by Matej Kristan, Roman Pflugfelder, Ales Leonardis, Jiri Matas, Luka Cehovin, Georg Nebehay, Tomas Vojir, Gustavo Fernandez, Alan Lukezic, Aleksandar Dimitriev, Alfredo Petrosino, Amir Saffari, Bo Li, Bohyung Han, CherKeng Heng, Christophe Garcia, Dominik Pangersic, Gustav Hager, 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 and ZhiHeng Niu
Abstract:
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.
Reference:
The Visual Object Tracking VOT2014 challenge results (Matej Kristan, Roman Pflugfelder, Ales Leonardis, Jiri Matas, Luka Cehovin, Georg Nebehay, Tomas Vojir, Gustavo Fernandez, Alan Lukezic, Aleksandar Dimitriev, Alfredo Petrosino, Amir Saffari, Bo Li, Bohyung Han, CherKeng Heng, Christophe Garcia, Dominik Pangersic, Gustav Hager, 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 and ZhiHeng Niu), In Proceedings, European Conference on Computer Vision (ECCV) Visual Object Tracking Challenge Workshop (Agapito, Lourdes, Bronstein, Michael M., Rother, Carsten, eds.), Springer International Publishing, volume 8926, 2014.
Bibtex Entry:
@InProceedings{Hadfield14d,
Title = {The Visual Object Tracking VOT2014 challenge results},
Author = {Matej Kristan and Roman Pflugfelder and Ales Leonardis and Jiri Matas and Luka Cehovin and Georg Nebehay and Tomas Vojir and Gustavo Fernandez and Alan Lukezic and Aleksandar Dimitriev and Alfredo Petrosino and Amir Saffari and Bo Li and Bohyung Han and CherKeng Heng and Christophe Garcia and Dominik Pangersic and Gustav Hager and Fahad Shahbaz Khan and Franci Oven and Horst Possegger and Horst Bischof and Hyeonseob Nam and Jianke Zhu and JiJia Li and Jin Young Choi and Jin-Woo Choi and Joao F. Henriques and Joost van de Weijer and Jorge Batista and Karel Lebeda and Kristoffer Ofjall and Kwang Moo Yi and Lei Qin and Longyin Wen and Mario Edoardo Maresca and Martin Danelljan and Michael Felsberg and Ming-Ming Cheng and Philip Torr and Qingming Huang and Richard Bowden and Sam Hare and Samantha YueYing Lim and Seunghoon Hong and Shengcai Liao and Simon Hadfield and Stan Z. Li and Stefan Duffner and Stuart Golodetz and Thomas Mauthner and Vibhav Vineet and Weiyao Lin and Yang Li and Yuankai Qi and Zhen Lei and ZhiHeng Niu},
Booktitle = {Proceedings, European Conference on Computer Vision (ECCV) Visual Object Tracking Challenge Workshop},
Year = {2014},
Address = {Zurich, Switzerland},
Editor = {Agapito, Lourdes and Bronstein, Michael M. and Rother, Carsten},
Month = {September},
Pages = {191-217},
Publisher = {Springer International Publishing},
Series = {Lecture Notes in Computer Science},
Volume = {8926},
Abstract = {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.},
Crossref = {ECCV14},
Day = {6},
Doi = {10.1007/978-3-319-16181-5_14},
Gsid = {15633426501004735453},
ISBN = {978-3-319-16180-8},
gsid = {13408679558583258933,5030672482439802527,11385383912247455161},
Keywords = {Performance evaluation; Short-term single-object trackers; VOT},
Prestige = {international},
Status = {published},
Timestamp = {2015.01.01},
Url = {http://personalpages.surrey.ac.uk/s.hadfield/papers/The%20Visual%20Object%20Tracking%20VOT2014%20challenge%20results.pdf}
}