The visual object tracking VOT2014 challenge results

Matej Kristan; Roman Pflugfelder; Ales Leonardis; Jiri Matas; Luka Čehovin; Georg Nebehay; Tomáš Vojíř; Gustavo Fernández; Alan Lukežič; Aleksandar Dimitriev; Alfredo Petrosino; Amir Saffari; Bo Li; Bohyung Han; Cher Keng Heng; Christophe Garcia; Dominik Pangeršič; Gustav Häger; Fahad Shahbaz Khan; Franci Oven; Horst Possegger; Horst Bischof; Hyeonseob Nam; Jianke Zhu; Ji Jia Li; Jin Young Choi; Jin Woo Choi; João F. Henriques; Joost van de Weijer; Jorge Batista; Karel Lebeda; Kristoffer Öfjäll; 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 Yue Ying 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; Zhi Heng Niu

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

The visual object tracking VOT2014 challenge results

Matej Kristan^*, Roman Pflugfelder, Ales Leonardis, Jiri Matas, Luka Čehovin, Georg Nebehay, Tomáš Vojíř, Gustavo Fernández, Alan Lukežič, Aleksandar Dimitriev, Alfredo Petrosino, Amir Saffari, Bo Li, Bohyung Han, Cher Keng Heng, Christophe Garcia, Dominik Pangeršič, Gustav Häger, Fahad Shahbaz Khan, Franci OvenHorst Possegger, Horst Bischof, Hyeonseob Nam, Jianke Zhu, Ji Jia Li, Jin Young Choi, Jin Woo Choi, João F. Henriques, Joost van de Weijer, Jorge Batista, Karel Lebeda, Kristoffer Öfjäll, 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 Yue Ying 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, Zhi Heng Niu

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

130 Citations (Scopus)

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 (http://votchallenge.net).

Original language	English
Title of host publication	Computer Vision - ECCV 2014 Workshops
Subtitle of host publication	Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II
Editors	Lourdes Agapito, Michael M. Bronstein, Carsten Rother
Publisher	Springer
Pages	191-217
Number of pages	27
Volume	8926
ISBN (Electronic)	9783319161815
ISBN (Print)	9783319161808
DOIs	https://doi.org/10.1007/978-3-319-16181-5_14
Publication status	Published - 2015
Event	13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sept 2014 → 12 Sept 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8926
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	13th European Conference on Computer Vision, ECCV 2014
Country/Territory	Switzerland
City	Zurich
Period	6/09/14 → 12/09/14

Keywords

Performance evaluation
Short-term single-object trackers
VOT

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Access to Document

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

Cite this

Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Čehovin, L., Nebehay, G., Vojíř, T., Fernández, G., Lukežič, A., Dimitriev, A., Petrosino, A., Saffari, A., Li, B., Han, B., Heng, C. K., Garcia, C., Pangeršič, D., Häger, G., Khan, F. S., ... Niu, Z. H. (2015). The visual object tracking VOT2014 challenge results. In L. Agapito, M. M. Bronstein, & C. Rother (Eds.), Computer Vision - ECCV 2014 Workshops : Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II (Vol. 8926, pp. 191-217). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8926). Springer. https://doi.org/10.1007/978-3-319-16181-5_14

Kristan, Matej ; Pflugfelder, Roman ; Leonardis, Ales et al. / The visual object tracking VOT2014 challenge results. Computer Vision - ECCV 2014 Workshops : Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II. editor / Lourdes Agapito ; Michael M. Bronstein ; Carsten Rother . Vol. 8926 Springer, 2015. pp. 191-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 (http://votchallenge.net).",

keywords = "Performance evaluation, Short-term single-object trackers, VOT",

author = "Matej Kristan and Roman Pflugfelder and Ales Leonardis and Jiri Matas and Luka {\v C}ehovin and Georg Nebehay and Tom{\'a}{\v s} Voj{\'i}{\v r} and Gustavo Fern{\'a}ndez and Alan Luke{\v z}i{\v c} and Aleksandar Dimitriev and Alfredo Petrosino and Amir Saffari and Bo Li and Bohyung Han and Heng, {Cher Keng} and Christophe Garcia and Dominik Panger{\v s}i{\v c} and Gustav H{\"a}ger and Khan, {Fahad Shahbaz} and Franci Oven and Horst Possegger and Horst Bischof and Hyeonseob Nam and Jianke Zhu and Li, {Ji Jia} and Choi, {Jin Young} and Choi, {Jin Woo} and Henriques, {Jo{\~a}o F.} and {van de Weijer}, Joost and Jorge Batista and Karel Lebeda and Kristoffer {\"O}fj{\"a}ll and Yi, {Kwang Moo} and Lei Qin and Longyin Wen and Maresca, {Mario Edoardo} and Martin Danelljan and Michael Felsberg and Cheng, {Ming Ming} and Philip Torr and Qingming Huang and Richard Bowden and Sam Hare and Lim, {Samantha Yue Ying} and Seunghoon Hong and Shengcai Liao and Simon Hadfield and Li, {Stan Z.} 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 Niu, {Zhi Heng}",

