation2019In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019., 2019Conference paper (Refereed)
  • 165.
    Johnander, Joakim
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking2017In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10424, p. 55-67Conference paper (Refereed)
    Abstract [en]

    Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appearance model of the target. However, this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations. In this paper, we propose a unified formulation for learning a deformable convolution filter. In our framework, the deformable filter is represented as a linear combination of sub-filters. Both the sub-filter coefficients and their relative locations are inferred jointly in our formulation. Experiments are performed on three challenging tracking benchmarks: OTB-2015, TempleColor and VOT2016. Our approach improves the baseline method, leading to performance comparable to state-of-the-art.

  • 166.
    Jonsson, Mikael
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Make it Flat: Detection and Correction of Planar Regions in Triangle Meshes2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The art of reconstructing a real-world scene digitally has been on the mind of researchers for decades. Recently, it has attracted more and more attention from companies seeing a chance to bring this kind of technology to the market. Digital reconstruction of buildings in particular is a niche that has both potential and room for improvement. With this background, this thesis will present the design and evaluation of a pipeline made to find and correct approximately flat surfaces in architectural scenes. The scenes are 3D-reconstructed triangle meshes based on RGB images. The thesis will also comprise an evaluation of a few different components available for doing this, leading to a choice of best components. The goal is to improve the visual quality of the reconstruction.

    The final pipeline is designed with two blocks - one to detect initial plane seeds and one to refine the detected planes. The first block makes use of a multi-label energy formulation on the graph that describes the reconstructed surface. Penalties are assigned to each vertex and each edge of the graph based on the vertex labels, effectively describing a Markov Random Field. The energy is minimized with the help of the alpha-expansion algorithm. The second block uses heuristics for growing the detected plane seeds, merging similar planes together and extracting deviating details.

    Results on several scenes are presented, showing that the visual quality has been improved while maintaining accuracy compared with ground truth data.

  • 167.
    Josefsson, Mattias
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    3D camera with built-in velocity measurement2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In today's industry 3D cameras are often used to inspect products. The camera produces both a 3D model and an intensity image by capturing a series of profiles of the object using laser triangulation. In many of these setups a physical encoder is attached to, for example, the conveyor belt that the product is travelling on. The encoder is used to get an accurate reading of the speed that the product has when it passes through the laser. Without this, the output image from the camera can be distorted due to a variation in velocity.

    In this master thesis a method for integrating the functionality of this physical encoder into the software of the camera is proposed. The object is scanned together with a pattern, with the help of this pattern the object can be restored to its original proportions.

  • 168.
    Järemo Lawin, Felix
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Depth Data Processing and 3D Reconstruction Using the Kinect v22015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The Kinect v2 is a RGB-D sensor manufactured as a gesture interaction tool for the entertainment console XBOX One. In this thesis we will use it to perform 3D reconstruction and investigate its ability to measure depth. In order to sense both color and depth the Kinect v2 has two cameras: one RGB camera and one infrared camera used to produce depth and near infrared images. These cameras need to be calibrated if we want to use them for 3D reconstruction. We present a calibration procedure for simultaneously calibrating the cameras and extracting their relative pose. This enables us to construct colored meshes of the environment. When we know the camera parameters of the infrared camera, the depth images could be used to perform the Kinectfusion algorithm. This produces well-formed meshes of the environment by combining many depth frames taken from several camera poses.The Kinect v2 uses a time-of-flight technology were the phase shifts are extracted from amplitude modulated infrared light signals produced by an emitter. The extracted phase shifts are then converted to depth values. However, the extraction of phase shifts includes a phase unwrapping procedure, which is sensitive to noise and can result in large depth errors.By utilizing the ability to access the raw phase measurements from the device we managed to modify the phase unwrapping procedure. This new procedure includes an extraction of several hypotheses for the unwrapped phase and a spatial propagation to select amongst them. This proposed method has been compared with the available drivers in the open source library libfreenect2 and the Microsoft Kinect SDK v2. Our experiments show that the depth images of the two available drivers have similar quality and our proposed method improves over libfreenect2. The calculations in the proposed method are more expensive than those in libfreenect2 but it still runs at 2.5× real time. However, contrary to libfreenect2 the proposed method lacks a filter that removes outliers from the depth images. It turned out that this is an important feature when performing Kinect fusion and future work should thus be focused on adding an outlier filter.

  • 169.
    Järemo Lawin, Felix
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Density Adaptive Point Set Registration2018In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, p. 3829-3837Conference paper (Refereed)
    Abstract [en]

    Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the density of 3D points. This fundamental problem is primarily caused by changes in the sensor location across point sets.    We revisit the foundations of the probabilistic registration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimizing the Kullback-Leibler divergence in an Expectation Maximization based framework. Our density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real-world Lidar datasets. The results demonstrate that our approach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.

  • 170.
    Järemo-Lawin, Felix
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Tosteberg, Patrik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Deep Projective 3D Semantic Segmentation2017In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, p. 95-107Conference paper (Refereed)
    Abstract [en]

    Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets.

  • 171.
    Järemo-Lawin, Felix
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ovrén, Hannes
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Efficient Multi-frequency Phase Unwrapping Using Kernel Density Estimation2016In: Computer Vision – ECCV 2016 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV / [ed] Bastian Leibe, Jiri MatasNicu Sebe and Max Welling, Springer, 2016, p. 170-185Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce an efficient method to unwrap multi-frequency phase estimates for time-of-flight ranging. The algorithm generates multiple depth hypotheses and uses a spatial kernel density estimate (KDE) to rank them. The confidence produced by the KDE is also an effective means to detect outliers. We also introduce a new closed-form expression for phase noise prediction, that better fits real data. The method is applied to depth decoding for the Kinect v2 sensor, and compared to the Microsoft Kinect SDK and to the open source driver libfreenect2. The intended Kinect v2 use case is scenes with less than 8m range, and for such cases we observe consistent improvements, while maintaining real-time performance. When extending the depth range to the maximal value of 18.75 m, we get about 52% more valid measurements than libfreenect2. The effect is that the sensor can now be used in large depth scenes, where it was previously not a good choice.

  • 172.
    Järrendahl, Hannes
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Automatic Detection of Anatomical Landmarks in Three-Dimensional MRI2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Detection and positioning of anatomical landmarks, also called points of interest(POI), is often a concept of interest in medical image processing. Different measures or automatic image analyzes are often directly based upon positions of such points, e.g. in organ segmentation or tissue quantification. Manual positioning of these landmarks is a time consuming and resource demanding process. In this thesis, a general method for positioning of anatomical landmarks is outlined, implemented and evaluated. The evaluation of the method is limited to three different POI; left femur head, right femur head and vertebra T9. These POI are used to define the range of the abdomen in order to measure the amount of abdominal fat in 3D data acquired with quantitative magnetic resonance imaging (MRI). By getting more detailed information about the abdominal body fat composition, medical diagnoses can be issued with higher confidence. Examples of applications could be identifying patients with high risk of developing metabolic or catabolic disease and characterizing the effects of different interventions, i.e. training, bariatric surgery and medications. The proposed method is shown to be highly robust and accurate for positioning of left and right femur head. Due to insufficient performance regarding T9 detection, a modified method is proposed for T9 positioning. The modified method shows promises of accurate and repeatable results but has to be evaluated more extensively in order to draw further conclusions.

  • 173.
    Kardell, Martin
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Magnusson, Maria
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Faculty of Science & Engineering.
    Sandborg, Michael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Alm Carlsson, Gudrun
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Jeuthe, Julius
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Malusek, Alexandr
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    AUTOMATIC SEGMENTATION OF PELVIS FOR BRACHYTHERAPYOF PROSTATE2016In: Radiation Protection Dosimetry, ISSN 0144-8420, E-ISSN 1742-3406, Vol. 169, no 1-4, p. 398-404Article in journal (Refereed)
    Abstract [en]

    Advanced model-based iterative reconstruction algorithms in quantitative computed tomography (CT) perform automatic segmentation of tissues to estimate material properties of the imaged object. Compared with conventional methods, these algorithms may improve quality of reconstructed images and accuracy of radiation treatment planning. Automatic segmentation of tissues is, however, a difficult task. The aim of this work was to develop and evaluate an algorithm that automatically segments tissues in CT images of the male pelvis. The newly developed algorithm (MK2014) combines histogram matching, thresholding, region growing, deformable model and atlas-based registration techniques for the segmentation of bones, adipose tissue, prostate and muscles in CT images. Visual inspection of segmented images showed that the algorithm performed well for the five analysed images. The tissues were identified and outlined with accuracy sufficient for the dual-energy iterative reconstruction algorithm whose aim is to improve the accuracy of radiation treatment planning in brachytherapy of the prostate.

