Type
Conference PaperAuthors
Kristan, MatejLeonardis, Ales
Matas, Jiri
Felsberg, Michael
Pflugfelder, Roman
Zajc, Luka Cehovin
Vojir, Tomas
Hager, Gustav
Lukezic, Alan
Eldesokey, Abdelrahman
Fernandez, Gustavo
Garcia-Martin, Alvaro
Muhic, A.
Petrosino, Alfredo
Memarmoghadam, Alireza
Vedaldi, Andrea
Manzanera, Antoine
Tran, Antoine
Alatan, Aydin
Mocanu, Bogdan
Chen, Boyu
Huang, Chang
Xu, Changsheng
Sun, Chong
Du, Dalong
Zhang, David
Du, Dawei
Mishra, Deepak
Gundogdu, Erhan
Velasco-Salido, Erik
Khan, Fahad Shahbaz
Battistone, Francesco
Subrahmanyam, Gorthi R. K. Sai
Bhat, Goutam
Huang, Guan
Bastos, Guilherme
Seetharaman, Guna
Zhang, Hongliang
Li, Houqiang
Lu, Huchuan
Drummond, Isabela
Valmadre, Jack
Jeong, Jae-chan
Cho, Jae-il
Lee, Jae-Yeong
Noskova, Jana
Zhu, Jianke
Gao, Jin
Liu, Jingyu
Kim, Ji-Wan
Henriques, Joao F.
Martinez, Jose M.
Zhuang, Junfei
Xing, Junliang
Gao, Junyu
Chen, Kai
Palaniappan, Kannappan
Lebeda, Karel
Gao, Ke
Kitani, Kris M.
Zhang, Lei
Wang, Lijun
Yang, Lingxiao
Wen, Longyin
Bertinetto, Luca
Poostchi, Mahdieh
Danelljan, Martin
Müller, Matthias

Zhang, Mengdan
Yang, Ming-Hsuan
Xie, Nianhao
Wang, Ning
Miksik, Ondrej
Moallem, P.
M, Pallavi Venugopal
Senna, Pedro
Torr, Philip H. S.
Wang, Qiang
Yu, Qifeng
Huang, Qingming
Martin-Nieto, Rafael
Bowden, Richard
Liu, Risheng
Tapu, Ruxandra
Hadfield, Simon
Lyu, Siwei
Golodetz, Stuart
Choi, Sunglok
Zhang, Tianzhu
Zaharia, Titus
Santopietro, Vincenzo
Zou, Wei
Hu, Weiming
Tao, Wenbing
Li, Wenbo
Zhou, Wengang
Yu, Xianguo
Bian, Xiao
Li, Yang
Xing, Yifan
Fan, Yingruo
Zhu, Zheng
Zhang, Zhipeng
He, Zhiqun
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2018-01-22Online Publication Date
2018-01-22Print Publication Date
2017-10Permanent link to this record
http://hdl.handle.net/10754/630412
Metadata
Show full item recordAbstract
The 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.Citation
Kristan 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.Sponsors
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. 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 name
16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017Additional Links
https://ieeexplore.ieee.org/document/8265440/ae974a485f413a2113503eed53cd6c53
10.1109/ICCVW.2017.230
Scopus Count
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