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The third Visual Object Tracking Segmentation VOTS2025 challenge results
University of Ljubljana, Slovenia.
Czech Technical University, Czech Republic.
Toyota Research Institute, United States.
University of Ljubljana, Slovenia.
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2025 (English)In: 2025 IEEE/CVF International Conference on Computer Vision Workshops: ICCV-W 2025, Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2025, p. 7472-7490Conference paper, Published paper (Refereed)
Abstract [en]

The VOTS2025 is the third edition of the Visual Object Tracking Segmentation benchmark. Organised the VOT initiative, VOTS builds on 10 years of experience in organising VOT challenges. Building on the tracking setup introduced in VOTS2023, the challenge continues to integrate short-term and long-term tracking, as well as single-target and multi-target scenarios, using segmentation masks as the sole form of target annotation. This year's benchmark features three sub-challenges. VOTS2025 and VOTSt2025, evaluate tracking of conventional objects and objects undergoing topological changes, respectively. A new addition, VOTS-RT2025, aims to foster the development of efficient tracking models by introducing constraints that highlight realtime performance. All sub-challenges adopt a consistent evaluation protocol, with VOTS-RT2025 introducing specific modifications to reflect latency-aware performance. We report and analyze results from 32 submissions. Full tracker descriptions, source code, datasetsand the evaluation toolkit are available on the project website (https://www.votchallenge.net/vots2025/=.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2025. p. 7472-7490
Series
IEEE International Conference on Computer Vision Workshops, ISSN 2473-9936, E-ISSN 2473-9944
Keywords [en]
general object tracking, video object segmentation, visual object tracking, VOTS
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:umu:diva-252380DOI: 10.1109/ICCVW69036.2025.00771Scopus ID: 2-s2.0-105035209983ISBN: 9798331589882 (electronic)ISBN: 9798331589899 (print)OAI: oai:DiVA.org:umu-252380DiVA, id: diva2:2056306
Conference
2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025, 19-20 October 2025 Honolulu, United States
Available from: 2026-04-28 Created: 2026-04-28 Last updated: 2026-04-28Bibliographically approved

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Tran, Khanh-TungBjörklund, Johanna

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Total: 67 hits
CiteExportLink to record
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Cite
Citation style
  • apa
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  • de-DE
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  • en-US
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  • nn-NB
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Output format
  • html
  • text
  • asciidoc
  • rtf