  • 174.
    Kargén, Rolf
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Utveckling av ett active vision system för demonstration av EDSDK++ i tillämpningar inom datorseende2014Independent thesis Basic level (degree of Bachelor), 10,5 credits / 16 HE creditsStudent thesis
    Abstract [en]

    Computer vision is a rapidly growing, interdisciplinary field whose applications are taking an increasingly prominent role in today's society. With an increased interest in computer vision there is also an increasing need to be able to control cameras connected to computer vision systems.

    At the division of computer vision, at Linköping University, the framework EDSDK++ has been developed to remotely control digital cameras made by Canon Inc. The framework is very comprehensive and contains a large amount of features and configuration options. The system is therefore largely still relatively untested. This thesis aims to develop a demonstrator to EDSDK++ in the form of a simple active vision system, which utilizes real-time face detection in order to control a camera tilt, and a camera mounted on the tilt, to follow, zoom in and focus on a face or a group of faces. A requirement was that the OpenCV library would be used for face detection and EDSDK++ would be used to control the camera. Moreover, an API to control the camera tilt was to be developed.

    During development, different methods for face detection were investigated. In order to improve performance, multiple, parallel face detectors using multithreading, were used to scan an image from different angles. Both experimental and theoretical approaches were made to determine the parameters needed to control the camera and camera tilt. The project resulted in a fully functional demonstrator, which fulfilled all requirements.

  • 175.
    Karlsson, Anette
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    In-Plane Motion Correction in Reconstruction of non-Cartesian 3D-functional MRI2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    When patients move during an MRI examination, severe artifacts arise in the reconstructed image and motion correction is therefore often desired. An in-plane motion correction algorithm suitable for PRESTO-CAN, a new 3D functional MRI method where sampling of k-space is radial in kx-direction and kz-direction and Cartesian in ky-direction, was implemented in this thesis work.

    Rotation and translation movements can be estimated and corrected for sepa- rately since the magnitude of the data is only affected by the rotation. The data were sampled in a radial pattern and the rotation was estimated by finding the translation in angular direction using circular correlation. Correlation was also used when finding the translation in x-direction and z-direction.

    The motion correction algorithm was evaluated on computer simulated data, the motion was detected and corrected for, and this resulted in images with greatly reduced artifacts due to patient movements. 

  • 176.
    Karlsson, Anette
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Magnusson, Maria
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics UHL. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Successful Motion Correction in Reconstruction of Radial MRI2011Conference paper (Refereed)
  • 177.
    Karlsson Schmidt, Carl
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Rhino and Human Detection in Overlapping RGB and LWIR Images2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The poaching of rhinoceros has increased dramatically the last few years andthe park rangers are often helpless against the militarised poachers. LinköpingUniversity is running several projects with the goal to aid the park rangers intheir work.This master thesis was produced at CybAero AB, which builds Remotely PilotedAircraft System (RPAS). With their helicopters, high end cameras with a rangesufficient to cover the whole area can be flown over the parks.The aim of this thesis is to investigate different methods to automatically findrhinos and humans, using airborne cameras. The system uses two cameras, onecolour camera and one thermal camera. The latter is used to find interestingobjects which are then extracted in the colour image. The object is then classifiedas either rhino, human or other. Several methods for classification have beenevaluated.The results show that classifying solely on the thermal image gives nearly as highaccuracy as classifying only in combination with the colour image. This enablesthe system to be used in dusk and dawn or in bad light conditions. This is animportant factor since most poaching occurs at dusk or dawn. As a conclusion asystem capable of running on low performance hardware and placeable on boardthe aircraft is presented.

  • 178.
    Kastberg, Maria
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Using Convolutional Neural Networks to Detect People Around Wells in South Sudan2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The organization International Aid Services (IAS) provides people in East Africawith clean water through well drilling. The wells are located in surroundingsfar away for the investors to inspect and therefore IAS wishes to be able to monitortheir wells to get a better overview if different types of improvements needto be made. To see the load on different water sources at different times of theday and during the year, and to know how many people that are visiting thewells, is of particular interest. In this paper, a method is proposed for countingpeople around the wells. The goal is to choose a suitable method for detectinghumans in images and evaluate how it performs. The area of counting humansin images is not a new topic, though it needs to be taken into account that thesituation implies some restrictions. A Raspberry Pi with an associated camerais used, which is a small embedded system that cannot handle large and complexsoftware. There is also a limited amount of data in the project. The methodproposed in this project uses a pre-trained convolutional neural network basedobject detector called the Single Shot Detector, which is adapted to suit smallerdevices and applications. The pre-trained network that it is based on is calledMobileNet, a network that is developed to be used on smaller systems. To see howgood the chosen detector performs it will be compared with some other models.Among them a detector based on the Inception network, a significantly larger networkthan the MobileNet. The base network is modified by transfer learning.Results shows that a fine-tuned and modified network can achieve better result,from a F1-score of 0.49 for a non-fine-tuned model to 0.66 for the fine-tuned one.

  • 179.
    Kernell, Björn
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Improving Photogrammetry using Semantic Segmentation2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    3D reconstruction is the process of constructing a three-dimensional model from images. It contains multiple steps where each step can induce errors. When doing 3D reconstruction of outdoor scenes, there are some types of scene content that regularly cause problems and affect the resulting 3D model. Two of these are water, due to its fluctuating nature, and sky because of it containing no useful (3D) data. These areas cause different problems throughout the process and do generally not benefit it in any way. Therefore, masking them early in the reconstruction chain could be a useful step in an outdoor scene reconstruction pipeline. Manual masking of images is a time-consuming and boring task and it gets very tedious for big data sets which are often used in large scale 3D reconstructions. This master thesis explores if this can be done automatically using Convolutional Neural Networks for semantic segmentation, and to what degree the masking would benefit a 3D reconstruction pipeline.

  • 180.
    Khan, Fahad Shahbaz
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Anwer, Rao Muhammad
    Universitat Autonoma de Barcelona, Spain.
    van de Weijer, Joost
    Universitat Autonoma de Barcelona, Spain.
    Bagdanov, Andrew D.
    Universitat Autonoma de Barcelona, Spain.
    Vanrell, Maria
    Universitat Autonoma de Barcelona, Spain.
    Lopez, Antonio M.
    Universitat Autonoma de Barcelona, Spain.
    Color Attributes for Object Detection2012In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012, IEEE , 2012, p. 3306-3313Conference paper (Refereed)
    Abstract [en]

    State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-of-the-art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.

  • 181.
    Khan, Fahad Shahbaz
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Beigpour, Shida
    Norwegian Colour and Visual Computing Laboratory, Gjovik University College, Gjøvik, Norway.
    van de Weijer, Joost
    Computer Vision Center, CS Dept. Universitat Autonoma de Barcelona, Spain.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Painting-91: a large scale database for computational painting categorization2014In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 25, no 6, p. 1385-1397Article in journal (Refereed)
    Abstract [en]

    Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.

  • 182.
    Khan, Fahad Shahbaz
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Muhammad Anwer, Rao
    Department of Information and Computer Science, Aalto University School of Science, Finland.
    van de Weijer, Joost
    Computer Vision Center, CS Dept. Universitat Autonoma de Barcelona, Spain.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Laaksonen, Jorma
    Department of Information and Computer Science, Aalto University School of Science, Finland.
    Compact color–texture description for texture classification2015In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 51, p. 16-22Article in journal (Refereed)
    Abstract [en]

    Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature. However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7.8%,4.3%7.8%,4.3% and 5.0%5.0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively.

