The visual object tracking VOT2013 challenge results

Matej Kristan, Roman Pflugfelder, Ales Leonardis, Jiri Matas, Fatih Porikli, Luka Cehovin, Georg Nebehay, Gustavo Fernandez, Tomas Vojir, Adam Gatt, Ahmad Khajenezhad, Ahmed Salahledin, Ali Soltani-Farini, Ali Zarezade, Alfredo Petrosino, Anthony Milton, Behzad BOZORGTABAR, Bo Li, Chee Chan, CherKeng Heng & 31 others Dale Ward, David Kearney, Dorothy Monekosso, Hakki Karaimer, Hamid Rabiee, Jianke Zhu, Jin Gao, Jingjing Xiao, Junge Zhang, Junliang Xing, Kaiqi Huang, Karel Lebeda, Lijun Cao, Mario Maresca, Mei Lim, Mohamed El Helw, Michael Felsberg, Paolo Remanginino, Richard Bowden, Roland GOECKE, Rustam Stolkin, Samantha Lim, Sara Maher, Sebastien Poullot, Sebastien Wong, Shin'ichi Satoh, Weihua Chen, Weiming Hu, Xiaoqin Zhang, Yang Li, ZhiHeng Niu

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    131 Citations (Scopus)

    Abstract

    Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge.net).
    Original languageEnglish
    Title of host publication2013 IEEE International conference on computer vision workshops
    EditorsPatrick Kellenberger
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages98-111
    Number of pages14
    ISBN (Electronic)9781479930227
    ISBN (Print)9781479930227
    DOIs
    Publication statusPublished - 2013
    Event2013 IEEE International conference on computer vision workshops - Sydney, Sydney, Australia
    Duration: 2 Dec 20138 Dec 2013

    Workshop

    Workshop2013 IEEE International conference on computer vision workshops
    CountryAustralia
    CitySydney
    Period2/12/138/12/13

    Fingerprint

    Network protocols
    Benchmarking
    Websites
    Lighting
    Cameras
    Industry
    Experiments

    Cite this

    Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Porikli, F., Cehovin, L., ... Niu, Z. (2013). The visual object tracking VOT2013 challenge results. In P. Kellenberger (Ed.), 2013 IEEE International conference on computer vision workshops (pp. 98-111). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCVW.2013.20
    Kristan, Matej ; Pflugfelder, Roman ; Leonardis, Ales ; Matas, Jiri ; Porikli, Fatih ; Cehovin, Luka ; Nebehay, Georg ; Fernandez, Gustavo ; Vojir, Tomas ; Gatt, Adam ; Khajenezhad, Ahmad ; Salahledin, Ahmed ; Soltani-Farini, Ali ; Zarezade, Ali ; Petrosino, Alfredo ; Milton, Anthony ; BOZORGTABAR, Behzad ; Li, Bo ; Chan, Chee ; Heng, CherKeng ; Ward, Dale ; Kearney, David ; Monekosso, Dorothy ; Karaimer, Hakki ; Rabiee, Hamid ; Zhu, Jianke ; Gao, Jin ; Xiao, Jingjing ; Zhang, Junge ; Xing, Junliang ; Huang, Kaiqi ; Lebeda, Karel ; Cao, Lijun ; Maresca, Mario ; Lim, Mei ; El Helw, Mohamed ; Felsberg, Michael ; Remanginino, Paolo ; Bowden, Richard ; GOECKE, Roland ; Stolkin, Rustam ; Lim, Samantha ; Maher, Sara ; Poullot, Sebastien ; Wong, Sebastien ; Satoh, Shin'ichi ; Chen, Weihua ; Hu, Weiming ; Zhang, Xiaoqin ; Li, Yang ; Niu, ZhiHeng. / The visual object tracking VOT2013 challenge results. 2013 IEEE International conference on computer vision workshops. editor / Patrick Kellenberger. USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 98-111
    @inproceedings{4727d4f8ac744bcfacc441b9118b837e,
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    abstract = "Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge.net).",
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    author = "Matej Kristan and Roman Pflugfelder and Ales Leonardis and Jiri Matas and Fatih Porikli and Luka Cehovin and Georg Nebehay and Gustavo Fernandez and Tomas Vojir and Adam Gatt and Ahmad Khajenezhad and Ahmed Salahledin and Ali Soltani-Farini and Ali Zarezade and Alfredo Petrosino and Anthony Milton and Behzad BOZORGTABAR and Bo Li and Chee Chan and CherKeng Heng and Dale Ward and David Kearney and Dorothy Monekosso and Hakki Karaimer and Hamid Rabiee and Jianke Zhu and Jin Gao and Jingjing Xiao and Junge Zhang and Junliang Xing and Kaiqi Huang and Karel Lebeda and Lijun Cao and Mario Maresca and Mei Lim and {El Helw}, Mohamed and Michael Felsberg and Paolo Remanginino and Richard Bowden and Roland GOECKE and Rustam Stolkin and Samantha Lim and Sara Maher and Sebastien Poullot and Sebastien Wong and Shin'ichi Satoh and Weihua Chen and Weiming Hu and Xiaoqin Zhang and Yang Li and ZhiHeng Niu",
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    Kristan, M, Pflugfelder, R, Leonardis, A, Matas, J, Porikli, F, Cehovin, L, Nebehay, G, Fernandez, G, Vojir, T, Gatt, A, Khajenezhad, A, Salahledin, A, Soltani-Farini, A, Zarezade, A, Petrosino, A, Milton, A, BOZORGTABAR, B, Li, B, Chan, C, Heng, C, Ward, D, Kearney, D, Monekosso, D, Karaimer, H, Rabiee, H, Zhu, J, Gao, J, Xiao, J, Zhang, J, Xing, J, Huang, K, Lebeda, K, Cao, L, Maresca, M, Lim, M, El Helw, M, Felsberg, M, Remanginino, P, Bowden, R, GOECKE, R, Stolkin, R, Lim, S, Maher, S, Poullot, S, Wong, S, Satoh, S, Chen, W, Hu, W, Zhang, X, Li, Y & Niu, Z 2013, The visual object tracking VOT2013 challenge results. in P Kellenberger (ed.), 2013 IEEE International conference on computer vision workshops. IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 98-111, 2013 IEEE International conference on computer vision workshops, Sydney, Australia, 2/12/13. https://doi.org/10.1109/ICCVW.2013.20

