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The Eighth Visual Object Tracking VOT2020 Challenge Results
University of Ljubljana, Ljubljana, Slovenia.
University of Birmingham, Birmingham, United Kingdom.
Czech Technical University, Prague, Czech Republic.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
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2020 (English)In: Computer Vision: ECCV 2020 Workshops, Glasgow, UK, August 23–28, 2020 / [ed] Adrien Bartoli; Andrea Fusiello, 2020, Vol. 12539, p. 547-601Conference paper, Published paper (Refereed)
Abstract [en]

The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. 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 ). 

Place, publisher, year, edition, pages
2020. Vol. 12539, p. 547-601
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12539
Keywords [en]
Depth; Long-term trackers; Performance evaluation protocol; RGB; RGBD; RGBT; Short-term trackers; State-of-the-art benchmark; Thermal imagery; Visual object tracking
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:liu:diva-179796DOI: 10.1007/978-3-030-68238-5_39Scopus ID: 2-s2.0-85101374294ISBN: 9783030682378 (electronic)OAI: oai:DiVA.org:liu-179796DiVA, id: diva2:1599875
Conference
ECCV 20 European Conference on Computer Vision
Available from: 2021-10-02 Created: 2021-10-02 Last updated: 2025-02-07

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Felsberg, MichaelHe, LinboRobinson, AndreasJäremo-Lawin, Felix

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Felsberg, MichaelHe, LinboZhang, YushanRobinson, AndreasJäremo-Lawin, Felix
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