  • 183.
    Khan, Fahad Shahbaz
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Rao, Muhammad Anwer
    Computer vision Center Barcelona, Universitat Autonoma de Barcelona, Spain.
    van de Weijer, Joost
    Computer vision Center Barcelona, Universitat Autonoma de Barcelona, Spain.
    Bagdanov, Andrew
    Media Integration and Communication Center, University of Florence, Florence, Italy.
    Lopez, Antonio
    Computer vision Center Barcelona, Universitat Autonoma de Barcelona, Spain.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Coloring Action Recognition in Still Images2013In: International Journal of Computer Vision, ISSN 0920-5691, E-ISSN 1573-1405, Vol. 105, no 3, p. 205-221Article in journal (Refereed)
    Abstract [en]

    In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.

  • 184.
    Khan, Fahad Shahbaz
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Rao, Muhammad Anwer
    Department of Information and Computer Science, Aalto University School of Science, Aalto, Finland.
    van de Weijer, Joost
    Computer Vision Center, CS Department, Universitet Autonoma de Barcelona, Barcelona, Spain.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Laaksonen, Jorma
    Department of Information and Computer Science, Aalto University School of Science, Aalto, Finland.
    Deep Semantic Pyramids for Human Attributes and Action Recognition2015In: Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings / [ed] Paulsen, Rasmus R., Pedersen, Kim S., Springer, 2015, Vol. 9127, p. 341-353Conference paper (Refereed)
    Abstract [en]

    Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.

    We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature.

  • 185.
    Khan, Fahad Shahbaz
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Van de Weijer, Joost
    Universitat Autonoma de Barcelona, Spain .
    Ali, Sadiq
    Universitat Autonoma de Barcelona, Spain .
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Evaluating the Impact of Color on Texture Recognition2013In: Computer Analysis of Images and Patterns: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part I / [ed] Richard Wilson, Edwin Hancock, Adrian Bors, William Smith, Springer Berlin/Heidelberg, 2013, p. 154-162Conference paper (Refereed)
    Abstract [en]

    State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.

    In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets.

  • 186.
    Khan, Fahad
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Van, De Weijer J.
    Computer Vision Center, CS Department, Universitat Autonoma de Barcelona, Spain.
    Bagdanov, A.D.
    Computer Vision Center, CS Department, Universitat Autonoma de Barcelona, Spain.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Scale coding bag-of-words for action recognition2014In: Pattern Recognition (ICPR), 2014 22nd International Conference on, Institute of Electrical and Electronics Engineers Inc. , 2014, no 6976979, p. 1514-1519Conference paper (Refereed)
    Abstract [en]

    Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image. Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant strategy is sub-optimal since it ignores the multi-scale information available with each bounding box of a person. This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music, riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods.

  • 187.
    Khan, Fahad
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    van de Weijer, Joost
    University of Autonoma Barcelona, Spain.
    Muhammad Anwer, Rao
    Aalto University, Finland.
    Bagdanov, Andrew D.
    University of Florence, Italy.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Laaksonen, Jorma
    Aalto University, Finland.
    Scale coding bag of deep features for human attribute and action recognition2018In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 29, no 1, p. 55-71Article in journal (Refereed)
    Abstract [en]

    Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.

  • 188.
    Khan, Fahad
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    van de Weijer, Joost
    Comp Vis Centre, Spain .
    Muhammad Anwer, Rao
    Aalto University, Finland .
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Gatta, Carlo
    Comp Vis Centre, Spain .
    Semantic Pyramids for Gender and Action Recognition2014In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 23, no 8, p. 3633-3645Article in journal (Refereed)
    Abstract [en]

    Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition.

  • 189.
    Khan, Fahad
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Xu, Jiaolong
    Comp Vis Centre Barcelona, Spain.
    van de Weijer, Joost
    Comp Vis Centre Barcelona, Spain.
    Bagdanov, Andrew D.
    Comp Vis Centre Barcelona, Spain.
    Muhammad Anwer, Rao
    Aalto University, Finland.
    Lopez, Antonio M.
    Comp Vis Centre Barcelona, Spain.
    Recognizing Actions Through Action-Specific Person Detection2015In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 24, no 11, p. 4422-4432Article in journal (Refereed)
    Abstract [en]

    Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage. Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1) the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2) due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3) using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images. We perform extensive experiments on two benchmark data sets: 1) Stanford-40 and 2) PASCAL VOC 2012. For the action detection task (i.e., both person localization and classification of the action performed), our approach outperforms methods based on general person detection by 5.7% mean average precision (MAP) on Stanford-40 and 2.1% MAP on PASCAL VOC 2012. Our approach also significantly outperforms the state of the art with a MAP of 45.4% on Stanford-40 and 31.4% on PASCAL VOC 2012. We also evaluate our action detection approach for the task of action classification (i.e., recognizing actions without localizing them). For this task, our approach, without using any ground-truth person localization at test time, outperforms on both data sets state-of-the-art methods, which do use person locations.

  • 190.
    Khan, Rahat
    et al.
    Université de Saint- Étienne, France.
    Van de Weijer, Joost
    Computer Vision Center, Barcelona, Spain.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Muselet, Damien
    Université de Saint- Étienne, France.
    Ducottet, Christophe
    Université de Saint- Étienne, France.
    Barat, Cecile
    Université de Saint- Étienne, France.
    Discriminative Color Descriptors2013In: Computer Vision and Pattern Recognition (CVPR), 2013, IEEE Computer Society, 2013, p. 2866-2873Conference paper (Refereed)
    Abstract [en]

    Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200.

  • 191.
    Kjellén, Kevin
    Linköping University, Department of Electrical Engineering, Computer Vision.
    Point Cloud Registration in Augmented Reality using the Microsoft HoloLens2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    When a Time-of-Flight (ToF) depth camera is used to monitor a region of interest, it has to be mounted correctly and have information regarding its position. Manual configuration currently require managing captured 3D ToF data in a 2D environment, which limits the user and might give rise to errors due to misinterpretation of the data. This thesis investigates if a real time 3D reconstruction mesh from a Microsoft HoloLens can be used as a target for point cloud registration using the ToF data, thus configuring the camera autonomously. Three registration algorithms, Fast Global Registration (FGR), Joint Registration Multiple Point Clouds (JR-MPC) and Prerejective RANSAC, were evaluated for this purpose.

    It was concluded that despite using different sensors it is possible to perform accurate registration. Also, it was shown that the registration can be done accurately within a reasonable time, compared with the inherent time to perform 3D reconstruction on the Hololens. All algorithms could solve the problem, but it was concluded that FGR provided the most satisfying results, though requiring several constraints on the data.

  • 192.
    Koschorrek, Philipp
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Piccini, Tommaso
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Öberg, Per
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Nielsen, Lars
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Mester, Rudolf
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. University of Frankfurt, Germany.
    A multi-sensor traffic scene dataset with omnidirectional video2013In: 2013 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), IEEE conference proceedings, 2013, p. 727-734Conference paper (Refereed)
    Abstract [en]

    The development of vehicles that perceive their environment, in particular those using computer vision, indispensably requires large databases of sensor recordings obtained from real cars driven in realistic traffic situations. These datasets should be time shaped for enabling synchronization of sensor data from different sources. Furthermore, full surround environment perception requires high frame rates of synchronized omnidirectional video data to prevent information loss at any speeds.

    This paper describes an experimental setup and software environment for recording such synchronized multi-sensor data streams and storing them in a new open source format. The dataset consists of sequences recorded in various environments from a car equipped with an omnidirectional multi-camera, height sensors, an IMU, a velocity sensor, and a GPS. The software environment for reading these data sets will be provided to the public, together with a collection of long multi-sensor and multi-camera data streams stored in the developed format.

