The Visual Object Tracking VOT2017 Challenge Results

Matej Kristan; Ales Leonardis; Jiri Matas; Michael Felsberg; Roman Pflugfelder; Luka Cehovin Zajc; Tomas Vojir; Gustav Hager; Alan Lukezic; Abdelrahman Eldesokey; Gustavo Fernandez; Alvaro Garcia-Martin; A. Muhic; Alfredo Petrosino; Alireza Memarmoghadam; Andrea Vedaldi; Antoine Manzanera; Antoine Tran; Aydin Alatan; Bogdan Mocanu; Boyu Chen; Chang Huang; Changsheng Xu; Chong Sun; Dalong Du; David Zhang; Dawei Du; Deepak Mishra; Erhan Gundogdu; Erik Velasco-Salido; Fahad Shahbaz Khan; Francesco Battistone; Gorthi R. K. Sai Subrahmanyam; Goutam Bhat; Guan Huang; Guilherme Bastos; Guna Seetharaman; Hongliang Zhang; Houqiang Li; Huchuan Lu; Isabela Drummond; Jack Valmadre; Jae-chan Jeong; Jae-il Cho; Jae-Yeong Lee; Jana Noskova; Jianke Zhu; Jin Gao; Jingyu Liu; Ji-Wan Kim; Joao F. Henriques; Jose M. Martinez; Junfei Zhuang; Junliang Xing; Junyu Gao; Kai Chen; Kannappan Palaniappan; Karel Lebeda; Ke Gao; Kris M. Kitani; Lei Zhang; Lijun Wang; Lingxiao Yang; Longyin Wen; Luca Bertinetto; Mahdieh Poostchi; Martin Danelljan; Matthias Müller; Mengdan Zhang; Ming-Hsuan Yang; Nianhao Xie; Ning Wang; Ondrej Miksik; P. Moallem; Pallavi Venugopal M; Pedro Senna; Philip H. S. Torr; Qiang Wang; Qifeng Yu; Qingming Huang; Rafael Martin-Nieto; Richard Bowden; Risheng Liu; Ruxandra Tapu; Simon Hadfield; Siwei Lyu; Stuart Golodetz; Sunglok Choi; Tianzhu Zhang; Titus Zaharia; Vincenzo Santopietro; Wei Zou; Weiming Hu; Wenbing Tao; Wenbo Li; Wengang Zhou; Xianguo Yu; Xiao Bian; Yang Li; Yifan Xing; Yingruo Fan; Zheng Zhu; Zhipeng Zhang; Zhiqun He

doi:10.1109/ICCVW.2017.230

The Visual Object Tracking VOT2017 Challenge Results

Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pflugfelder, Luka Cehovin Zajc, Tomas Vojir, Gustav Hager, Alan Lukezic, Abdelrahman Eldesokey, Gustavo Fernandez, Alvaro Garcia-Martin, A. Muhic, Alfredo Petrosino, Alireza Memarmoghadam, Andrea Vedaldi, Antoine Manzanera, Antoine Tran, Aydin Alatan, Bogdan MocanuBoyu Chen, Chang Huang, Changsheng Xu, Chong Sun, Dalong Du, David Zhang, Dawei Du, Deepak Mishra, Erhan Gundogdu, Erik Velasco-Salido, Fahad Shahbaz Khan, Francesco Battistone, Gorthi R. K. Sai Subrahmanyam, Goutam Bhat, Guan Huang, Guilherme Bastos, Guna Seetharaman, Hongliang Zhang, Houqiang Li, Huchuan Lu, Isabela Drummond, Jack Valmadre, Jae-chan Jeong, Jae-il Cho, Jae-Yeong Lee, Jana Noskova, Jianke Zhu, Jin Gao, Jingyu Liu, Ji-Wan Kim, Joao F. Henriques, Jose M. Martinez, Junfei Zhuang, Junliang Xing, Junyu Gao, Kai Chen, Kannappan Palaniappan, Karel Lebeda, Ke Gao, Kris M. Kitani, Lei Zhang, Lijun Wang, Lingxiao Yang, Longyin Wen, Luca Bertinetto, Mahdieh Poostchi, Martin Danelljan, Matthias Müller, Mengdan Zhang, Ming-Hsuan Yang, Nianhao Xie, Ning Wang, Ondrej Miksik, P. Moallem, Pallavi Venugopal M, Pedro Senna, Philip H. S. Torr, Qiang Wang, Qifeng Yu, Qingming Huang, Rafael Martin-Nieto, Richard Bowden, Risheng Liu, Ruxandra Tapu, Simon Hadfield, Siwei Lyu, Stuart Golodetz, Sunglok Choi, Tianzhu Zhang, Titus Zaharia, Vincenzo Santopietro, Wei Zou, Weiming Hu, Wenbing Tao, Wenbo Li, Wengang Zhou, Xianguo Yu, Xiao Bian, Yang Li, Yifan Xing, Yingruo Fan, Zheng Zhu, Zhipeng Zhang, Zhiqun He

