The thermal infrared visual object tracking VOT-TIR2016 challenge results

Michael Felsberg; Matej Kristan; Jiři Matas; Aleš Leonardis; Roman Pflugfelder; Gustav Häger; Amanda Berg; Abdelrahman Eldesokey; Jörgen Ahlberg; Luka Čehovin; Tomáš Vojír; Alan Lukežič; Gustavo Fernández; Alfredo Petrosino; Alvaro Garcia Martin; Andrés Solís Montero; Anton Varfolomieiev; Aykut Erdem; Bohyung Han; Chang Ming Chang; Dawei Du; Erkut Erdem; Fahad Shahbaz Khan; Fatih Porikli; Fei Zhao; Filiz Bunyak; Francesco Battistone; Gao Zhu; Guna Seetharaman; Hongdong Li; Honggang Qi; Horst Bischof; Horst Possegger; Hyeonseob Nam; Jack Valmadre; Jianke Zhu; Jiayi Feng; Jochen Lang; Jose M. Martinez; Kannappan Palaniappan; Karel Lebeda; Ke Gao; Krystian Mikolajczyk; Longyin Wen; Luca Bertinetto; Mahdieh Poostchi; Mario Maresca; Martin Danelljan; Ming Tang; Yang Li

doi:10.1007/978-3-319-48881-3_55

The thermal infrared visual object tracking VOT-TIR2016 challenge results

Michael Felsberg^*, Matej Kristan, Jiři Matas, Aleš Leonardis, Roman Pflugfelder, Gustav Häger, Amanda Berg, Abdelrahman Eldesokey, Jörgen Ahlberg, Luka Čehovin, Tomáš Vojír, Alan Lukežič, Gustavo Fernández, Alfredo Petrosino, Alvaro Garcia Martin, Andrés Solís Montero, Anton Varfolomieiev, Aykut Erdem, Bohyung Han, Chang Ming ChangDawei Du, Erkut Erdem, Fahad Shahbaz Khan, Fatih Porikli, Fei Zhao, Filiz Bunyak, Francesco Battistone, Gao Zhu, Guna Seetharaman, Hongdong Li, Honggang Qi, Horst Bischof, Horst Possegger, Hyeonseob Nam, Jack Valmadre, Jianke Zhu, Jiayi Feng, Jochen Lang, Jose M. Martinez, Kannappan Palaniappan, Karel Lebeda, Ke Gao, Krystian Mikolajczyk, Longyin Wen, Luca Bertinetto, Mahdieh Poostchi, Mario Maresca, Martin Danelljan, Ming Tang, Yang Li

^*Corresponding author for this work

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

54 Scopus citations

Abstract

The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

Original language	English (US)
Title of host publication	Computer Vision – ECCV 2016 Workshops, Proceedings
Editors	Gang Hua, Herve Jegou
Publisher	Springer Verlag
Pages	824-849
Number of pages	26
ISBN (Print)	9783319488806
DOIs	https://doi.org/10.1007/978-3-319-48881-3_55
State	Published - 2016
Event	Computer Vision - ECCV 2016 Workshops, Proceedings - Amsterdam, Netherlands Duration: Oct 8 2016 → Oct 16 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9914 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Computer Vision - ECCV 2016 Workshops, Proceedings
Country/Territory	Netherlands
City	Amsterdam
Period	10/8/16 → 10/16/16

Keywords

Object tracking
Performance evaluation
Thermal IR
VOT

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-48881-3_55

Cite this

Felsberg, M., Kristan, M., Matas, J., Leonardis, A., Pflugfelder, R., Häger, G., Berg, A., Eldesokey, A., Ahlberg, J., Čehovin, L., Vojír, T., Lukežič, A., Fernández, G., Petrosino, A., Martin, A. G., Montero, A. S., Varfolomieiev, A., Erdem, A., Han, B., ... Li, Y. (2016). The thermal infrared visual object tracking VOT-TIR2016 challenge results. In G. Hua, & H. Jegou (Eds.), Computer Vision – ECCV 2016 Workshops, Proceedings (pp. 824-849). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9914 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-48881-3_55