  • 193.
    Krebs, Andreas
    et al.
    Dept. Aerodynamics/Fluid Mech., BTU Cottbus, Germany.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Optimization of Quadrature Filters Based on the Numerical Integration of Improper Integrals2011In: Pattern Recognition: 33rd annual DAGM conference, Frankfurt, Germany / [ed] Rudolf Mester and Michael Felsberg, Springer Berlin/Heidelberg, 2011, Vol. 6835, p. 91-100Conference paper (Refereed)
    Abstract [en]

    Convolution kernels are a commonly used tool in computer vision. These kernels are often specified by an ideal frequency response and the actual filter coefficients are obtained by minimizing some weighted distance with respect to the ideal filter. State-of-the-art approaches usually replace the continuous frequency response by a discrete Fourier spectrum with a multitude of samples compared to the kernel size, depending on the smoothness of the ideal filter and the weight function. The number of samples in the Fourier domain grows exponentially with the dimensionality and becomes a bottleneck concerning memory requirements.

    In this paper we propose a method that avoids the discretization of the frequency space and makes filter optimization feasible in higher dimensions than the standard approach. The result is no longer depending on the choice of the sampling grid and it remains exact even if the weighting function is singular in the origin. The resulting improper integrals are efficiently computed using Gauss-Jacobi quadrature.

  • 194.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Leonardis, Ales
    University of Birmingham, England.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Pflugfelder, Roman
    Austrian Institute Technology, Austria.
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Vojir, Tomas
    Czech Technical University, Czech Republic.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Lukezic, Alan
    University of Ljubljana, Slovenia.
    Fernandez, Gustavo
    Austrian Institute Technology, Austria.
    Gupta, Abhinav
    Carnegie Mellon University, PA 15213 USA.
    Petrosino, Alfredo
    Parthenope University of Naples, Italy.
    Memarmoghadam, Alireza
    University of Isfahan, Iran.
    Garcia-Martin, Alvaro
    University of Autonoma Madrid, Spain.
    Solis Montero, Andres
    University of Ottawa, Canada.
    Vedaldi, Andrea
    University of Oxford, England.
    Robinson, Andreas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ma, Andy J.
    Hong Kong Baptist University, Peoples R China.
    Varfolomieiev, Anton
    Kyiv Polytech Institute, Ukraine.
    Alatan, Aydin
    Middle East Technical University, Çankaya, Turkey.
    Erdem, Aykut
    Hacettepe University, Turkey.
    Ghanem, Bernard
    KAUST, Saudi Arabia.
    Liu, Bin
    Moshanghua Technology Co, Peoples R China.
    Han, Bohyung
    POSTECH, South Korea.
    Martinez, Brais
    University of Nottingham, England.
    Chang, Chang-Ming
    University of Albany, GA USA.
    Xu, Changsheng
    Chinese Academic Science, Peoples R China.
    Sun, Chong
    Dalian University of Technology, Peoples R China.
    Kim, Daijin
    POSTECH, South Korea.
    Chen, Dapeng
    Xi An Jiao Tong University, Peoples R China.
    Du, Dawei
    University of Chinese Academic Science, Peoples R China.
    Mishra, Deepak
    Indian Institute Space Science and Technology, India.
    Yeung, Dit-Yan
    Hong Kong University of Science and Technology, Peoples R China.
    Gundogdu, Erhan
    Aselsan Research Centre, Turkey.
    Erdem, Erkut
    Hacettepe University, Turkey.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Porikli, Fatih
    ARC Centre Excellence Robot Vis, Australia; Australian National University, Australia; CSIRO, Australia.
    Zhao, Fei
    Chinese Academic Science, Peoples R China.
    Bunyak, Filiz
    University of Missouri, MO 65211 USA.
    Battistone, Francesco
    Parthenope University of Naples, Italy.
    Zhu, Gao
    Australian National University, Australia.
    Roffo, Giorgio
    University of Verona, Italy.
    Sai Subrahmanyam, Gorthi R. K.
    Indian Institute Space Science and Technology, India.
    Bastos, Guilherme
    University of Federal Itajuba, Brazil.
    Seetharaman, Guna
    US Navy, DC 20375 USA.
    Medeiros, Henry
    Marquette University, WI 53233 USA.
    Li, Hongdong
    ARC Centre Excellence Robot Vis, Australia; Australian National University, Australia.
    Qi, Honggang
    University of Chinese Academic Science, Peoples R China.
    Bischof, Horst
    Graz University of Technology, Austria.
    Possegger, Horst
    Graz University of Technology, Austria.
    Lu, Huchuan
    Dalian University of Technology, Peoples R China.
    Lee, Hyemin
    POSTECH, South Korea.
    Nam, Hyeonseob
    NAVER Corp, South Korea.
    Jin Chang, Hyung
    Imperial Coll London, England.
    Drummond, Isabela
    University of Federal Itajuba, Brazil.
    Valmadre, Jack
    University of Oxford, England.
    Jeong, Jae-chan
    ASRI, South Korea; Elect and Telecommun Research Institute, South Korea.
    Cho, Jae-il
    Elect and Telecommun Research Institute, South Korea.
    Lee, Jae-Yeong
    Elect and Telecommun Research Institute, South Korea.
    Zhu, Jianke
    Zhejiang University, Peoples R China.
    Feng, Jiayi
    Chinese Academic Science, Peoples R China.
    Gao, Jin
    Chinese Academic Science, Peoples R China.
    Young Choi, Jin
    ASRI, South Korea.
    Xiao, Jingjing
    University of Birmingham, England.
    Kim, Ji-Wan
    Elect and Telecommun Research Institute, South Korea.
    Jeong, Jiyeoup
    ASRI, South Korea; Elect and Telecommun Research Institute, South Korea.
    Henriques, Joao F.
    University of Oxford, England.
    Lang, Jochen
    University of Ottawa, Canada.
    Choi, Jongwon
    ASRI, South Korea.
    Martinez, Jose M.
    University of Autonoma Madrid, Spain.
    Xing, Junliang
    Chinese Academic Science, Peoples R China.
    Gao, Junyu
    Chinese Academic Science, Peoples R China.
    Palaniappan, Kannappan
    University of Missouri, MO 65211 USA.
    Lebeda, Karel
    University of Surrey, England.
    Gao, Ke
    University of Missouri, MO 65211 USA.
    Mikolajczyk, Krystian
    Imperial Coll London, England.
    Qin, Lei
    Chinese Academic Science, Peoples R China.
    Wang, Lijun
    Dalian University of Technology, Peoples R China.
    Wen, Longyin
    University of Albany, GA USA.
    Bertinetto, Luca
    University of Oxford, England.
    Kumar Rapuru, Madan
    Indian Institute Space Science and Technology, India.
    Poostchi, Mahdieh
    University of Missouri, MO 65211 USA.
    Maresca, Mario
    Parthenope University of Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Mueller, Matthias
    KAUST, Saudi Arabia.
    Zhang, Mengdan
    Chinese Academic Science, Peoples R China.
    Arens, Michael
    Fraunhofer IOSB, Germany.
    Valstar, Michel
    University of Nottingham, England.
    Tang, Ming
    Chinese Academic Science, Peoples R China.
    Baek, Mooyeol
    POSTECH, South Korea.
    Haris Khan, Muhammad
    University of Nottingham, England.
    Wang, Naiyan
    Hong Kong University of Science and Technology, Peoples R China.
    Fan, Nana
    Harbin Institute Technology, Peoples R China.
    Al-Shakarji, Noor
    University of Missouri, MO 65211 USA.
    Miksik, Ondrej
    University of Oxford, England.
    Akin, Osman
    Hacettepe University, Turkey.
    Moallem, Payman
    University of Isfahan, Iran.
    Senna, Pedro
    University of Federal Itajuba, Brazil.
    Torr, Philip H. S.
    University of Oxford, England.
    Yuen, Pong C.
    Hong Kong Baptist University, Peoples R China.
    Huang, Qingming
    Harbin Institute Technology, Peoples R China; University of Chinese Academic Science, Peoples R China.
    Martin-Nieto, Rafael
    University of Autonoma Madrid, Spain.
    Pelapur, Rengarajan
    University of Missouri, MO 65211 USA.
    Bowden, Richard
    University of Surrey, England.
    Laganiere, Robert
    University of Ottawa, Canada.
    Stolkin, Rustam
    University of Birmingham, England.
    Walsh, Ryan
    Marquette University, WI 53233 USA.
    Krah, Sebastian B.
    Fraunhofer IOSB, Germany.
    Li, Shengkun
    Hong Kong University of Science and Technology, Peoples R China; University of Albany, GA USA.
    Zhang, Shengping
    Harbin Institute Technology, Peoples R China.
    Yao, Shizeng
    University of Missouri, MO 65211 USA.
    Hadfield, Simon
    University of Surrey, England.
    Melzi, Simone
    University of Verona, Italy.
    Lyu, Siwei
    University of Albany, GA USA.
    Li, Siyi
    Hong Kong University of Science and Technology, Peoples R China; University of Albany, GA USA.
    Becker, Stefan
    Fraunhofer IOSB, Germany.
    Golodetz, Stuart
    University of Oxford, England.
    Kakanuru, Sumithra
    Indian Institute Space Science and Technology, India.
    Choi, Sunglok
    Elect and Telecommun Research Institute, South Korea.
    Hu, Tao
    University of Chinese Academic Science, Peoples R China.
    Mauthner, Thomas
    Graz University of Technology, Austria.
    Zhang, Tianzhu
    Chinese Academic Science, Peoples R China.
    Pridmore, Tony
    University of Nottingham, England.
    Santopietro, Vincenzo
    Parthenope University of Naples, Italy.
    Hu, Weiming
    Chinese Academic Science, Peoples R China.
    Li, Wenbo
    Lehigh University, PA 18015 USA.
    Huebner, Wolfgang
    Fraunhofer IOSB, Germany.
    Lan, Xiangyuan
    Hong Kong Baptist University, Peoples R China.
    Wang, Xiaomeng
    University of Nottingham, England.
    Li, Xin
    Harbin Institute Technology, Peoples R China.
    Li, Yang
    Zhejiang University, Peoples R China.
    Demiris, Yiannis
    Imperial Coll London, England.
    Wang, Yifan
    Dalian University of Technology, Peoples R China.
    Qi, Yuankai
    Harbin Institute Technology, Peoples R China.
    Yuan, Zejian
    Xi An Jiao Tong University, Peoples R China.
    Cai, Zexiong
    Hong Kong Baptist University, Peoples R China.
    Xu, Zhan
    Zhejiang University, Peoples R China.
    He, Zhenyu
    Harbin Institute Technology, Peoples R China.
    Chi, Zhizhen
    Dalian University of Technology, Peoples R China.
    The Visual Object Tracking VOT2016 Challenge Results2016In: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II, SPRINGER INT PUBLISHING AG , 2016, Vol. 9914, p. 777-823Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment.

