The Eighth Visual Object Tracking VOT2020 Challenge Results

被引:180
作者
Kristan, Matej [1 ]
Leonardis, Ales [2 ]
Matas, Jiri [3 ]
Felsberg, Michael [4 ]
Pflugfelder, Roman [5 ,6 ]
Kamarainen, Joni-Kristian [7 ]
Danelljan, Martin [8 ]
Zajc, Luka Cehovin [1 ]
Lukezic, Alan [1 ]
Drbohlav, Ondrej [3 ]
He, Linbo [4 ]
Zhang, Yushan [4 ,9 ]
Yan, Song [7 ]
Yang, Jinyu [2 ]
Fernandez, Gustavo [5 ]
Hauptmann, Alexander [10 ]
Memarmoghadam, Alireza [39 ]
Garcia-Martin, Alvaro [36 ]
Robinson, Andreas [4 ]
Varfolomieiev, Anton [25 ]
Haileslassie Gebrehiwot, Awet [36 ]
Uzun, Bedirhan [12 ]
Yan, Bin [11 ]
Li, Bing [18 ]
Qian, Chen [29 ]
Tsai, Chi-Yi [35 ]
Micheloni, Christian [43 ]
Wang, Dong [11 ]
Wang, Fei [29 ]
Xie, Fei [33 ]
Lawin, Felix Jaremo [4 ]
Gustafsson, Fredrik [44 ]
Foresti, Gian Luca [43 ]
Bhat, Goutam [8 ]
Chen, Guangqi [29 ]
Ling, Haibin [34 ]
Zhang, Haitao [46 ]
Cevikalp, Hakan [12 ]
Zhao, Haojie [11 ]
Bai, Haoran [32 ]
Kuchibhotla, Hari Chandana [17 ]
Saribas, Hasan [13 ]
Fan, Heng [34 ]
Ghanei-Yakhdan, Hossein [45 ]
Li, Houqiang [41 ]
Peng, Houwen [23 ]
Lu, Huchuan [11 ]
Li, Hui [19 ]
Khaghani, Javad [37 ]
Bescos, Jesus [36 ]
机构
[1] Univ Ljubljana, Ljubljana, Slovenia
[2] Univ Birmingham, Birmingham, W Midlands, England
[3] Czech Tech Univ, Prague, Czech Republic
[4] Linkoping Univ, Linkoping, Sweden
[5] Austrian Inst Technol, Seibersdorf, Austria
[6] TU Wien, Vienna, Austria
[7] Tampere Univ, Tampere, Finland
[8] Swiss Fed Inst Technol, Zurich, Switzerland
[9] Beijing Inst Technol, Beijing, Peoples R China
[10] Carnegie Mellon Univ, Pittsburgh, PA USA
[11] Dalian Univ Technol, Dalian, Peoples R China
[12] Eskisehir Osmangazi Univ, Eskisehir, Turkey
[13] Eskisehir Tech Univ, Eskisehir, Turkey
[14] Five AI, London, England
[15] Fuzhou Univ, Fuzhou, Peoples R China
[16] Renmin Univ China, High Sch, Beijing, Peoples R China
[17] Indian Inst Technol, Tirupati, Andhra Pradesh, India
[18] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
[19] Jiangnan Univ, Wuxi, Jiangsu, Peoples R China
[20] Korea Adv Inst Sci & Technol, Daejeon, South Korea
[21] Korea Univ, Seoul, South Korea
[22] Megvii, Beijing, Peoples R China
[23] Microsoft Res, Redmond, WA USA
[24] Nanjing Univ Informat Sci & Technol, Nanjing, Peoples R China
[25] Natl Tech Univ Ukraine, Kiev, Ukraine
[26] NLP, Beijing, Peoples R China
[27] Remark Holdings, London, England
[28] Samsung Res China Beijing, Beijing, Peoples R China
[29] Sensetime, Hong Kong, Peoples R China
[30] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai, Peoples R China
[31] Sharif Univ Technol, Tehran, Iran
[32] Sichuan Univ, Chengdu, Peoples R China
[33] Southeast Univ, Nanjing, Peoples R China
[34] SUNY Stony Brook, Stony Brook, NY USA
[35] Tamkang Univ, New Taipei, Taiwan
[36] Univ Autonoma Madrid, Madrid, Spain
[37] Univ Alberta, Edmonton, AB, Canada
[38] Univ Elect Sci & Technol China, Chengdu, Peoples R China
[39] Univ Isfahan, Esfahan, Iran
[40] Univ Oxford, Oxford, England
[41] Univ Sci & Technol China, Hefei, Peoples R China
[42] Univ Surrey, Guildford, Surrey, England
[43] Univ Udine, Udine, Italy
[44] Uppsala Univ, Uppsala, Sweden
[45] Yazd Univ, Yazd, Iran
[46] Zhejiang Dahua Technol, Binjiang, Peoples R China
来源
COMPUTER VISION - ECCV 2020 WORKSHOPS, PT V | 2020年 / 12539卷
基金
英国工程与自然科学研究理事会;
关键词
Visual object tracking; Performance evaluation protocol; State-of-the-art benchmark; RGB; RGBD; Depth; RGBT; Thermal imagery; Short-term trackers; Long-term trackers;
D O I
10.1007/978-3-030-68238-5_39
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge - bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. 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).
引用
收藏
页码:547 / 601
页数:55
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