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The Visual Object Tracking VOT2017 challenge results
Univ Ljubljana, Slovenia.
Univ Birmingham, England.
Czech Tech Univ, 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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2017 (English)In: 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017), IEEE , 2017, p. 1949-1972Conference paper, Published paper (Refereed)
Abstract [en]

The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. 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 VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).

Place, publisher, year, edition, pages
IEEE , 2017. p. 1949-1972
Series
IEEE International Conference on Computer Vision Workshops, ISSN 2473-9936
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-145822DOI: 10.1109/ICCVW.2017.230ISI: 000425239602001ISBN: 978-1-5386-1034-3 OAI: oai:DiVA.org:liu-145822DiVA, id: diva2:1192158
Conference
16th IEEE International Conference on Computer Vision (ICCV)
Note

Funding Agencies|Slovenian research agency research programs [P2-0214, P2-0094]; Slovenian research agency project [J2-8175]; Czech Science Foundation Project [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme Visual Surveillance and Insight; Faculty of Computer Science, University of Ljubljana, Slovenia

Available from: 2018-03-21 Created: 2018-03-21 Last updated: 2018-03-21

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Felsberg, MichaelHäger, GustavEldesokey, AbdelrahmanKhan, FahadBhat, GoutamDanelljan, Martin
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • 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