The Visual Object Tracking VOT2015 Challenge Results

Type
Conference Paper

Authors
Kristan, Matej
Matas, Jiri
Leonardis, Ale
Felsberg, Michael
Cehovin, Luka
Fernandez, Gustavo
Vojir, Toma
Hager, Gustav
Nebehay, Georg
Pflugfelder, Roman
Gupta, Abhinav
Bibi, Adel
Lukezic, Alan
Garcia-Martin, Alvaro
Saffari, Amir
Petrosino, Alfredo
Montero, Andres Solıs
Varfolomieiev, Anton
Baskurt, Atilla
Zhao, Baojun
Ghanem, Bernard
Martinez, Brais
Lee, ByeongJu
Han, Bohyung
Wang, Chaohui
Garcia, Christophe
Zhang, Chunyuan
Schmid, Cordelia
Tao, Dacheng
Kim, Daijin
Huang, Dafei
Prokhorov, Danil
Du, Dawei
Yeung, Dit-Yan
Ribeiro, Eraldo
Khan, Fahad Shahbaz
Porikli, Fatih
Bunyak, Filiz
Zhu, Gao
Seetharaman, Guna
Kieritz, Hilke
Yau, Hing Tuen
Li, Hongdong
Qi, Honggang
Bischof, Horst
Possegger, Horst
Lee, Hyemin
Nam, Hyeonseob
Bogun, Ivan
Jeong, Jae-chan
Cho, Jae-il
Lee, Jae-Yeong
Zhu, Jianke
Shi, Jianping
Li, Jiatong
Jia, Jiaya
Feng, Jiayi
Gao, Jin
Choi, Jin Young
Kim, Ji-Wan
Lang, Jochen
Martinez, Jose M.
Choi, Jongwon
Xing, Junliang
Xue, Kai
Palaniappan, Kannappan
Lebeda, Karel
Alahari, Karteek
Gao, Ke
Yun, Kimin
Wong, Kin Hong
Luo, Lei
Ma, Liang
Ke, Lipeng
Wen, Longyin
Bertinetto, Luca
Pootschi, Mahdieh
Maresca, Mario
Danelljan, Martin
Wen, Mei
Zhang, Mengdan
Arens, Michael
Valstar, Michel
Tang, Ming
Chang, Ming-Ching
Khan, Muhammad Haris
Fan, Nana
Wang, Naiyan
Miksik, Ondrej
Torr, Philip H S
Wang, Qiang
Martin-Nieto, Rafael
Pelapur, Rengarajan
Bowden, Richard
Laganiere, Robert
Moujtahid, Salma
Hare, Sam
Hadfield, Simon
Lyu, Siwei
Li, Siyi
Zhu, Song-Chun
Becker, Stefan
Duffner, Stefan
Hicks, Stephen L
Golodetz, Stuart
Choi, Sunglok
Wu, Tianfu
Mauthner, Thomas
Pridmore, Tony
Hu, Weiming
Hubner, Wolfgang
Wang, Xiaomeng
Li, Xin
Shi, Xinchu
Zhao, Xu
Mei, Xue
Shizeng, Yao
Hua, Yang
Li, Yang
Lu, Yang
Li, Yuezun
Chen, Zhaoyun
Huang, Zehua
Chen, Zhe
Zhang, Zhe
He, Zhenyu
Hong, Zhibin

KAUST Department
Electrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date
2016-02-16

Print Publication Date
2015-12

Date
2016-02-16

Abstract
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.

Citation
The Visual Object Tracking VOT2015 Challenge Results. (2015). 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). doi:10.1109/iccvw.2015.79

Acknowledgements
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 projects J2-4284, J2-3607, J2-2221 and European Union seventh framework programme under grant agreement no 257906. Jiri Matas and Tomas Vojir were supported by CTU Project SGS13/142/OHK3/2T/13 and by the Technology Agency of the Czech Republic project TE01020415 (V3C – Visual Computing Competence Center). Michael Felsberg and Gustav H¨ager were supported by the Swedish Foundation for Strategic Research through the project CUAS and the Swedish Research Council trough the project EMC2. Some experiments where run on GPUs donated by NVIDIA.

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
2015 IEEE International Conference on Computer Vision Workshop (ICCVW)

Conference/Event Name
2015 IEEE International Conference on Computer Vision Workshop (ICCVW)

DOI
10.1109/ICCVW.2015.79

Additional Links
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7406428

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