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The Visual Object Tracking VOT2016 Challenge Results
University of Ljubljana, Slovenia.
University of Birmingham, England.
Czech Technical University, Czech Republic.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-6096-3648
Vise andre og tillknytning
2016 (engelsk)Inngår i: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II, SPRINGER INT PUBLISHING AG , 2016, Vol. 9914, s. 777-823Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment.

sted, utgiver, år, opplag, sider
SPRINGER INT PUBLISHING AG , 2016. Vol. 9914, s. 777-823
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9914
Emneord [en]
Performance evaluation; Short-term single-object trackers; VOT
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-133772DOI: 10.1007/978-3-319-48881-3_54ISI: 000389501700054ISBN: 978-3-319-48881-3 (tryckt)ISBN: 978-3-319-48880-6 (tryckt)OAI: oai:DiVA.org:liu-133772DiVA, id: diva2:1063965
Konferanse
14th European Conference on Computer Vision (ECCV)
Tilgjengelig fra: 2017-01-11 Laget: 2017-01-09 Sist oppdatert: 2018-10-16

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