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

447 Scopus citations

Abstract

The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new 'real-time' experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website1.

Original language	English (US)
Title of host publication	2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1949-1972
Number of pages	24
ISBN (Print)	9781538610343
DOIs	https://doi.org/10.1109/ICCVW.2017.230
State	Published - Jan 22 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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 project J2–8175. Jifi Matas and Tomáš Vojíř were supported by the Czech Science Foundation Project GACR P103/12/G084. Michael Felsberg and Gustav Häger were supported by WASP, VR (EMC2), SSF (SymbiCloud), and SNIC. Gustavo Fernández and Roman Pflugfelder were supported by the AIT Strategic Research Programme 2017 Visual Surveillance and Insight. The challenge was sponsored by Faculty of Computer Science, University of Ljubljana, Slovenia.

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10.1109/ICCVW.2017.230

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Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Zajc, L. C., Vojir, T., Hager, G., Lukezic, A., Eldesokey, A., Fernandez, G., Garcia-Martin, A., Muhic, A., Petrosino, A., Memarmoghadam, A., Vedaldi, A., Manzanera, A., Tran, A., Alatan, A., ... He, Z. (2018). The Visual Object Tracking VOT2017 Challenge Results. In 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) (pp. 1949-1972). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICCVW.2017.230

@inproceedings{374935b88d95403db2c6796ad9e36798,

title = "The Visual Object Tracking VOT2017 Challenge Results",

abstract = "The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new 'real-time' experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website1.",

author = "Matej Kristan and Ales Leonardis and Jiri Matas and Michael Felsberg and Roman Pflugfelder and Zajc, {Luka Cehovin} and Tomas Vojir and Gustav Hager and Alan Lukezic and Abdelrahman Eldesokey and Gustavo Fernandez and Alvaro Garcia-Martin and A. Muhic and Alfredo Petrosino and Alireza Memarmoghadam and Andrea Vedaldi and Antoine Manzanera and Antoine Tran and Aydin Alatan and Bogdan Mocanu and Boyu Chen and Chang Huang and Changsheng Xu and Chong Sun and Dalong Du and David Zhang and Dawei Du and Deepak Mishra and Erhan Gundogdu and Erik Velasco-Salido and Khan, {Fahad Shahbaz} and Francesco Battistone and Subrahmanyam, {Gorthi R. K. Sai} and Goutam Bhat and Guan Huang and Guilherme Bastos and Guna Seetharaman and Hongliang Zhang and Houqiang Li and Huchuan Lu and Isabela Drummond and Jack Valmadre and Jae-chan Jeong and Jae-il Cho and Jae-Yeong Lee and Jana Noskova and Jianke Zhu and Jin Gao and Jingyu Liu and Ji-Wan Kim and Henriques, {Joao F.} and Martinez, {Jose M.} and Junfei Zhuang and Junliang Xing and Junyu Gao and Kai Chen and Kannappan Palaniappan and Karel Lebeda and Ke Gao and Kitani, {Kris M.} and Lei Zhang and Lijun Wang and Lingxiao Yang and Longyin Wen and Luca Bertinetto and Mahdieh Poostchi and Martin Danelljan and Matthias M{\"u}ller and Mengdan Zhang and Ming-Hsuan Yang and Nianhao Xie and Ning Wang and Ondrej Miksik and P. Moallem and M, {Pallavi Venugopal} and Pedro Senna and Torr, {Philip H. S.} and Qiang Wang and Qifeng Yu and Qingming Huang and Rafael Martin-Nieto and Richard Bowden and Risheng Liu and Ruxandra Tapu and Simon Hadfield and Siwei Lyu and Stuart Golodetz and Sunglok Choi and Tianzhu Zhang and Titus Zaharia and Vincenzo Santopietro and Wei Zou and Weiming Hu and Wenbing Tao and Wenbo Li and Wengang Zhou and Xianguo Yu and Xiao Bian and Yang Li and Yifan Xing and Yingruo Fan and Zheng Zhu and Zhipeng Zhang and Zhiqun He",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: 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 project J2–8175. Jifi Matas and Tom{\'a}{\v s} Voj{\'i}{\v r} were supported by the Czech Science Foundation Project GACR P103/12/G084. Michael Felsberg and Gustav H{\"a}ger were supported by WASP, VR (EMC2), SSF (SymbiCloud), and SNIC. Gustavo Fern{\'a}ndez and Roman Pflugfelder were supported by the AIT Strategic Research Programme 2017 Visual Surveillance and Insight. The challenge was sponsored by Faculty of Computer Science, University of Ljubljana, Slovenia.",