  • 195.
    Kristan, Matej
    et al.
    Univ Ljubljana, Slovenia.
    Leonardis, Ales
    Univ Birmingham, England.
    Matas, Jiri
    Czech Tech Univ, Czech Republic.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Pflugfelder, Roman
    Austrian Inst Technol, Austria.
    Zajc, Luka Cehovin
    Univ Ljubljana, Slovenia.
    Vojir, Tomas
    Czech Tech Univ, Czech Republic.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Lukezic, Alan
    Univ Ljubljana, Slovenia.
    Eldesokey, Abdelrahman
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Fernandez, Gustavo
    Austrian Inst Technol, Austria.
    Garcia-Martin, Alvaro
    Univ Autonoma Madrid, Spain.
    Muhic, A.
    Univ Ljubljana, Slovenia.
    Petrosino, Alfredo
    Univ Parthenope Naples, Italy.
    Memarmoghadam, Alireza
    Univ Isfahan, Iran.
    Vedaldi, Andrea
    Univ Oxford, England.
    Manzanera, Antoine
    Univ Paris Saclay, France.
    Tran, Antoine
    Univ Paris Saclay, France.
    Alatan, Aydin
    Middle East Tech Univ, Turkey.
    Mocanu, Bogdan
    Univ Politehn Bucuresti, Romania.
    Chen, Boyu
    Dalian Univ Technol, Peoples R China.
    Huang, Chang
    Horizon Robot Inc, Peoples R China.
    Xu, Changsheng
    Chinese Acad Sci, Peoples R China.
    Sun, Chong
    Dalian Univ Technol, Peoples R China.
    Du, Dalong
    Horizon Robot Inc, Peoples R China; Univ Chinese Acad Sci, Peoples R China.
    Zhang, David
    Hong Kong Polytech Univ, Peoples R China.
    Du, Dawei
    Horizon Robot Inc, Peoples R China; Univ Chinese Acad Sci, Peoples R China.
    Mishra, Deepak
    Indian Inst Space Sci and Technol Trivandrum, India.
    Gundogdu, Erhan
    Aselsan Res Ctr, Turkey; Middle East Tech Univ, Turkey.
    Velasco-Salido, Erik
    Univ Autonoma Madrid, Spain.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Battistone, Francesco
    Univ Parthenope Naples, Italy.
    Subrahmanyam, Gorthi R. K. Sai
    Indian Inst Space Sci and Technol Trivandrum, India.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Huang, Guan
    Horizon Robot Inc, Peoples R China.
    Bastos, Guilherme
    Univ Fed Itajuba, Brazil.
    Seetharaman, Guna
    Naval Res Lab, DC 20375 USA.
    Zhang, Hongliang
    Natl Univ Def Technol, Peoples R China.
    Li, Houqiang
    Univ Sci and Technol China, Peoples R China.
    Lu, Huchuan
    Dalian Univ Technol, Peoples R China.
    Drummond, Isabela
    Univ Fed Itajuba, Brazil.
    Valmadre, Jack
    Univ Oxford, England.
    Jeong, Jae-Chan
    ETRI, South Korea.
    Cho, Jae-Il
    ETRI, South Korea.
    Lee, Jae-Yeong
    ETRI, South Korea.
    Noskova, Jana
    Czech Tech Univ, Czech Republic.
    Zhu, Jianke
    Zhejiang Univ, Peoples R China.
    Gao, Jin
    Chinese Acad Sci, Peoples R China.
    Liu, Jingyu
    Chinese Acad Sci, Peoples R China.
    Kim, Ji-Wan
    ETRI, South Korea.
    Henriques, Joao F.
    Univ Oxford, England.
    Martinez, Jose M.
    Univ Autonoma Madrid, Spain.
    Zhuang, Junfei
    Beijing Univ Posts and Telecommun, Peoples R China.
    Xing, Junliang
    Chinese Acad Sci, Peoples R China.
    Gao, Junyu
    Chinese Acad Sci, Peoples R China.
    Chen, Kai
    Huazhong Univ Sci and Technol, Peoples R China.
    Palaniappan, Kannappan
    Univ Missouri Columbia, MO USA.
    Lebeda, Karel
    The Foundry, England.
    Gao, Ke
    Univ Missouri Columbia, MO USA.
    Kitani, Kris M.
    Carnegie Mellon Univ, PA 15213 USA.
    Zhang, Lei
    Hong Kong Polytech Univ, Peoples R China.
    Wang, Lijun
    Dalian Univ Technol, Peoples R China.
    Yang, Lingxiao
    Hong Kong Polytech Univ, Peoples R China.
    Wen, Longyin
    GE Global Res, NY USA.
    Bertinetto, Luca
    Univ Oxford, England.
    Poostchi, Mahdieh
    Univ Missouri Columbia, MO USA.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Mueller, Matthias
    KAUST, Saudi Arabia.
    Zhang, Mengdan
    Chinese Acad Sci, Peoples R China.
    Yang, Ming-Hsuan
    Univ Calif Merced, CA USA.
    Xie, Nianhao
    Natl Univ Def Technol, Peoples R China.
    Wang, Ning
    Univ Sci and Technol China, Peoples R China.
    Miksik, Ondrej
    Univ Oxford, England.
    Moallem, P.
    Univ Isfahan, Iran.
    Venugopal, Pallavi M.
    Indian Inst Space Sci and Technol Trivandrum, India.
    Senna, Pedro
    Univ Fed Itajuba, Brazil.
    Torr, Philip H. S.
    Univ Oxford, England.
    Wang, Qiang
    Chinese Acad Sci, Peoples R China.
    Yu, Qifeng
    Natl Univ Def Technol, Peoples R China.
    Huang, Qingming
    Univ Chinese Acad Sci, Peoples R China.
    Martin-Nieto, Rafael
    Univ Autonoma Madrid, Spain.
    Bowden, Richard
    Univ Surrey, England.
    Liu, Risheng
    Dalian Univ Technol, Peoples R China.
    Tapu, Ruxandra
    Univ Politehn Bucuresti, Romania.
    Hadfield, Simon
    Univ Surrey, England.
    Lyu, Siwei
    SUNY Albany, NY 12222 USA.
    Golodetz, Stuart
    Univ Oxford, England.
    Choi, Sunglok
    ETRI, South Korea.
    Zhang, Tianzhu
    Chinese Acad Sci, Peoples R China.
    Zaharia, Titus
    Inst. Mines-Telecom/ TelecomSudParis, France.
    Santopietro, Vincenzo
    Univ Parthenope Naples, Italy.
    Zou, Wei
    Chinese Acad Sci, Peoples R China.
    Hu, Weiming
    Chinese Acad Sci, Peoples R China.
    Tao, Wenbing
    Huazhong Univ Sci and Technol, Peoples R China.
    Li, Wenbo
    SUNY Albany, NY 12222 USA.
    Zhou, Wengang
    Univ Sci and Technol China, Peoples R China.
    Yu, Xianguo
    Natl Univ Def Technol, Peoples R China.
    Bian, Xiao
    GE Global Res, NY USA.
    Li, Yang
    Zhejiang Univ, Peoples R China.
    Xing, Yifan
    Carnegie Mellon Univ, PA 15213 USA.
    Fan, Yingruo
    Beijing Univ Posts and Telecommun, Peoples R China.
    Zhu, Zheng
    Chinese Acad Sci, Peoples R China; Univ Chinese Acad Sci, Peoples R China.
    Zhang, Zhipeng
    Chinese Acad Sci, Peoples R China.
    He, Zhiqun
    Beijing Univ Posts and Telecommun, Peoples R China.
    The Visual Object Tracking VOT2017 challenge results2017In: 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017), IEEE , 2017, p. 1949-1972Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).