Felsberg, Michael ; Kristan, Matej ; Matas, Jiři et al. / The thermal infrared visual object tracking VOT-TIR2016 challenge results. Computer Vision – ECCV 2016 Workshops, Proceedings. editor / Gang Hua ; Herve Jegou. Springer Verlag, 2016. pp. 824-849 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4bdb9060ed4b4db098cf7f6446538acf,

title = "The thermal infrared visual object tracking VOT-TIR2016 challenge results",

abstract = "The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.",

keywords = "Object tracking, Performance evaluation, Thermal IR, VOT",

author = "Michael Felsberg and Matej Kristan and Ji{\v r}i Matas and Ale{\v s} Leonardis and Roman Pflugfelder and Gustav H{\"a}ger and Amanda Berg and Abdelrahman Eldesokey and J{\"o}rgen Ahlberg and Luka {\v C}ehovin and Tom{\'a}{\v s} Voj{\'i}r and Alan Luke{\v z}i{\v c} and Gustavo Fern{\'a}ndez and Alfredo Petrosino and Martin, {Alvaro Garcia} and Montero, {Andr{\'e}s Sol{\'i}s} and Anton Varfolomieiev and Aykut Erdem and Bohyung Han and Chang, {Chang Ming} and Dawei Du and Erkut Erdem and Khan, {Fahad Shahbaz} and Fatih Porikli and Fei Zhao and Filiz Bunyak and Francesco Battistone and Gao Zhu and Guna Seetharaman and Hongdong Li and Honggang Qi and Horst Bischof and Horst Possegger and Hyeonseob Nam and Jack Valmadre and Jianke Zhu and Jiayi Feng and Jochen Lang and Martinez, {Jose M.} and Kannappan Palaniappan and Karel Lebeda and Ke Gao and Krystian Mikolajczyk and Longyin Wen and Luca Bertinetto and Mahdieh Poostchi and Mario Maresca and Martin Danelljan and Ming Tang and Yang Li",

note = "Funding Information: This work was supported in part by the following research programs and projects: Slovenian research agency research programs P2-0214, P2-0094, Slovenian research agency projects J2-4284, J2-3607, J2-2221 and European Union 7th Framework Programme under grant agreement 257906. J. Matas and T. Vojir were supported by CTU Project SGS13/142/OHK3/2T/13 and by the Technology Agency of the Czech Republic project TE01020415 (V3C – Visual Computing Competence Center). M. Felsberg, G. H{\"a}ger, and A. Eldesokey were supported by the Wallenberg Autonomous Systems Program WASP, the Swedish Foundation for Strategic Research through the project CUAS, and the Swedish Research Council trough the project . J. Ahlberg and A. Berg were supported by the European Union 7th Framework Programme under grant agreement 312784 (P5) and the Swedish Research Council through the contract D0570301. Some experiments where run on GPUs donated by NVIDIA. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; Computer Vision - ECCV 2016 Workshops, Proceedings ; Conference date: 08-10-2016 Through 16-10-2016",

year = "2016",

doi = "10.1007/978-3-319-48881-3_55",

language = "English (US)",

isbn = "9783319488806",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "824--849",

editor = "Gang Hua and Herve Jegou",

booktitle = "Computer Vision – ECCV 2016 Workshops, Proceedings",

address = "Germany",

}

Felsberg, M, Kristan, M, Matas, J, Leonardis, A, Pflugfelder, R, Häger, G, Berg, A, Eldesokey, A, Ahlberg, J, Čehovin, L, Vojír, T, Lukežič, A, Fernández, G, Petrosino, A, Martin, AG, Montero, AS, Varfolomieiev, A, Erdem, A, Han, B, Chang, CM, Du, D, Erdem, E, Khan, FS, Porikli, F, Zhao, F, Bunyak, F, Battistone, F, Zhu, G, Seetharaman, G, Li, H, Qi, H, Bischof, H, Possegger, H, Nam, H, Valmadre, J, Zhu, J, Feng, J, Lang, J, Martinez, JM, Palaniappan, K, Lebeda, K, Gao, K, Mikolajczyk, K, Wen, L, Bertinetto, L, Poostchi, M, Maresca, M, Danelljan, M, Tang, M & Li, Y 2016, The thermal infrared visual object tracking VOT-TIR2016 challenge results. in G Hua & H Jegou (eds), Computer Vision – ECCV 2016 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9914 LNCS, Springer Verlag, pp. 824-849, Computer Vision - ECCV 2016 Workshops, Proceedings, Amsterdam, Netherlands, 10/8/16. https://doi.org/10.1007/978-3-319-48881-3_55

The thermal infrared visual object tracking VOT-TIR2016 challenge results. / Felsberg, Michael; Kristan, Matej; Matas, Jiři et al.
Computer Vision – ECCV 2016 Workshops, Proceedings. ed. / Gang Hua; Herve Jegou. Springer Verlag, 2016. p. 824-849 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9914 LNCS).

