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BETA
Häger, Gustav
Publications (10 of 10) Show all publications
Häger, G., Felsberg, M. & Khan, F. S. (2018). Countering bias in tracking evaluations. In: Francisco Imai, Alain Tremeau and Jose Braz (Ed.), Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: . Paper presented at 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, January 27-29, Funchal, Madeira (pp. 581-587). Science and Technology Publications, Lda, 5
Open this publication in new window or tab >>Countering bias in tracking evaluations
2018 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Science and Technology Publications, Lda, 2018
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-151306 (URN)10.5220/0006714805810587 (DOI)9789897582905 (ISBN)
Conference
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, January 27-29, Funchal, Madeira
Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2019-06-26Bibliographically approved
Danelljan, M., Häger, G., Khan, F. S. & Felsberg, M. (2016). Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): . Paper presented at 29th IEEE Conference on Computer Vision and Pattern Recognition, 27-30 June 2016, Las Vegas, NV, USA (pp. 1430-1438). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking
2016 (English)In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1430-1438Conference paper, Published paper (Refereed)
Abstract [en]

Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
IEEE Conference on Computer Vision and Pattern Recognition, E-ISSN 1063-6919 ; 2016
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-137882 (URN)10.1109/CVPR.2016.159 (DOI)000400012301051 ()9781467388511 (ISBN)9781467388528 (ISBN)
Conference
29th IEEE Conference on Computer Vision and Pattern Recognition, 27-30 June 2016, Las Vegas, NV, USA
Note

Funding Agencies|SSF (CUAS); VR (EMC2); VR (ELLIIT); Wallenberg Autonomous Systems Program; NSC; Nvidia

Available from: 2017-06-01 Created: 2017-06-01 Last updated: 2019-06-27Bibliographically approved
Berg, A., Felsberg, M., Häger, G. & Ahlberg, J. (2016). An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge. In: : . Paper presented at Swedish Symposium on Image Analysis.
Open this publication in new window or tab >>An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge
2016 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

The Thermal Infrared Visual Object Tracking (VOT-TIR2015) Challenge was organized in conjunction with ICCV2015. It was the first benchmark on short-term,single-target tracking in thermal infrared (TIR) sequences. The challenge aimed at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. It was based on the VOT2013 Challenge, but introduced the following novelties: (i) the utilization of the LTIR (Linköping TIR) dataset, (ii) adaption of the VOT2013 attributes to thermal data, (iii) a similar evaluation to that of VOT2015. This paper provides an overview of the VOT-TIR2015 Challenge as well as the results of the 24 participating trackers.

Series
Svenska sällskapet för automatiserad bildanalys (SSBA)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-127598 (URN)
Conference
Swedish Symposium on Image Analysis
Available from: 2016-05-03 Created: 2016-05-03 Last updated: 2018-01-10Bibliographically approved
Häger, G., Bhat, G., Danelljan, M., Khan, F. S., Felsberg, M., Rudol, P. & Doherty, P. (2016). Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV. In: Proceedings of the 12th International Symposium on Advances in Visual Computing: . Paper presented at International Symposium on Advances in Visual Computing.
Open this publication in new window or tab >>Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV
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2016 (English)In: Proceedings of the 12th International Symposium on Advances in Visual Computing, 2016Conference paper, Published 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.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-137897 (URN)10.1007/978-3-319-50835-1_50 (DOI)2-s2.0-85007039301 (Scopus ID)978-3-319-50834-4 (ISBN)978-3-319-50835-1 (ISBN)
Conference
International Symposium on Advances in Visual Computing
Available from: 2017-05-31 Created: 2017-05-31 Last updated: 2018-01-13Bibliographically approved
Danelljan, M., Häger, G., Khan, F. S. & Felsberg, M. (2015). Coloring Channel Representations for Visual Tracking. In: Rasmus R. Paulsen, Kim S. Pedersen (Ed.), 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings: . Paper presented at Scandinavian Conference on Image Analysis (pp. 117-129). Springer, 9127
Open this publication in new window or tab >>Coloring Channel Representations for Visual Tracking
2015 (English)In: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings / [ed] Rasmus R. Paulsen, Kim S. Pedersen, Springer, 2015, Vol. 9127, p. 117-129Conference paper, Published paper (Refereed)
Abstract [en]

Visual object tracking is a classical, but still open research problem in computer vision, with many real world applications. The problem is challenging due to several factors, such as illumination variation, occlusions, camera motion and appearance changes. Such problems can be alleviated by constructing robust, discriminative and computationally efficient visual features. Recently, biologically-inspired channel representations \cite{felsberg06PAMI} have shown to provide promising results in many applications ranging from autonomous driving to visual tracking.

This paper investigates the problem of coloring channel representations for visual tracking. We evaluate two strategies, channel concatenation and channel product, to construct channel coded color representations. The proposed channel coded color representations are generic and can be used beyond tracking.

Experiments are performed on 41 challenging benchmark videos. Our experiments clearly suggest that a careful selection of color feature together with an optimal fusion strategy, significantly outperforms the standard luminance based channel representation. Finally, we show promising results compared to state-of-the-art tracking methods in the literature.