  • 196.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Leonardis, Aleš
    University of Birmingham, United Kingdom.
    Matas, Jirí
    Czech Technical University, Czech Republic.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Pflugfelder, Roman
    Austrian Institute of Technology, Austria / TU Wien, Austria.
    Zajc, Luka Cehovin
    University of Ljubljana, Slovenia.
    Vojírì, Tomáš
    Czech Technical University, Czech Republic.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Lukezič, Alan
    University of Ljubljana, Slovenia.
    Eldesokey, Abdelrahman
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Fernández, Gustavo
    García-Martín, Álvaro
    Iglesias-Arias, Álvaro
    Alatan, A. Aydin
    González-García, Abel
    Petrosino, Alfredo
    Memarmoghadam, Alireza
    Vedaldi, Andrea
    Muhič, Andrej
    He, Anfeng
    Smeulders, Arnold
    Perera, Asanka G.
    Li, Bo
    Chen, Boyu
    Kim, Changick
    Xu, Changsheng
    Xiong, Changzhen
    Tian, Cheng
    Luo, Chong
    Sun, Chong
    Hao, Cong
    Kim, Daijin
    Mishra, Deepak
    Chen, Deming
    Wang, Dong
    Wee, Dongyoon
    Gavves, Efstratios
    Gundogdu, Erhan
    Velasco-Salido, Erik
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yang, Fan
    Zhao, Fei
    Li, Feng
    Battistone, Francesco
    De Ath, George
    Subrahmanyam, Gorthi R. K. S.
    Bastos, Guilherme
    Ling, Haibin
    Galoogahi, Hamed Kiani
    Lee, Hankyeol
    Li, Haojie
    Zhao, Haojie
    Fan, Heng
    Zhang, Honggang
    Possegger, Horst
    Li, Houqiang
    Lu, Huchuan
    Zhi, Hui
    Li, Huiyun
    Lee, Hyemin
    Chang, Hyung Jin
    Drummond, Isabela
    Valmadre, Jack
    Martin, Jaime Spencer
    Chahl, Javaan
    Choi, Jin Young
    Li, Jing
    Wang, Jinqiao
    Qi, Jinqing
    Sung, Jinyoung
    Johnander, Joakim
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Henriques, Joao
    Choi, Jongwon
    van de Weijer, Joost
    Herranz, Jorge Rodríguez
    Martínez, José M.
    Kittler, Josef
    Zhuang, Junfei
    Gao, Junyu
    Grm, Klemen
    Zhang, Lichao
    Wang, Lijun
    Yang, Lingxiao
    Rout, Litu
    Si, Liu
    Bertinetto, Luca
    Chu, Lutao
    Che, Manqiang
    Maresca, Mario Edoardo
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yang, Ming-Hsuan
    Abdelpakey, Mohamed
    Shehata, Mohamed
    Kang, Myunggu
    Lee, Namhoon
    Wang, Ning
    Miksik, Ondrej
    Moallem, P.
    Vicente-Moñivar, Pablo
    Senna, Pedro
    Li, Peixia
    Torr, Philip
    Raju, Priya Mariam
    Ruihe, Qian
    Wang, Qiang
    Zhou, Qin
    Guo, Qing
    Martín-Nieto, Rafael
    Gorthi, Rama Krishna
    Tao, Ran
    Bowden, Richard
    Everson, Richard
    Wang, Runling
    Yun, Sangdoo
    Choi, Seokeon
    Vivas, Sergio
    Bai, Shuai
    Huang, Shuangping
    Wu, Sihang
    Hadfield, Simon
    Wang, Siwen
    Golodetz, Stuart
    Ming, Tang
    Xu, Tianyang
    Zhang, Tianzhu
    Fischer, Tobias
    Santopietro, Vincenzo
    Štruc, Vitomir
    Wei, Wang
    Zuo, Wangmeng
    Feng, Wei
    Wu, Wei
    Zou, Wei
    Hu, Weiming
    Zhou, Wengang
    Zeng, Wenjun
    Zhang, Xiaofan
    Wu, Xiaohe
    Wu, Xiao-Jun
    Tian, Xinmei
    Li, Yan
    Lu, Yan
    Law, Yee Wei
    Wu, Yi
    Demiris, Yiannis
    Yang, Yicai
    Jiao, Yifan
    Li, Yuhong
    Zhang, Yunhua
    Sun, Yuxuan
    Zhang, Zheng
    Zhu, Zheng
    Feng, Zhen-Hua
    Wang, Zhihui
    He, Zhiqun
    The Sixth Visual Object Tracking VOT2018 Challenge Results2019In: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8–14, 2018 Proceedings, Part I / [ed] Laura Leal-Taixé and Stefan Roth, Cham: Springer Publishing Company, 2019, p. 3-53Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