    The visual object tracking VOT2013 challenge results. / Kristan, Matej; Pflugfelder, Roman; Leonardis, Ales; Matas, Jiri; Porikli, Fatih; Cehovin, Luka; Nebehay, Georg; Fernandez, Gustavo; Vojir, Tomas; Gatt, Adam; Khajenezhad, Ahmad; Salahledin, Ahmed; Soltani-Farini, Ali; Zarezade, Ali; Petrosino, Alfredo; Milton, Anthony; BOZORGTABAR, Behzad; Li, Bo; Chan, Chee; Heng, CherKeng; Ward, Dale; Kearney, David; Monekosso, Dorothy; Karaimer, Hakki; Rabiee, Hamid; Zhu, Jianke; Gao, Jin; Xiao, Jingjing; Zhang, Junge; Xing, Junliang; Huang, Kaiqi; Lebeda, Karel; Cao, Lijun; Maresca, Mario; Lim, Mei; El Helw, Mohamed; Felsberg, Michael; Remanginino, Paolo; Bowden, Richard; GOECKE, Roland; Stolkin, Rustam; Lim, Samantha; Maher, Sara; Poullot, Sebastien; Wong, Sebastien; Satoh, Shin'ichi; Chen, Weihua; Hu, Weiming; Zhang, Xiaoqin; Li, Yang; Niu, ZhiHeng.

    2013 IEEE International conference on computer vision workshops. ed. / Patrick Kellenberger. USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 98-111.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    TY - GEN

    T1 - The visual object tracking VOT2013 challenge results

    AU - Kristan, Matej

    AU - Pflugfelder, Roman

    AU - Leonardis, Ales

    AU - Matas, Jiri

    AU - Porikli, Fatih

    AU - Cehovin, Luka

    AU - Nebehay, Georg

    AU - Fernandez, Gustavo

    AU - Vojir, Tomas

    AU - Gatt, Adam

    AU - Khajenezhad, Ahmad

    AU - Salahledin, Ahmed

    AU - Soltani-Farini, Ali

    AU - Zarezade, Ali

    AU - Petrosino, Alfredo

    AU - Milton, Anthony

    AU - BOZORGTABAR, Behzad

    AU - Li, Bo

    AU - Chan, Chee

    AU - Heng, CherKeng

    AU - Ward, Dale

    AU - Kearney, David

    AU - Monekosso, Dorothy

    AU - Karaimer, Hakki

    AU - Rabiee, Hamid

    AU - Zhu, Jianke

    AU - Gao, Jin

    AU - Xiao, Jingjing

    AU - Zhang, Junge

    AU - Xing, Junliang

    AU - Huang, Kaiqi

    AU - Lebeda, Karel

    AU - Cao, Lijun

    AU - Maresca, Mario

    AU - Lim, Mei

    AU - El Helw, Mohamed

    AU - Felsberg, Michael

    AU - Remanginino, Paolo

    AU - Bowden, Richard

    AU - GOECKE, Roland

    AU - Stolkin, Rustam

    AU - Lim, Samantha

    AU - Maher, Sara

    AU - Poullot, Sebastien

    AU - Wong, Sebastien

    AU - Satoh, Shin'ichi

    AU - Chen, Weihua

    AU - Hu, Weiming

    AU - Zhang, Xiaoqin

    AU - Li, Yang

    AU - Niu, ZhiHeng

    PY - 2013

    Y1 - 2013

    N2 - Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge.net).

    AB - Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge.net).

    KW - visual object tracking

    U2 - 10.1109/ICCVW.2013.20

    DO - 10.1109/ICCVW.2013.20

    M3 - Conference contribution

    SN - 9781479930227

    SP - 98

    EP - 111

    BT - 2013 IEEE International conference on computer vision workshops

    A2 - Kellenberger, Patrick

    PB - IEEE, Institute of Electrical and Electronics Engineers

    CY - USA

    ER -

    Kristan M, Pflugfelder R, Leonardis A, Matas J, Porikli F, Cehovin L et al. The visual object tracking VOT2013 challenge results. In Kellenberger P, editor, 2013 IEEE International conference on computer vision workshops. USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 98-111 https://doi.org/10.1109/ICCVW.2013.20