The Visual Object Tracking VOT2014 Challenge Results

Matej Kristan; Roman P Pflugfelder; Ales Leonardis; Jiri Matas; Luka Cehovin; Georg Nebehay; Tomas Vojir; Gustavo Fernandez; Alan Lukezi; Aleksandar Dimitriev; Alfredo Petrosino; Amir Saffari; Bo Li; Bohyung Han; CherKeng Heng; Christophe Garcia; Dominik Pangersic; Gustav Häger; Fahad Shahbaz Khan; Franci Oven; Horst Possegger; Horst Bischof; Hyeonseob Nam; Jianke Zhu; JiJia Li; Jin Young Choi; Jin-Woo Choi; Joao F Henriques; Joost van de Weijer; Jorge Batista; Karel Lebeda; Kristoffer Ofjall; Kwang Moo Yi; Lei Qin; Longyin Wen; Mario Edoardo Maresca; Martin Danelljan; Michael Felsberg; Ming-Ming Cheng; Philip Torr; Qingming Huang; Richard Bowden; Sam Hare; Samantha YueYing Lim; Seunghoon Hong; Shengcai Liao; Simon Hadfield; Stan Z Li; Stefan Duffner; Stuart Golodetz; Thomas Mauthner; Vibhav Vineet; Weiyao Lin; Yang Li; Yuankai Qi; Zhen Lei; ZhiHeng Niu

doi:10.1007/978-3-319-16181-5_14

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The Visual Object Tracking VOT2014 Challenge Results

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The Visual Object Tracking VOT2014 Challenge Results

Matej Kristan, Roman P Pflugfelder, Ales Leonardis, Jiri Matas, Luka Cehovin, Georg Nebehay, Tomas Vojir, Gustavo Fernandez, Alan Lukezi, Aleksandar Dimitriev, …

COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Vol.8926, pp.191-217

Lecture Notes in Computer Science

2015

DOI: https://doi.org/10.1007/978-3-319-16181-5_14

Abstract

Computer Vision and Robotics (Autonomous Systems)

Data- och informationsvetenskap

Naturvetenskap

Natural Sciences

Computer and Information Sciences

Datorseende och robotik (autonoma system)

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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Title: The Visual Object Tracking VOT2014 Challenge Results
Creators: Matej Kristan - University of Ljubljana, Ljubljana, Slovenia
Roman P Pflugfelder - Austrian Institute of Technology, Vienna, Austria
Ales Leonardis - University of Birmingham, Birmingham, UK
Jiri Matas - Czech Technical University, Prague, Czech Republic
Luka Cehovin - University of Ljubljana, Ljubljana, Slovenia
Georg Nebehay - Austrian Institute of Technology, Vienna, Austria
Tomas Vojir - Czech Technical University, Prague, Czech Republic
Gustavo Fernandez - Austrian Institute of Technology, Vienna, Austria
Alan Lukezi - University of Ljubljana, Ljubljana, Slovenia
Aleksandar Dimitriev - University of Ljubljana, Ljubljana, Slovenia
Alfredo Petrosino - Parthenope University of Naples, Naples, Italy
Amir Saffari - Affectv Limited, London, UK
Bo Li - Panasonic R&D Center, Singapore, Singapore
Bohyung Han - POSTECH, Pohang, Korea
CherKeng Heng - Panasonic R&D Center, Singapore, Singapore
Christophe Garcia - LIRIS, Lyon, France
Dominik Pangersic - University of Ljubljana, Ljubljana, Slovenia
Gustav Häger - Tekniska fakulteten
Fahad Shahbaz Khan - Tekniska fakulteten
Franci Oven - University of Ljubljana, Ljubljana, Slovenia
Horst Possegger - Graz University of Technology, Graz, Austria
Horst Bischof - Graz University of Technology, Graz, Austria
Hyeonseob Nam - POSTECH, Pohang, Korea
Jianke Zhu - Zhejiang University, Hangzhou, China
JiJia Li - Shanghai Jiao Tong University, Shanghai, China
Jin Young Choi - ASRI Seoul National University, Gwanak, Korea
Jin-Woo Choi - Electronics and Telecommunications Research Institute, Daejeon, Korea
Joao F Henriques - University of Coimbra, Coimbra, Portugal
Joost van de Weijer - Universitat Autonoma de Barcelona, Barcelona, Spain
Jorge Batista - University of Coimbra, Coimbra, Portugal
Karel Lebeda - University of Surrey, Surrey, UK
Kristoffer Ofjall - Tekniska fakulteten
Kwang Moo Yi - EPFL CVLab, Lausanne, Switzerland
Lei Qin - ICT CAS, Beijing, China
Longyin Wen - Chinese Academy of Sciences, Beijing, China
Mario Edoardo Maresca - Parthenope University of Naples, Naples, Italy
Martin Danelljan - Tekniska fakulteten
Michael Felsberg - Tekniska fakulteten
Ming-Ming Cheng - University of Oxford, Oxford, UK
Philip Torr - University of Oxford, Oxford, UK
Qingming Huang - Harbin Institute of Technology, Harbin, China
Richard Bowden - University of Surrey, Surrey, UK
Sam Hare - Obvious Engineering Limited, London, UK
Samantha YueYing Lim - Panasonic R&D Center, Singapore, Singapore
Seunghoon Hong - POSTECH, Pohang, Korea
Shengcai Liao - Chinese Academy of Sciences, Beijing, China
Simon Hadfield - University of Surrey, Surrey, UK
Stan Z Li - Chinese Academy of Sciences, Beijing, China
Stefan Duffner - LIRIS, Lyon, France
Stuart Golodetz - University of Oxford, Oxford, UK
Thomas Mauthner - Graz University of Technology, Graz, Austria
Vibhav Vineet - University of Oxford, Oxford, UK
Weiyao Lin - Shanghai Jiao Tong University, Shanghai, China
Yang Li - Zhejiang University, Hangzhou, China
Yuankai Qi - Harbin Institute of Technology, Harbin, China
Zhen Lei - Chinese Academy of Sciences, Beijing, China
ZhiHeng Niu - Panasonic R&D Center, Singapore, Singapore
Publication Details: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Vol.8926, pp.191-217
Series: Lecture Notes in Computer Science
Date published: 2015
Identifiers: 99586574002346
Academic Unit: School of Computer Science and Electronic Engineering
Language: English
Resource Type: Other

The Visual Object Tracking VOT2014 Challenge Results

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