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

TY - GEN

T1 - The thermal infrared visual object tracking VOT-TIR2016 challenge results

AU - Felsberg, Michael

AU - Kristan, Matej

AU - Matas, Jiři

AU - Leonardis, Aleš

AU - Pflugfelder, Roman

AU - Häger, Gustav

AU - Berg, Amanda

AU - Eldesokey, Abdelrahman

AU - Ahlberg, Jörgen

AU - Čehovin, Luka

AU - Vojír, Tomáš

AU - Lukežič, Alan

AU - Fernández, Gustavo

AU - Petrosino, Alfredo

AU - Martin, Alvaro Garcia

AU - Montero, Andrés Solís

AU - Varfolomieiev, Anton

AU - Erdem, Aykut

AU - Han, Bohyung

AU - Chang, Chang Ming

AU - Du, Dawei

AU - Erdem, Erkut

AU - Khan, Fahad Shahbaz

AU - Porikli, Fatih

AU - Zhao, Fei

AU - Bunyak, Filiz

AU - Battistone, Francesco

AU - Zhu, Gao

AU - Seetharaman, Guna

AU - Li, Hongdong

AU - Qi, Honggang

AU - Bischof, Horst

AU - Possegger, Horst

AU - Nam, Hyeonseob

AU - Valmadre, Jack

AU - Zhu, Jianke

AU - Feng, Jiayi

AU - Lang, Jochen

AU - Martinez, Jose M.

AU - Palaniappan, Kannappan

AU - Lebeda, Karel

AU - Gao, Ke

AU - Mikolajczyk, Krystian

AU - Wen, Longyin

AU - Bertinetto, Luca

AU - Poostchi, Mahdieh

AU - Maresca, Mario

AU - Danelljan, Martin

AU - Tang, Ming

AU - Li, Yang

N1 - Funding Information: This work was supported in part by the following research programs and projects: Slovenian research agency research programs P2-0214, P2-0094, Slovenian research agency projects J2-4284, J2-3607, J2-2221 and European Union 7th Framework Programme under grant agreement 257906. J. Matas and T. Vojir were supported by CTU Project SGS13/142/OHK3/2T/13 and by the Technology Agency of the Czech Republic project TE01020415 (V3C – Visual Computing Competence Center). M. Felsberg, G. Häger, and A. Eldesokey were supported by the Wallenberg Autonomous Systems Program WASP, the Swedish Foundation for Strategic Research through the project CUAS, and the Swedish Research Council trough the project . J. Ahlberg and A. Berg were supported by the European Union 7th Framework Programme under grant agreement 312784 (P5) and the Swedish Research Council through the contract D0570301. Some experiments where run on GPUs donated by NVIDIA. Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

AB - The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

KW - Object tracking

KW - Performance evaluation

KW - Thermal IR

KW - VOT

UR - http://www.scopus.com/inward/record.url?scp=84996605652&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-48881-3_55

DO - 10.1007/978-3-319-48881-3_55

M3 - Conference contribution

AN - SCOPUS:84996605652

SN - 9783319488806

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 824

EP - 849

BT - Computer Vision – ECCV 2016 Workshops, Proceedings

A2 - Hua, Gang

A2 - Jegou, Herve

PB - Springer Verlag

T2 - Computer Vision - ECCV 2016 Workshops, Proceedings

Y2 - 8 October 2016 through 16 October 2016

ER -

Felsberg M, Kristan M, Matas J, Leonardis A, Pflugfelder R, Häger G et al. The thermal infrared visual object tracking VOT-TIR2016 challenge results. In Hua G, Jegou H, editors, Computer Vision – ECCV 2016 Workshops, Proceedings. Springer Verlag. 2016. p. 824-849. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-48881-3_55

The thermal infrared visual object tracking VOT-TIR2016 challenge results

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