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Convolutional Features for Correlation Filter Based Visual Tracking
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.ORCID iD: 0000-0002-6096-3648
2015 (English)In: Proceedings of the IEEE International Conference on Computer Vision, IEEE conference proceedings, 2015, p. 621-629Conference paper, Published paper (Refereed)
Abstract [en]

Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep features (fully connected layers). Firstly, they mitigate the need of task specific fine-tuning. Secondly, they contain structural information crucial for the tracking problem. Lastly, these activations have low dimensionality. We perform comprehensive experiments on three benchmark datasets: OTB, ALOV300++ and the recently introduced VOT2015. Surprisingly, different to image classification, our results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers. Our results further show that the convolutional features provide improved results compared to standard handcrafted features. Finally, results comparable to state-of-theart trackers are obtained on all three benchmark datasets.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. p. 621-629
Series
IEEE International Conference on Computer Vision. Proceedings, ISSN 1550-5499
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-128869DOI: 10.1109/ICCVW.2015.84ISI: 000380434700075ISBN: 978-146738390-5 (print)OAI: oai:DiVA.org:liu-128869DiVA, id: diva2:933006
Conference
15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015
Available from: 2016-06-02 Created: 2016-06-02 Last updated: 2018-04-25
In thesis
1. Learning Convolution Operators for Visual Tracking
Open this publication in new window or tab >>Learning Convolution Operators for Visual Tracking
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Visual tracking is one of the fundamental problems in computer vision. Its numerous applications include robotics, autonomous driving, augmented reality and 3D reconstruction. In essence, visual tracking can be described as the problem of estimating the trajectory of a target in a sequence of images. The target can be any image region or object of interest. While humans excel at this task, requiring little effort to perform accurate and robust visual tracking, it has proven difficult to automate. It has therefore remained one of the most active research topics in computer vision.

In its most general form, no prior knowledge about the object of interest or environment is given, except for the initial target location. This general form of tracking is known as generic visual tracking. The unconstrained nature of this problem makes it particularly difficult, yet applicable to a wider range of scenarios. As no prior knowledge is given, the tracker must learn an appearance model of the target on-the-fly. Cast as a machine learning problem, it imposes several major challenges which are addressed in this thesis.

The main purpose of this thesis is the study and advancement of the, so called, Discriminative Correlation Filter (DCF) framework, as it has shown to be particularly suitable for the tracking application. By utilizing properties of the Fourier transform, a correlation filter is discriminatively learned by efficiently minimizing a least-squares objective. The resulting filter is then applied to a new image in order to estimate the target location.

This thesis contributes to the advancement of the DCF methodology in several aspects. The main contribution regards the learning of the appearance model: First, the problem of updating the appearance model with new training samples is covered. Efficient update rules and numerical solvers are investigated for this task. Second, the periodic assumption induced by the circular convolution in DCF is countered by proposing a spatial regularization component. Third, an adaptive model of the training set is proposed to alleviate the impact of corrupted or mislabeled training samples. Fourth, a continuous-space formulation of the DCF is introduced, enabling the fusion of multiresolution features and sub-pixel accurate predictions. Finally, the problems of computational complexity and overfitting are addressed by investigating dimensionality reduction techniques.

As a second contribution, different feature representations for tracking are investigated. A particular focus is put on the analysis of color features, which had been largely overlooked in prior tracking research. This thesis also studies the use of deep features in DCF-based tracking. While many vision problems have greatly benefited from the advent of deep learning, it has proven difficult to harvest the power of such representations for tracking. In this thesis it is shown that both shallow and deep layers contribute positively. Furthermore, the problem of fusing their complementary properties is investigated.

The final major contribution of this thesis regards the prediction of the target scale. In many applications, it is essential to track the scale, or size, of the target since it is strongly related to the relative distance. A thorough analysis of how to integrate scale estimation into the DCF framework is performed. A one-dimensional scale filter is proposed, enabling efficient and accurate scale estimation.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 71
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1926
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-147543 (URN)10.3384/diss.diva-147543 (DOI)9789176853320 (ISBN)
Public defence
2018-06-11, Ada Lovelace, B-huset, Campus Valla, Linköping, 13:00 (English)
Opponent
Supervisors
Available from: 2018-05-03 Created: 2018-04-25 Last updated: 2018-05-24Bibliographically approved

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Danelljan, MartinHäger, GustavKhan, Fahad ShahbazFelsberg, Michael

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