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editor = "Agapito, {Lourdes } and { Bronstein}, {Michael M. } and {Rother }, {Carsten }",

booktitle = "Computer Vision - ECCV 2014 Workshops",

note = "13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",

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Kristan, M, Pflugfelder, R, Leonardis, A, Matas, J, Čehovin, L, Nebehay, G, Vojíř, T, Fernández, G, Lukežič, A, Dimitriev, A, Petrosino, A, Saffari, A, Li, B, Han, B, Heng, CK, Garcia, C, Pangeršič, D, Häger, G, Khan, FS, Oven, F, Possegger, H, Bischof, H, Nam, H, Zhu, J, Li, JJ, Choi, JY, Choi, JW, Henriques, JF, van de Weijer, J, Batista, J, Lebeda, K, Öfjäll, K, Yi, KM, Qin, L, Wen, L, Maresca, ME, Danelljan, M, Felsberg, M, Cheng, MM, Torr, P, Huang, Q, Bowden, R, Hare, S, Lim, SYY, Hong, S, Liao, S, Hadfield, S, Li, SZ, Duffner, S, Golodetz, S, Mauthner, T, Vineet, V, Lin, W, Li, Y, Qi, Y, Lei, Z & Niu, ZH 2015, The visual object tracking VOT2014 challenge results. in L Agapito, MM Bronstein & C Rother (eds), Computer Vision - ECCV 2014 Workshops : Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II. vol. 8926, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8926, Springer, pp. 191-217, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6/09/14. https://doi.org/10.1007/978-3-319-16181-5_14

The visual object tracking VOT2014 challenge results. / Kristan, Matej; Pflugfelder, Roman; Leonardis, Ales et al.
Computer Vision - ECCV 2014 Workshops : Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II. ed. / Lourdes Agapito; Michael M. Bronstein; Carsten Rother . Vol. 8926 Springer, 2015. p. 191-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8926).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Kristan, Matej

AU - Pflugfelder, Roman

AU - Leonardis, Ales

AU - Matas, Jiri

AU - Čehovin, Luka

AU - Nebehay, Georg

AU - Vojíř, Tomáš

AU - Fernández, Gustavo

AU - Lukežič, Alan

AU - Dimitriev, Aleksandar

AU - Petrosino, Alfredo

AU - Saffari, Amir

AU - Li, Bo

AU - Han, Bohyung

AU - Heng, Cher Keng

AU - Garcia, Christophe

AU - Pangeršič, Dominik

AU - Häger, Gustav

AU - Khan, Fahad Shahbaz

AU - Oven, Franci

AU - Possegger, Horst

AU - Bischof, Horst

AU - Nam, Hyeonseob

AU - Zhu, Jianke

AU - Li, Ji Jia

AU - Choi, Jin Young

AU - Choi, Jin Woo

AU - Henriques, João F.

AU - van de Weijer, Joost

AU - Batista, Jorge

AU - Lebeda, Karel

AU - Öfjäll, Kristoffer

AU - Yi, Kwang Moo

AU - Qin, Lei

AU - Wen, Longyin

AU - Maresca, Mario Edoardo

AU - Danelljan, Martin

AU - Felsberg, Michael

AU - Cheng, Ming Ming

AU - Torr, Philip

AU - Huang, Qingming

AU - Bowden, Richard

AU - Hare, Sam

AU - Lim, Samantha Yue Ying

AU - Hong, Seunghoon

AU - Liao, Shengcai

AU - Hadfield, Simon

AU - Li, Stan Z.

AU - Duffner, Stefan

AU - Golodetz, Stuart

AU - Mauthner, Thomas

AU - Vineet, Vibhav

AU - Lin, Weiyao

AU - Li, Yang

AU - Qi, Yuankai

AU - Lei, Zhen

AU - Niu, Zhi Heng

PY - 2015

Y1 - 2015

N2 - 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).

AB - 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).

KW - Performance evaluation

KW - Short-term single-object trackers

KW - VOT

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DO - 10.1007/978-3-319-16181-5_14

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BT - Computer Vision - ECCV 2014 Workshops

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Kristan M, Pflugfelder R, Leonardis A, Matas J, Čehovin L, Nebehay G et al. The visual object tracking VOT2014 challenge results. In Agapito L, Bronstein MM, Rother C, editors, Computer Vision - ECCV 2014 Workshops : Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II. Vol. 8926. Springer. 2015. p. 191-217. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-16181-5_14

The visual object tracking VOT2014 challenge results

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this