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| 2. |
- Felsberg, Michael, et al.
(författare)
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The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
- 2015
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Ingår i: Proceedings of the IEEE International Conference on Computer Vision. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467383905 ; , s. 639-651
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Felsberg, Michael, 1974-, et al.
(författare)
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The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
- 2016
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Ingår i: Computer Vision – ECCV 2016 Workshops. ECCV 2016.. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 824-849
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Konferensbidrag (refereegranskat)abstract
- The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second 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-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.
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| 4. |
- Häger, Gustav, 1988-
(författare)
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Learning visual perception for autonomous systems
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the last decade, developments in hardware, sensors and software have made it possible to create increasingly autonomous systems. These systems can be as simple as limited driver assistance software lane-following in cars, or limited collision warning systems for otherwise manually piloted drones. On the other end of the spectrum exist fully autonomous cars, boats or helicopters. With increasing abilities to function autonomously, the demands to operate with minimal human supervision in unstructured environments increase accordingly.Common to most, if not all, autonomous systems is that they require an accurate model of the surrounding world. While there is currently a large number of possible sensors useful to create such models available, cameras are one of the most versatile. From a sensing perspective cameras have several advantages over other sensors in that they require no external infrastructure, are relatively cheap and can be used to extract such information as the relative positions of other objects, their movements over time, create accurate maps and locate the autonomous system within these maps.Using cameras to produce a model of the surroundings require solving a number of technical problems. Often these problems have a basis in recognizing that an object or region of interest is the same over time or in novel viewpoints. In visual tracking this type of recognition is required to follow an object of interest through a sequence of images. In geometric problems it is often a requirement to recognize corresponding image regions in order to perform 3D reconstruction or localization. The first set of contributions in this thesis is related to the improvement of a class of on-line learned visual object trackers based on discriminative correlation filters. In visual tracking estimation of the objects size is important for reliable tracking, the first contribution in this part of the thesis investigates this problem. The performance of discriminative correlation filters is highly dependent on what feature representation is used by the filter. The second tracking contribution investigates the performance impact of different features derived from a deep neural network.A second set of contributions relate to the evaluation of visual object trackers. The first of these are the visual object tracking challenge. This challenge is a yearly comparison of state-of-the art visual tracking algorithms. A second contribution is an investigation into the possible issues when using bounding-box representations for ground-truth data.In real world settings tracking typically occur over longer time sequences than is common in benchmarking datasets. In such settings it is common that the model updates of many tracking algorithms cause the tracker to fail silently. For this reason it is important to have an estimate of the trackers performance even in cases when no ground-truth annotations exist. The first of the final three contributions investigates this problem in a robotics setting, by fusing information from a pre-trained object detector in a state-estimation framework. An additional contribution describes how to dynamically re-weight the data used for the appearance model of a tracker. A final contribution investigates how to obtain an estimate of how certain detections are in a setting where geometrical limitations can be imposed on the search region. The proposed solution learns to accurately predict stereo disparities along with accurate assessments of each predictions certainty.
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| 5. |
- Kristan, Matej, et al.
(författare)
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The Ninth Visual Object Tracking VOT2021 Challenge Results
- 2021
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Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
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| 6. |
- Kristan, Matej, et al.
(författare)
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The Sixth Visual Object Tracking VOT2018 Challenge Results
- 2019
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Ingår i: Computer Vision – ECCV 2018 Workshops. - Cham : Springer Publishing Company. - 9783030110086 - 9783030110093 ; , s. 3-53
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Konferensbidrag (refereegranskat)abstract
- 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).
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| 7. |
- Kristan, Matej, et al.
(författare)
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The Visual Object Tracking VOT2013 challenge results
- 2013
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Ingår i: 2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE. - 9781479930227 ; , s. 98-111
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Konferensbidrag (refereegranskat)abstract
- 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).
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| 8. |
- Kristan, Matej, et al.
(författare)
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The Visual Object Tracking VOT2014 Challenge Results
- 2015
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Ingår i: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II. - Cham : Springer. - 9783319161808 - 9783319161815 ; , s. 191-217
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Konferensbidrag (refereegranskat)abstract
- 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).
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| 9. |
- Kristan, Matej, et al.
(författare)
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The Visual Object Tracking VOT2015 challenge results
- 2015
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Ingår i: Proceedings 2015 IEEE International Conference on Computer Vision Workshops ICCVW 2015. - : IEEE. - 9780769557205 ; , s. 564-586
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Konferensbidrag (refereegranskat)abstract
- 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).
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| 10. |
- Kristan, Matej, et al.
(författare)
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The Visual Object Tracking VOT2016 Challenge Results
- 2016
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Ingår i: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 777-823
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Konferensbidrag (refereegranskat)abstract
- 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.
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