Zajc, Luka Cehovin
Khan, Fahad Shahbaz
Subrahmanyam, Gorthi R. K. Sai
Henriques, Joao F.
Martinez, Jose M.
Kitani, Kris M.
M, Pallavi Venugopal
Torr, Philip H. S.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2018-01-22
Print Publication Date2017-10
Permanent link to this recordhttp://hdl.handle.net/10754/630412
MetadataShow full item record
AbstractThe 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 website1.
CitationKristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, et al. (2017) The Visual Object Tracking VOT2017 Challenge Results. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). Available: http://dx.doi.org/10.1109/ICCVW.2017.230.
SponsorsThis 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. Jifi Matas and Tomáš Vojíř were supported by the Czech Science Foundation Project GACR P103/12/G084. Michael Felsberg and Gustav Häger were supported by WASP, VR (EMC2), SSF (SymbiCloud), and SNIC. Gustavo Fernández and Roman Pflugfelder were supported by the AIT Strategic Research Programme 2017 Visual Surveillance and Insight. The challenge was sponsored by Faculty of Computer Science, University of Ljubljana, Slovenia.
Conference/Event name16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
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