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The Visual Object Tracking {VOT2014} Challenge Results
University of Ljubljana, Ljubljana, Slovenia.
Austrian Institute of Technology, Vienna, Austria.
University of Birmingham, Birmingham, UK.
Czech Technical University, Prague, Czech Republic.
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2015 (English)In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Springer, 2015, Vol. 8926, 191-217 p.Conference paper (Other academic)
Abstract [en]

The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2014 challenge that go beyond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execution of experiments. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://​votchallenge.​net).

Place, publisher, year, edition, pages
Springer, 2015. Vol. 8926, 191-217 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8926
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-121006DOI: 10.1007/978-3-319-16181-5_14ISI: 000362495500014ISBN: 978-3-319-16180-8ISBN: 978-3-319-16181-5OAI: diva2:850764
13th European Conference on Computer Vision (ECCV)
Available from: 2015-09-02 Created: 2015-09-02 Last updated: 2016-05-04Bibliographically approved

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Häger, GustavKhan, Fahad ShahbazOfjall, KristofferDanelljan, MartinFelsberg, Michael
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