year = "2018",

month = jan,

day = "22",

doi = "10.1109/ICCVW.2017.230",

language = "English (US)",

isbn = "9781538610343",

pages = "1949--1972",

booktitle = "2017 IEEE International Conference on Computer Vision Workshops (ICCVW)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

Kristan, M, Leonardis, A, Matas, J, Felsberg, M, Pflugfelder, R, Zajc, LC, Vojir, T, Hager, G, Lukezic, A, Eldesokey, A, Fernandez, G, Garcia-Martin, A, Muhic, A, Petrosino, A, Memarmoghadam, A, Vedaldi, A, Manzanera, A, Tran, A, Alatan, A, Mocanu, B, Chen, B, Huang, C, Xu, C, Sun, C, Du, D, Zhang, D, Du, D, Mishra, D, Gundogdu, E, Velasco-Salido, E, Khan, FS, Battistone, F, Subrahmanyam, GRKS, Bhat, G, Huang, G, Bastos, G, Seetharaman, G, Zhang, H, Li, H, Lu, H, Drummond, I, Valmadre, J, Jeong, J, Cho, J, Lee, J-Y, Noskova, J, Zhu, J, Gao, J, Liu, J, Kim, J-W, Henriques, JF, Martinez, JM, Zhuang, J, Xing, J, Gao, J, Chen, K, Palaniappan, K, Lebeda, K, Gao, K, Kitani, KM, Zhang, L, Wang, L, Yang, L, Wen, L, Bertinetto, L, Poostchi, M, Danelljan, M, Müller, M, Zhang, M, Yang, M-H, Xie, N, Wang, N, Miksik, O, Moallem, P, M, PV, Senna, P, Torr, PHS, Wang, Q, Yu, Q, Huang, Q, Martin-Nieto, R, Bowden, R, Liu, R, Tapu, R, Hadfield, S, Lyu, S, Golodetz, S, Choi, S, Zhang, T, Zaharia, T, Santopietro, V, Zou, W, Hu, W, Tao, W, Li, W, Zhou, W, Yu, X, Bian, X, Li, Y, Xing, Y, Fan, Y, Zhu, Z, Zhang, Z & He, Z 2018, The Visual Object Tracking VOT2017 Challenge Results. in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). Institute of Electrical and Electronics Engineers (IEEE), pp. 1949-1972. https://doi.org/10.1109/ICCVW.2017.230

TY - GEN

T1 - The Visual Object Tracking VOT2017 Challenge Results

AU - Kristan, Matej

AU - Leonardis, Ales

AU - Matas, Jiri

AU - Felsberg, Michael

AU - Pflugfelder, Roman

AU - Zajc, Luka Cehovin

AU - Vojir, Tomas

AU - Hager, Gustav

AU - Lukezic, Alan

AU - Eldesokey, Abdelrahman

AU - Fernandez, Gustavo

AU - Garcia-Martin, Alvaro

AU - Muhic, A.

