The seventh visual object tracking VOT2019 challenge results

Matej Kristan, Jirí Matas, Ales Leonardis, Michael Felsberg, Roman Pflugfelder, Joni Kristian Kämäräinen, Luka Cehovin Zajc, Ondrej Drbohlav, Alan Lukezic, Amanda Berg, Abdelrahman Eldesokey, Jani Kapyla, Gustavo Fernández, Abel Gonzalez-Garcia, Alireza Memarmoghadam, Andong Lu, Anfeng He, Anton Varfolomieiev, Antoni Chan, Ardhendu Shekhar TripathiArnold Smeulders, Bala Suraj Pedasingu, Bao Xin Chen, Baopeng Zhang, B. Baoyuanwu, Bi Li, Bin He, Bin Yan, Bing Bai, Bing Li, Bo Li, Byeong Hak Kim, Chao Ma, Chen Fang, Chen Qian, Cheng Chen, Chenglong Li, Chengquan Zhang, Chi Yi Tsai, Chong Luo, Christian Micheloni, Chunhui Zhang, Jie Chen, Jing Li, Li Zhang, Ming Tang, Pengfei Zhang, Qi Zhang Qiang Wang, Yang Liu, Yi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

313 Scopus citations

Abstract

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 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 as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on 'real-time' shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. 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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2206-2241
Number of pages36
ISBN (Electronic)9781728150239
DOIs
StatePublished - Oct 2019
Event17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of
Duration: Oct 27 2019Oct 28 2019

Publication series

NameProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

Conference

Conference17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
Country/TerritoryKorea, Republic of
CitySeoul
Period10/27/1910/28/19

Bibliographical note

Funding Information:
This work was supported in part by the following research programs and projects: Slovenian research agency research programs P2-0214, P2-0094, Slovenian research agency project J2-8175. Jiˇi Matas and Ondrej Drbohlav were supported by the Czech Science Foundation Project GACR P103/12/G084. Alesˇ Leonardis was supported by MURI project financed by MoD/Dstl and EPSRC through EP/N019415/1 grant. Michael Felsberg, Amanda Berg, and Abdelrahman Eldesokey were supported by WASP, VR (ELLIIT, LÄST, and NCNN), and SSF (SymbiCloud). Roman Pflugfelder and Gustavo Fernández were supported by the AIT Strategic Research Programme 2019 Visual Surveillance and Insight. The challenge was sponsored by the Faculty of Computer Science, University of Ljubljana, Slovenia.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Object tracking
  • Performance evaluation
  • VOT challenge

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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