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9127
Keywords
Visual tracking, channel coding, color names
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-121003 (URN)10.1007/978-3-319-19665-7_10 (DOI)978-3-319-19664-0 (ISBN)978-3-319-19665-7 (ISBN)
Conference
Scandinavian Conference on Image Analysis
Available from: 2015-09-02 Created: 2015-09-02 Last updated: 2018-04-25Bibliographically approved
Danelljan, M., Häger, G., Khan, F. S. & Felsberg, M. (2015). Convolutional Features for Correlation Filter Based Visual Tracking. In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW): . Paper presented at 15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015, 7-13 December 2015, Santiago, Chile (pp. 621-629). IEEE conference proceedings
Open this publication in new window or tab >>Convolutional Features for Correlation Filter Based Visual Tracking
2015 (English)In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 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
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-128869 (URN)10.1109/ICCVW.2015.84 (DOI)000380434700075 ()9781467397117 (ISBN)9781467397100 (ISBN)
Conference
15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015, 7-13 December 2015, Santiago, Chile
Available from: 2016-06-02 Created: 2016-06-02 Last updated: 2019-06-26Bibliographically approved
Danelljan, M., Häger, G., Khan, F. S. & Felsberg, M. (2015). Learning Spatially Regularized Correlation Filters for Visual Tracking. In: Proceedings of the International Conference in Computer Vision (ICCV), 2015: . Paper presented at International Conference in Computer Vision (ICCV), Santiago, Chile, December 13-16, 2015 (pp. 4310-4318). IEEE Computer Society
Open this publication in new window or tab >>Learning Spatially Regularized Correlation Filters for Visual Tracking
2015 (English)In: Proceedings of the International Conference in Computer Vision (ICCV), 2015, IEEE Computer Society, 2015, p. 4310-4318Conference paper, Published paper (Refereed)
Abstract [en]

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied to address this problem for tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood. However, the periodic assumption also introduces unwanted boundary effects, which severely degrade the quality of the tracking model.

We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. A spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location. Our SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples. We further propose an optimization strategy, based on the iterative Gauss-Seidel method, for efficient online learning of our SRDCF. Experiments are performed on four benchmark datasets: OTB-2013, ALOV++, OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and 8.2% respectively, in mean overlap precision, compared to the best existing trackers.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015
Series
IEEE International Conference on Computer Vision. Proceedings, ISSN 1550-5499
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-121609 (URN)10.1109/ICCV.2015.490 (DOI)000380414100482 ()978-1-4673-8390-5 (ISBN)
Conference
International Conference in Computer Vision (ICCV), Santiago, Chile, December 13-16, 2015
Available from: 2015-09-28 Created: 2015-09-28 Last updated: 2018-04-25
Felsberg, M., Berg, A., Häger, G., Ahlberg, J., Kristan, M., Matas, J., . . . Hong, Z. (2015). The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results. In: Proceedings of the IEEE International Conference on Computer Vision: . Paper presented at IEEE International Conference on Computer Vision Workshop (ICCVW. 7-13 Dec. 2015 Santiago, Chile (pp. 639-651). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
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2015 (English)In: Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 639-651Conference paper, Published paper (Refereed)
Abstract [en]

The Thermal Infrared Visual Object Tracking challenge 2015, VOTTIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply prelearned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015
Series
IEEE International Conference on Computer Vision. Proceedings, ISSN 1550-5499
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-126917 (URN)10.1109/ICCVW.2015.86 (DOI)000380434700077 ()978-146738390-5 (ISBN)
External cooperation:
Conference
IEEE International Conference on Computer Vision Workshop (ICCVW. 7-13 Dec. 2015 Santiago, Chile
Available from: 2016-04-07 Created: 2016-04-07 Last updated: 2018-01-10Bibliographically approved
Kristan, M., Pflugfelder, R. P., Leonardis, A., Matas, J., Cehovin, L., Nebehay, G., . . . Niu, Z. (2015). The Visual Object Tracking VOT2014 Challenge Results. In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II: . Paper presented at 13th European Conference on Computer Vision (ECCV), September 6-12, Zurich, Switzerland (pp. 191-217). Springer, 8926
Open this publication in new window or tab >>The Visual Object Tracking VOT2014 Challenge Results
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2015 (English)In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Springer, 2015, Vol. 8926, p. 191-217Conference paper, Published 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).

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 8926
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-121006 (URN)10.1007/978-3-319-16181-5_14 (DOI)000362495500014 ()978-3-319-16180-8 (ISBN)978-3-319-16181-5 (ISBN)
Conference
13th European Conference on Computer Vision (ECCV), September 6-12, Zurich, Switzerland
Available from: 2015-09-02 Created: 2015-09-02 Last updated: 2018-01-11Bibliographically approved
Danelljan, M., Häger, G., Khan, F. & Felsberg, M. (2014). Accurate Scale Estimation for Robust Visual Tracking. In: Michel Valstar, Andrew French and Tony Pridmore (Ed.), Proceedings of the British Machine Vision Conference 2014: . Paper presented at British Machine Vision Conference, Nottingham, September 1-5, 2014. BMVA Press
Open this publication in new window or tab >>Accurate Scale Estimation for Robust Visual Tracking
2014 (English)In: Proceedings of the British Machine Vision Conference 2014 / [ed] Michel Valstar, Andrew French and Tony Pridmore, BMVA Press , 2014Conference paper, Published paper (Refereed)
Abstract [en]

Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation.

Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18.8 % in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-the-art trackers in literature. The proposed method is shown to outperform the best existing tracker by 16.6 % in median distance precision, while operating at real-time.

Place, publisher, year, edition, pages
BMVA Press, 2014
National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-113948 (URN)10.5244/C.28.65 (DOI)1901725529 (ISBN)
Conference
British Machine Vision Conference, Nottingham, September 1-5, 2014
Available from: 2015-02-03 Created: 2015-02-03 Last updated: 2016-06-08Bibliographically approved
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