AU - Petrosino, Alfredo

AU - Memarmoghadam, Alireza

AU - Vedaldi, Andrea

AU - Manzanera, Antoine

AU - Tran, Antoine

AU - Alatan, Aydin

AU - Mocanu, Bogdan

AU - Chen, Boyu

AU - Huang, Chang

AU - Xu, Changsheng

AU - Sun, Chong

AU - Du, Dalong

AU - Zhang, David

AU - Du, Dawei

AU - Mishra, Deepak

AU - Gundogdu, Erhan

AU - Velasco-Salido, Erik

AU - Khan, Fahad Shahbaz

AU - Battistone, Francesco

AU - Subrahmanyam, Gorthi R. K. Sai

AU - Bhat, Goutam

AU - Huang, Guan

AU - Bastos, Guilherme

AU - Seetharaman, Guna

AU - Zhang, Hongliang

AU - Li, Houqiang

AU - Lu, Huchuan

AU - Drummond, Isabela

AU - Valmadre, Jack

AU - Jeong, Jae-chan

AU - Cho, Jae-il

AU - Lee, Jae-Yeong

AU - Noskova, Jana

AU - Zhu, Jianke

AU - Gao, Jin

AU - Liu, Jingyu

AU - Kim, Ji-Wan

AU - Henriques, Joao F.

AU - Martinez, Jose M.

AU - Zhuang, Junfei

AU - Xing, Junliang

AU - Gao, Junyu

AU - Chen, Kai

AU - Palaniappan, Kannappan

AU - Lebeda, Karel

AU - Gao, Ke

AU - Kitani, Kris M.

AU - Zhang, Lei

AU - Wang, Lijun

AU - Yang, Lingxiao

AU - Wen, Longyin

AU - Bertinetto, Luca

AU - Poostchi, Mahdieh

AU - Danelljan, Martin

AU - Müller, Matthias

AU - Zhang, Mengdan

AU - Yang, Ming-Hsuan

AU - Xie, Nianhao

AU - Wang, Ning

AU - Miksik, Ondrej

AU - Moallem, P.

AU - M, Pallavi Venugopal

AU - Senna, Pedro

AU - Torr, Philip H. S.

AU - Wang, Qiang

AU - Yu, Qifeng

AU - Huang, Qingming

AU - Martin-Nieto, Rafael

AU - Bowden, Richard

AU - Liu, Risheng

AU - Tapu, Ruxandra

AU - Hadfield, Simon

AU - Lyu, Siwei

AU - Golodetz, Stuart

AU - Choi, Sunglok

AU - Zhang, Tianzhu

AU - Zaharia, Titus

AU - Santopietro, Vincenzo

AU - Zou, Wei

AU - Hu, Weiming

AU - Tao, Wenbing

AU - Li, Wenbo

AU - Zhou, Wengang

AU - Yu, Xianguo

AU - Bian, Xiao

AU - Li, Yang

AU - Xing, Yifan

AU - Fan, Yingruo

AU - Zhu, Zheng

AU - Zhang, Zhipeng

AU - He, Zhiqun

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: 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 project J2–8175. Jifi Matas and Tomáš Vojíř were supported by the Czech Science Foundation Project GACR P103/12/G084. Michael Felsberg and Gustav Häger were supported by WASP, VR (EMC2), SSF (SymbiCloud), and SNIC. Gustavo Fernández and Roman Pflugfelder were supported by the AIT Strategic Research Programme 2017 Visual Surveillance and Insight. The challenge was sponsored by Faculty of Computer Science, University of Ljubljana, Slovenia.

PY - 2018/1/22

Y1 - 2018/1/22

N2 - The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new 'real-time' experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website1.

AB - The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new 'real-time' experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website1.

UR - http://hdl.handle.net/10754/630412

UR - https://ieeexplore.ieee.org/document/8265440/

UR - http://www.scopus.com/inward/record.url?scp=85046256196&partnerID=8YFLogxK

U2 - 10.1109/ICCVW.2017.230

DO - 10.1109/ICCVW.2017.230

M3 - Conference contribution

SN - 9781538610343

SP - 1949

EP - 1972

BT - 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

The Visual Object Tracking VOT2017 Challenge Results

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