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The Sixth Visual Object Tracking VOT2018 Challenge Results
University of Ljubljana, Slovenia.
University of Birmingham, United Kingdom.
Czech Technical University, Czech Republic.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
Show others and affiliations
2019 (English)In: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8–14, 2018 Proceedings, Part I / [ed] Laura Leal-Taixé and Stefan Roth, Cham: Springer Publishing Company, 2019, p. 3-53Conference paper, Published paper (Refereed)
Abstract [en]

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. 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
Cham: Springer Publishing Company, 2019. p. 3-53
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11129
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-161343DOI: 10.1007/978-3-030-11009-3_1ISBN: 9783030110086 (print)ISBN: 9783030110093 (electronic)OAI: oai:DiVA.org:liu-161343DiVA, id: diva2:1366619
Conference
Computer Vision – ECCV 2018 Workshops, Munich, Germany, September 8–14, 2018
Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2019-10-30Bibliographically approved

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