  • 197.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Leonardis, Ales
    University of Birmingham, England.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Fernandez, Gustavo
    Austrian Institute Technology, Austria.
    Vojir, Tomas
    Czech Technical University, Czech Republic.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Nebehay, Georg
    Austrian Institute Technology, Austria.
    Pflugfelder, Roman
    Austrian Institute Technology, Austria.
    Gupta, Abhinav
    Carnegie Mellon University, PA 15213 USA.
    Bibi, Adel
    King Abdullah University of Science and Technology, Saudi Arabia.
    Lukezic, Alan
    University of Ljubljana, Slovenia.
    Garcia-Martins, Alvaro
    University of Autonoma Madrid, Spain.
    Saffari, Amir
    Affectv, England.
    Petrosino, Alfredo
    Parthenope University of Naples, Italy.
    Solis Montero, Andres
    University of Ottawa, Canada.
    Varfolomieiev, Anton
    National Technical University of Ukraine, Ukraine.
    Baskurt, Atilla
    University of Lyon, France.
    Zhao, Baojun
    Beijing Institute Technology, Peoples R China.
    Ghanem, Bernard
    King Abdullah University of Science and Technology, Saudi Arabia.
    Martinez, Brais
    University of Nottingham, England.
    Lee, ByeongJu
    Seoul National University, South Korea.
    Han, Bohyung
    POSTECH, South Korea.
    Wang, Chaohui
    University of Paris Est, France.
    Garcia, Christophe
    LIRIS, France.
    Zhang, Chunyuan
    National University of Def Technology, Peoples R China; National Key Lab Parallel and Distributed Proc, Peoples R China.
    Schmid, Cordelia
    INRIA Grenoble Rhone Alpes, France.
    Tao, Dacheng
    University of Technology Sydney, Australia.
    Kim, Daijin
    POSTECH, South Korea.
    Huang, Dafei
    National University of Def Technology, Peoples R China; National Key Lab Parallel and Distributed Proc, Peoples R China.
    Prokhorov, Danil
    Toyota Research Institute, Japan.
    Du, Dawei
    SUNY Albany, NY USA; Chinese Academic Science, Peoples R China.
    Yeung, Dit-Yan
    Hong Kong University of Science and Technology, Peoples R China.
    Ribeiro, Eraldo
    Florida Institute Technology, FL USA.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Porikli, Fatih
    Australian National University, Australia; NICTA, Australia.
    Bunyak, Filiz
    University of Missouri, MO 65211 USA.
    Zhu, Gao
    Australian National University, Australia.
    Seetharaman, Guna
    Naval Research Lab, DC 20375 USA.
    Kieritz, Hilke
    Fraunhofer IOSB, Germany.
    Tuen Yau, Hing
    Chinese University of Hong Kong, Peoples R China.
    Li, Hongdong
    Australian National University, Australia; ARC Centre Excellence Robot Vis, Australia.
    Qi, Honggang
    SUNY Albany, NY USA; Chinese Academic Science, Peoples R China.
    Bischof, Horst
    Graz University of Technology, Austria.
    Possegger, Horst
    Graz University of Technology, Austria.
    Lee, Hyemin
    POSTECH, South Korea.
    Nam, Hyeonseob
    POSTECH, South Korea.
    Bogun, Ivan
    Florida Institute Technology, FL USA.
    Jeong, Jae-chan
    Elect and Telecommun Research Institute, South Korea.
    Cho, Jae-il
    Elect and Telecommun Research Institute, South Korea.
    Lee, Jae-Young
    Elect and Telecommun Research Institute, South Korea.
    Zhu, Jianke
    Zhejiang University, Peoples R China.
    Shi, Jianping
    CUHK, Peoples R China.
    Li, Jiatong
    Beijing Institute Technology, Peoples R China; University of Technology Sydney, Australia.
    Jia, Jiaya
    CUHK, Peoples R China.
    Feng, Jiayi
    Chinese Academic Science, Peoples R China.
    Gao, Jin
    Chinese Academic Science, Peoples R China.
    Young Choi, Jin
    Seoul National University, South Korea.
    Kim, Ji-Wan
    Elect and Telecommun Research Institute, South Korea.
    Lang, Jochen
    University of Ottawa, Canada.
    Martinez, Jose M.
    University of Autonoma Madrid, Spain.
    Choi, Jongwon
    Seoul National University, South Korea.
    Xing, Junliang
    Chinese Academic Science, Peoples R China.
    Xue, Kai
    Harbin Engn University, Peoples R China.
    Palaniappan, Kannappan
    University of Missouri, MO 65211 USA.
    Lebeda, Karel
    University of Surrey, England.
    Alahari, Karteek
    INRIA Grenoble Rhone Alpes, France.
    Gao, Ke
    University of Missouri, MO 65211 USA.
    Yun, Kimin
    Seoul National University, South Korea.
    Hong Wong, Kin
    Chinese University of Hong Kong, Peoples R China.
    Luo, Lei
    National University of Def Technology, Peoples R China.
    Ma, Liang
    Harbin Engn University, Peoples R China.
    Ke, Lipeng
    SUNY Albany, NY USA; Chinese Academic Science, Peoples R China.
    Wen, Longyin
    SUNY Albany, NY USA.
    Bertinetto, Luca
    University of Oxford, England.
    Pootschi, Mandieh
    University of Missouri, MO 65211 USA.
    Maresca, Mario
    Parthenope University of Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Wen, Mei
    National University of Def Technology, Peoples R China; National Key Lab Parallel and Distributed Proc, Peoples R China.
    Zhang, Mengdan
    Chinese Academic Science, Peoples R China.
    Arens, Michael
    Fraunhofer IOSB, Germany.
    Valstar, Michel
    University of Nottingham, England.
    Tang, Ming
    Chinese Academic Science, Peoples R China.
    Chang, Ming-Ching
    SUNY Albany, NY USA.
    Haris Khan, Muhammad
    University of Nottingham, England.
    Fan, Nana
    Harbin Institute Technology, Peoples R China.
    Wang, Naiyan
    TuSimple LLC, CA USA; Hong Kong University of Science and Technology, Peoples R China.
    Miksik, Ondrej
    University of Oxford, England.
    Torr, Philip H. S.
    University of Oxford, England.
    Wang, Qiang
    Chinese Academic Science, Peoples R China.
    Martin-Nieto, Rafael
    University of Autonoma Madrid, Spain.
    Pelapur, Rengarajan
    University of Missouri, MO 65211 USA.
    Bowden, Richard
    University of Surrey, England.
    Laganiere, Robert
    University of Ottawa, Canada.
    Moujtahid, Salma
    University of Lyon, France.
    Hare, Sam
    Obvious Engn, England.
    Hadfield, Simon
    University of Surrey, England.
    Lyu, Siwei
    SUNY Albany, NY USA.
    Li, Siyi
    Hong Kong University of Science and Technology, Peoples R China.
    Zhu, Song-Chun
    University of California, USA.
    Becker, Stefan
    Fraunhofer IOSB, Germany.
    Duffner, Stefan
    University of Lyon, France; LIRIS, France.
    Hicks, Stephen L.
    University of Oxford, England.
    Golodetz, Stuart
    University of Oxford, England.
    Choi, Sunglok
    Elect and Telecommun Research Institute, South Korea.
    Wu, Tianfu
    University of California, USA.
    Mauthner, Thomas
    Graz University of Technology, Austria.
    Pridmore, Tony
    University of Nottingham, England.
    Hu, Weiming
    Chinese Academic Science, Peoples R China.
    Hubner, Wolfgang
    Fraunhofer IOSB, Germany.
    Wang, Xiaomeng
    University of Nottingham, England.
    Li, Xin
    Harbin Institute Technology, Peoples R China.
    Shi, Xinchu
    Chinese Academic Science, Peoples R China.
    Zhao, Xu
    Chinese Academic Science, Peoples R China.
    Mei, Xue
    Toyota Research Institute, Japan.
    Shizeng, Yao
    University of Missouri, USA.
    Hua, Yang
    INRIA Grenoble Rhône-Alpes, France.
    Li, Yang
    Zhejiang University, Peoples R China.
    Lu, Yang
    University of California, USA.
    Li, Yuezun
    SUNY Albany, NY USA.
    Chen, Zhaoyun
    National University of Def Technology, Peoples R China; National Key Lab Parallel and Distributed Proc, Peoples R China.
    Huang, Zehua
    Carnegie Mellon University, PA 15213 USA.
    Chen, Zhe
    University of Technology Sydney, Australia.
    Zhang, Zhe
    Baidu Corp, Peoples R China.
    He, Zhenyu
    Harbin Institute Technology, Peoples R China.
    Hong, Zhibin
    University of Technology Sydney, Australia.
    The Visual Object Tracking VOT2015 challenge results2015In: Proceedings 2015 IEEE International Conference on Computer Vision Workshops ICCVW 2015, IEEE , 2015, p. 564-586Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website(1).

