liu.seSearch for publications in DiVA
EnglishSvenskaNorsk
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
The Ninth Visual Object Tracking VOT2021 Challenge Results
Univ Ljubljana, Slovenia.
Czech Tech Univ, Czech Republic.
Univ Birmingham, England.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
Show others and affiliations
2021 (English)In: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021), IEEE COMPUTER SOC , 2021, p. 2711-2738Conference paper, Published paper (Refereed)
Abstract [en]

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).
Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2021. p. 2711-2738
Series
IEEE International Conference on Computer Vision Workshops, ISSN 2473-9936
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:liu:diva-183275DOI: 10.1109/ICCVW54120.2021.00305ISI: 000739651102089ISBN: 9781665401913 (electronic)OAI: oai:DiVA.org:liu-183275DiVA, id: diva2:1643014
Conference
IEEE/CVF International Conference on Computer Vision (ICCVW), ELECTR NETWORK, oct 11-17, 2021
Available from: 2022-03-08 Created: 2022-03-08 Last updated: 2025-02-07

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Felsberg, MichaelHäger, GustavJäremo-Lawin, FelixRobinson, Andreas
By organisation
Computer VisionFaculty of Science & Engineering
Computer graphics and computer vision

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 181 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.47.0
|
Accessibility
|
DiVA portal
|
DiVA Log in
|
Contact us
|
LiU University Library
|
SwePub
|
Uppsök