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The Visual Object Tracking VOT2014 Challenge Results
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  • 作者：Matej Kristan (16)
    Roman Pflugfelder (17)
    Ale拧 Leonardis (18)
    Jiri Matas (19)
    Luka 膶ehovin (16)
    Georg Nebehay (17)
    Tom谩拧 Voj铆艡 (19)
    Gustavo Fern谩ndez (17)
    Alan Luke啪i膷 (16)
    Aleksandar Dimitriev (16)
    Alfredo Petrosino (20)
    Amir Saffari (21)
    Bo Li (22)
    Bohyung Han (23)
    CherKeng Heng (22)
    Christophe Garcia (24)
    Dominik Panger拧i膷 (16)
    Gustav H盲ger (25)
    Fahad Shahbaz Khan (25)
    Franci Oven (16)
    Horst Possegger (26)
    Horst Bischof (26)
    Hyeonseob Nam (23)
    Jianke Zhu (27)
    JiJia Li (28)
    Jin Young Choi (29)
    Jin-Woo Choi (30)
    Jo茫o F. Henriques (31)
    Joost van de Weijer (32)
    Jorge Batista (31)
    Karel Lebeda (33)
    Kristoffer 脰fj盲ll (25)
    Kwang Moo Yi (34)
    Lei Qin (35)
    Longyin Wen (36)
    Mario Edoardo Maresca (20)
    Martin Danelljan (25)
    Michael Felsberg (25)
    Ming-Ming Cheng (37)
    Philip Torr (37)
    Qingming Huang (38)
    Richard Bowden (33)
    Sam Hare (39)
    Samantha YueYing Lim (22)
    Seunghoon Hong (23)
    Shengcai Liao (36)
    Simon Hadfield (33)
    Stan Z. Li (36)
    Stefan Duffner (24)
    Stuart Golodetz (37)
    Thomas Mauthner (26)
    Vibhav Vineet (37)
    Weiyao Lin (28)
    Yang Li (27)
    Yuankai Qi (38)
    Zhen Lei (36)
    Zhi Heng Niu (22)

    16. University of Ljubljana     ; Ljubljana ; Slovenia
    17. Austrian Institute of Technology     ; Vienna ; Austria
    18. University of Birmingham     ; Birmingham ; UK
    19. Czech Technical University     ; Prague ; Czech Republic
    20. Parthenope University of Naples     ; Naples ; Italy
    21. Affectv Limited     ; London ; UK
    22. Panasonic R&D Center     ; Singapore ; Singapore
    23. POSTECH     ; Pohang ; Korea
    24. LIRIS     ; Lyon ; France
    25. Link枚ping University     ; Link枚ping ; Sweden
    26. Graz University of Technology     ; Graz ; Austria
    27. Zhejiang University     ; Hangzhou ; China
    28. Shanghai Jiao Tong University     ; Shanghai ; China
    29. ASRI Seoul National University     ; Gwanak ; Korea
    30. Electronics and Telecommunications Research Institute     ; Daejeon ; Korea
    31. University of Coimbra     ; Coimbra ; Portugal
    32. Universitat Autonoma de Barcelona     ; Barcelona ; Spain
    33. University of Surrey     ; Surrey ; UK
    34. EPFL CVLab     ; Lausanne ; Switzerland
    35. ICT CAS     ; Beijing ; China
    36. Chinese Academy of Sciences     ; Beijing ; China
    37. University of Oxford     ; Oxford ; UK
    38. Harbin Institute of Technology     ; Harbin ; China
    39. Obvious Engineering Limited     ; London ; UK
  • 关键词：Performance evaluation ; Short ; term single ; object trackers ; VOT
  • 刊名：Lecture Notes in Computer Science
  • 出版年：2015
  • 出版时间：2015
  • 年：2015
  • 卷：8926
  • 期：1
  • 页码：191-217
  • 全文大小：472 KB
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  • 作者单位：Computer Vision - ECCV 2014 Workshops
  • 丛书名：978-3-319-16180-8
  • 刊物类别：Computer Science
  • 刊物主题：Artificial Intelligence and Robotics
    Computer Communication Networks
    Software Engineering
    Data Encryption
    Database Management
    Computation by Abstract Devices
    Algorithm Analysis and Problem Complexity
  • 出版者：Springer Berlin / Heidelberg
  • ISSN：1611-3349
文摘
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2014 challenge that go beyond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execution of experiments. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).

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