  • 198.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Pflugfelder, Roman
    Austrian Institute Technology, Austria.
    Leonardis, Ales
    University of Birmingham, England.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Porikli, Fatih
    Australian National University, Australia.
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Nebehay, Georg
    Austrian Institute Technology, Austria.
    Fernandez, Gustavo
    Austrian Institute Technology, Austria.
    Vojir, Tomas
    Czech Technical University, Czech Republic.
    Gatt, Adam
    DSTO, Australia.
    Khajenezhad, Ahmad
    Sharif University of Technology, Iran.
    Salahledin, Ahmed
    Nile University, Egypt.
    Soltani-Farani, Ali
    Sharif University of Technology, Iran.
    Zarezade, Ali
    Sharif University of Technology, Iran.
    Petrosino, Alfredo
    Parthenope University of Naples, Italy.
    Milton, Anthony
    University of S Australia, Australia.
    Bozorgtabar, Behzad
    University of Canberra, Australia.
    Li, Bo
    Panason RandD Centre, Singapore.
    Seng Chan, Chee
    University of Malaya, Malaysia.
    Heng, CherKeng
    Panason RandD Centre, Singapore.
    Ward, Dale
    University of S Australia, Australia.
    Kearney, David
    University of S Australia, Australia.
    Monekosso, Dorothy
    University of W England, England.
    Can Karaimer, Hakki
    Izmir Institute Technology, Turkey.
    Rabiee, Hamid R.
    Sharif University of Technology, Iran.
    Zhu, Jianke
    Zhejiang University, Peoples R China.
    Gao, Jin
    Chinese Academic Science, Peoples R China.
    Xiao, Jingjing
    University of Birmingham, England.
    Zhang, Junge
    Chinese Academic Science, Peoples R China.
    Xing, Junliang
    Chinese Academic Science, Peoples R China.
    Huang, Kaiqi
    Chinese Academic Science, Peoples R China.
    Lebeda, Karel
    University of Surrey, England.
    Cao, Lijun
    Chinese Academic Science, Peoples R China.
    Edoardo Maresca, Mario
    Parthenope University of Naples, Italy.
    Kuan Lim, Mei
    University of Malaya, Malaysia.
    ELHelw, Mohamed
    Nile University, Egypt.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Remagnino, Paolo
    University of Kingston, Canada.
    Bowden, Richard
    University of Surrey, England.
    Goecke, Roland
    Australian National University, Australia.
    Stolkin, Rustam
    University of Birmingham, England.
    YueYing Lim, Samantha
    Panason RandD Centre, Singapore.
    Maher, Sara
    Nile University, Egypt.
    Poullot, Sebastien
    JFLI, Japan.
    Wong, Sebastien
    DSTO, Edinburgh, SA, Australia.
    Satoh, Shinichi
    NII, Japan.
    Chen, Weihua
    Chinese Academic Science, Peoples R China.
    Hu, Weiming
    Chinese Academic Science, Peoples R China.
    Zhang, Xiaoqin
    Chinese Academic Science, Peoples R China.
    Li, Yang
    Zhejiang University, China.
    Niu, ZhiHeng
    Panason RandD Centre, Singapore.
    The Visual Object Tracking VOT2013 challenge results2013In: 2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), IEEE , 2013, p. 98-111Conference paper (Refereed)
    Abstract [en]

    Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website(1).

  • 199.
    Kristan, Matej
    et al.
    University of Ljubljana, Ljubljana, Slovenia.
    Pflugfelder, Roman P.
    Austrian Institute of Technology, Vienna, Austria.
    Leonardis, Ales
    University of Birmingham, Birmingham, UK.
    Matas, Jiri
    Czech Technical University, Prague, Czech Republic.
    Cehovin, Luka
    University of Ljubljana, Ljubljana, Slovenia.
    Nebehay, Georg
    Austrian Institute of Technology, Vienna, Austria.
    Vojir, Tomas
    Czech Technical University, Prague, Czech Republic.
    Fernandez, Gustavo
    Austrian Institute of Technology, Vienna, Austria.
    Lukezi, Alan
    University of Ljubljana, Ljubljana, Slovenia.
    Dimitriev, Aleksandar
    University of Ljubljana, Ljubljana, Slovenia.
    Petrosino, Alfredo
    Parthenope University of Naples, Naples, Italy.
    Saffari, Amir
    Affectv Limited, London, UK.
    Li, Bo
    Panasonic R&D Center, Singapore, Singapore.
    Han, Bohyung
    POSTECH, Pohang, Korea.
    Heng, CherKeng
    Panasonic R&D Center, Singapore, Singapore.
    Garcia, Christophe
    LIRIS, Lyon, France.
    Pangersic, Dominik
    University of Ljubljana, Ljubljana, Slovenia.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Oven, Franci
    University of Ljubljana, Ljubljana, Slovenia.
    Possegger, Horst
    Graz University of Technology, Graz, Austria.
    Bischof, Horst
    Graz University of Technology, Graz, Austria.
    Nam, Hyeonseob
    POSTECH, Pohang, Korea.
    Zhu, Jianke
    Zhejiang University, Hangzhou, China.
    Li, JiJia
    Shanghai Jiao Tong University, Shanghai, China.
    Choi, Jin Young
    ASRI Seoul National University, Gwanak, Korea.
    Choi, Jin-Woo
    Electronics and Telecommunications Research Institute, Daejeon, Korea.
    Henriques, Joao F.
    University of Coimbra, Coimbra, Portugal.
    van de Weijer, Joost
    Universitat Autonoma de Barcelona, Barcelona, Spain.
    Batista, Jorge
    University of Coimbra, Coimbra, Portugal.
    Lebeda, Karel
    University of Surrey, Surrey, UK.
    Ofjall, Kristoffer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yi, Kwang Moo
    EPFL CVLab, Lausanne, Switzerland.
    Qin, Lei
    ICT CAS, Beijing, China.
    Wen, Longyin
    Chinese Academy of Sciences, Beijing, China.
    Maresca, Mario Edoardo
    Parthenope University of Naples, Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Cheng, Ming-Ming
    University of Oxford, Oxford, UK.
    Torr, Philip
    University of Oxford, Oxford, UK.
    Huang, Qingming
    Harbin Institute of Technology, Harbin, China.
    Bowden, Richard
    University of Surrey, Surrey, UK.
    Hare, Sam
    Obvious Engineering Limited, London, UK.
    YueYing Lim, Samantha
    Panasonic R&D Center, Singapore, Singapore.
    Hong, Seunghoon
    POSTECH, Pohang, Korea.
    Liao, Shengcai
    Chinese Academy of Sciences, Beijing, China.
    Hadfield, Simon
    University of Surrey, Surrey, UK.
    Li, Stan Z.
    Chinese Academy of Sciences, Beijing, China.
    Duffner, Stefan
    LIRIS, Lyon, France.
    Golodetz, Stuart
    University of Oxford, Oxford, UK.
    Mauthner, Thomas
    Graz University of Technology, Graz, Austria.
    Vineet, Vibhav
    University of Oxford, Oxford, UK.
    Lin, Weiyao
    Shanghai Jiao Tong University, Shanghai, China.
    Li, Yang
    Zhejiang University, Hangzhou, China.
    Qi, Yuankai
    Harbin Institute of Technology, Harbin, China.
    Lei, Zhen
    Chinese Academy of Sciences, Beijing, China.
    Niu, ZhiHeng
    Panasonic R&D Center, Singapore, Singapore.
    The Visual Object Tracking VOT2014 Challenge Results2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Springer, 2015, Vol. 8926, p. 191-217Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2014 challenge that go beyond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execution of experiments. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://​votchallenge.​net).

  • 200.
    Landberg, Markus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Enhancement Techniques for Lane PositionAdaptation (Estimation) using GPS- and Map Data2014Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A lane position system and enhancement techniques, for increasing the robustnessand availability of such a system, are investigated. The enhancements areperformed by using additional sensor sources like map data and GPS. The thesiscontains a description of the system, two models of the system and two implementedfilters for the system. The thesis also contains conclusions and results oftheoretical and experimental tests of the increased robustness and availability ofthe system. The system can be integrated with an existing system that investigatesdriver behavior, developed for fatigue. That system was developed in aproject named Drowsi, where among others Volvo Technology participated.

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  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf