The sixth visual object tracking VOT2018 challenge results

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Date

2019-01-01

Author

Kristan, Matej
Leonardis, Aleš
Matas, Jiří
Felsberg, Michael
Pflugfelder, Roman
Zajc, Luka Čehovin
Vojír̃, Tomáš
Bhat, Goutam
Lukežič, Alan
Eldesokey, Abdelrahman
Fernández, Gustavo
García-Martín, Álvaro
Iglesias-Arias, Álvaro
Alatan, Abdullah Aydın
González-García, Abel
Petrosino, Alfredo
Memarmoghadam, Alireza
Vedaldi, Andrea
Muhič, Andrej
He, Anfeng
Smeulders, Arnold
Perera, Asanka G.
Li, Bo
Chen, Boyu
Kim, Changick
Xu, Changsheng
Xiong, Changzhen
Tian, Cheng
Luo, Chong
Sun, Chong
Hao, Cong
Kim, Daijin
Mishra, Deepak
Chen, Deming
Wang, Dong
Wee, Dongyoon
Gavves, Efstratios
Gundogdu, Erhan
Velasco-Salido, Erik
Khan, Fahad Shahbaz
Yang, Fan
Zhao, Fei
Li, Feng
Battistone, Francesco
De Ath, George
Subrahmanyam, Gorthi R. K. S.
Bastos, Guilherme
Ling, Haibin
Galoogahi, Hamed Kiani
Lee, Hankyeol
Li, Haojie
Zhao, Haojie
Fan, Heng
Zhang, Honggang
Possegger, Horst
Li, Houqiang
Lu, Huchuan
Zhi, Hui
Li, Huiyun
Lee, Hyemin
Chang, Hyung Jin
Drummond, Isabela
Valmadre, Jack
Martin, Jaime Spencer
Chahl, Javaan
Choi, Jin Young
Li, Jing
Wang, Jinqiao
Qi, Jinqing
Sung, Jinyoung
Johnander, Joakim
Henriques, Joao
Choi, Jongwon
van de Weijer, Joost
Herranz, Jorge Rodríguez
Martínez, José M.
Kittler, Josef
Zhuang, Junfei
Gao, Junyu
Grm, Klemen
Zhang, Lichao
Wang, Lijun
Yang, Lingxiao
Rout, Litu
Si, Liu
Bertinetto, Luca
Chu, Lutao
Che, Manqiang
Maresca, Mario Edoardo
Danelljan, Martin
Yang, Ming-Hsuan
Abdelpakey, Mohamed
Shehata, Mohamed
Kang, Myunggu
Lee, Namhoon
Wang, Ning
Miksik, Ondrej
Moallem, P.
Vicente-Moñivar, Pablo
Senna, Pedro
Li, Peixia
Torr, Philip
Raju, Priya Mariam
Ruihe, Qian
Wang, Qiang
Zhou, Qin
Guo, Qing
Martín-Nieto, Rafael
Gorthi, Rama Krishna
Tao, Ran
Bowden, Richard
Everson, Richard
Wang, Runling
Yun, Sangdoo
Choi, Seokeon
Vivas, Sergio
Bai, Shuai
Huang, Shuangping
Wu, Sihang
Hadfield, Simon
Wang, Siwen
Golodetz, Stuart
Ming, Tang
Xu, Tianyang
Zhang, Tianzhu
Fischer, Tobias
Santopietro, Vincenzo
Štruc, Vitomir
Wei, Wang
Zuo, Wangmeng
Feng, Wei
Wu, Wei
Zou, Wei
Hu, Weiming
Zhou, Wengang
Zeng, Wenjun
Zhang, Xiaofan
Wu, Xiaohe
Wu, Xiao-Jun
Tian, Xinmei
Li, Yan
Lu, Yan
Law, Yee Wei
Wu, Yi
Demiris, Yiannis
Yang, Yicai
Jiao, Yifan
Li, Yuhong
Zhang, Yunhua
Sun, Yuxuan
Zhang, Zheng
Zhu, Zheng
Feng, Zhen-Hua
Wang, Zhihui
He, Zhiqun

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The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty 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 and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. 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 (http://votchallenge.net).

Subject Keywords

Tracking, Performance evaluation, Theoretical computer science, Computer science

URI

https://hdl.handle.net/11511/62227

DOI

https://doi.org/10.1007/978-3-030-11009-3_1

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

M. Kristan et al., “The sixth visual object tracking VOT2018 challenge results,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62227.