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The Tenth Visual Object Tracking VOT2022 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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2023 (English)In: Computer Vision – ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VIII / [ed] Leonid Karlinsky, Tomer Michaeli, Ko Nishino, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 13808 LNCS, p. 431-460Conference paper, Published paper (Refereed)
Abstract [en]

The Visual Object Tracking challenge VOT2022 is the tenth annual tracker benchmarking activity organized by the VOT initiative. Results of 93 entries are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2022 challenge was composed of seven sub-challenges focusing on different tracking domains: (i) VOT-STs2022 challenge focused on short-term tracking in RGB by segmentation, (ii) VOT-STb2022 challenge focused on short-term tracking in RGB by bounding boxes, (iii) VOT-RTs2022 challenge focused on “real-time” short-term tracking in RGB by segmentation, (iv) VOT-RTb2022 challenge focused on “real-time” short-term tracking in RGB by bounding boxes, (v) VOT-LT2022 focused on long-term tracking, namely coping with target disappearance and reappearance, (vi) VOT-RGBD2022 challenge focused on short-term tracking in RGB and depth imagery, and (vii) VOT-D2022 challenge focused on short-term tracking in depth-only imagery. New datasets were introduced in VOT-LT2022 and VOT-RGBD2022, VOT-ST2022 dataset was refreshed, and a training dataset was introduced for VOT-LT2022. The source code for most of the trackers, the datasets, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. Vol. 13808 LNCS, p. 431-460
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13808
Keywords [en]
Long-term tracking; Performance evaluation; Short-term tracking; Visual Object Tracking challenge; VOT
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:liu:diva-209221DOI: 10.1007/978-3-031-25085-9_25Scopus ID: 2-s2.0-85151355577ISBN: 9783031250842 (print)ISBN: 9783031250859 (electronic)OAI: oai:DiVA.org:liu-209221DiVA, id: diva2:1911034
Conference
17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, October 23–27, 2022
Note

Funding Agencies|ELLIIT; Faculty of Computer Science, University of Ljubljana; Institute of Information and communications Technology Planning; Slovenian research agency research program, (J2-2506, P2-0214); Swedish government; Wallenberg research arena for Media and Language; Ashikaga Institute of Technology, AIT; Horizon 2020 Framework Programme, H2020, (899987); Horizon 2020 Framework Programme, H2020; National Science Council, NSC; Ministry of Science, ICT and Future Planning, MSIP, (2021-0-00537); Ministry of Science, ICT and Future Planning, MSIP; Knut och Alice Wallenbergs Stiftelse; Institute for Information and Communications Technology Promotion, IITP; Fundamental Research Funds for the Central Universities, (226-2022-00051); Fundamental Research Funds for the Central Universities

Available from: 2024-11-06 Created: 2024-11-06 Last updated: 2025-02-07

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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