SoccerNet 2022 Challenges Results

Silvio Giancola; Anthony Cioppa; Adrien Deliège; Floriane Magera; Vladimir Somers; Le Kang; Xin Zhou; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdulrahman Darwish; Adrien Maglo; Albert Clapés; Andreas Luyts; Andrei Boiarov; Artur Xarles; Astrid Orcesi; Avijit Shah; Baoyu Fan; Bharath Comandur; Chen Chen; Chen Zhang; Chen Zhao; Chengzhi Lin; Cheuk Yiu Chan; Chun Chuen Hui; Dengjie Li; Fan Yang; Fan Liang; Fang Da; Feng Yan; Fufu Yu; Guanshuo Wang; H. Anthony Chan; He Zhu; Hongwei Kan; Jiaming Chu; Jianming Hu; Jianyang Gu; Jin Chen; João V.B. Soares; Jonas Theiner; Jorge De Corte; Ning Wang; Wei Zhang; Wei Li; Xing Liu; Yi Yu

doi:10.1145/3552437.3558545

SoccerNet 2022 Challenges Results

Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit ShahBaoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V.B. Soares, Jonas Theiner, Jorge De Corte, Ning Wang, Wei Zhang, Wei Li, Xing Liu, Yi Yu

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

28 Scopus citations

Abstract

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet.

Original language	English (US)
Title of host publication	MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports
Publisher	Association for Computing Machinery, Inc
Pages	75-86
Number of pages	12
ISBN (Electronic)	9781450394888
DOIs	https://doi.org/10.1145/3552437.3558545
State	Published - Oct 14 2022
Event	5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022 - Lisboa, Portugal Duration: Oct 14 2022 → …

Publication series

Name	MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports

Conference

Conference	5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022
Country/Territory	Portugal
City	Lisboa
Period	10/14/22 → …

Keywords

challenges
computer vision
datasets
neural networks
soccer
video understanding

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Software
Media Technology

Access to Document

10.1145/3552437.3558545

Cite this

Giancola, S., Cioppa, A., Deliège, A., Magera, F., Somers, V., Kang, L., Zhou, X., Barnich, O., De Vleeschouwer, C., Alahi, A., Ghanem, B., Van Droogenbroeck, M., Darwish, A., Maglo, A., Clapés, A., Luyts, A., Boiarov, A., Xarles, A., Orcesi, A., ... Yu, Y. (2022). SoccerNet 2022 Challenges Results. In MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports (pp. 75-86). (MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports). Association for Computing Machinery, Inc. https://doi.org/10.1145/3552437.3558545

Giancola, Silvio ; Cioppa, Anthony ; Deliège, Adrien et al. / SoccerNet 2022 Challenges Results. MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, Inc, 2022. pp. 75-86 (MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports).

@inproceedings{505a8855d5d748de85033faace38d670,

title = "SoccerNet 2022 Challenges Results",

abstract = "The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet.",

keywords = "challenges, computer vision, datasets, neural networks, soccer, video understanding",

author = "Silvio Giancola and Anthony Cioppa and Adrien Deli{\`e}ge and Floriane Magera and Vladimir Somers and Le Kang and Xin Zhou and Olivier Barnich and {De Vleeschouwer}, Christophe and Alexandre Alahi and Bernard Ghanem and {Van Droogenbroeck}, Marc and Abdulrahman Darwish and Adrien Maglo and Albert Clap{\'e}s and Andreas Luyts and Andrei Boiarov and Artur Xarles and Astrid Orcesi and Avijit Shah and Baoyu Fan and Bharath Comandur and Chen Chen and Chen Zhang and Chen Zhao and Chengzhi Lin and Chan, {Cheuk Yiu} and Hui, {Chun Chuen} and Dengjie Li and Fan Yang and Fan Liang and Fang Da and Feng Yan and Fufu Yu and Guanshuo Wang and Chan, {H. Anthony} and He Zhu and Hongwei Kan and Jiaming Chu and Jianming Hu and Jianyang Gu and Jin Chen and Soares, {Jo{\~a}o V.B.} and Jonas Theiner and {De Corte}, Jorge and Ning Wang and Wei Zhang and Wei Li and Xing Liu and Yi Yu",

note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022 ; Conference date: 14-10-2022",

year = "2022",

month = oct,

day = "14",

doi = "10.1145/3552437.3558545",

language = "English (US)",

series = "MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports",

publisher = "Association for Computing Machinery, Inc",

pages = "75--86",

booktitle = "MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports",

}

Giancola, S, Cioppa, A, Deliège, A, Magera, F, Somers, V, Kang, L, Zhou, X, Barnich, O, De Vleeschouwer, C, Alahi, A, Ghanem, B, Van Droogenbroeck, M, Darwish, A, Maglo, A, Clapés, A, Luyts, A, Boiarov, A, Xarles, A, Orcesi, A, Shah, A, Fan, B, Comandur, B, Chen, C, Zhang, C, Zhao, C, Lin, C, Chan, CY, Hui, CC, Li, D, Yang, F, Liang, F, Da, F, Yan, F, Yu, F, Wang, G, Chan, HA, Zhu, H, Kan, H, Chu, J, Hu, J, Gu, J, Chen, J, Soares, JVB, Theiner, J, De Corte, J, Wang, N, Zhang, W, Li, W, Liu, X & Yu, Y 2022, SoccerNet 2022 Challenges Results. in MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports. MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports, Association for Computing Machinery, Inc, pp. 75-86, 5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022, Lisboa, Portugal, 10/14/22. https://doi.org/10.1145/3552437.3558545

SoccerNet 2022 Challenges Results. / Giancola, Silvio; Cioppa, Anthony; Deliège, Adrien et al.
MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, Inc, 2022. p. 75-86 (MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports).

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

TY - GEN

T1 - SoccerNet 2022 Challenges Results

AU - Giancola, Silvio

AU - Cioppa, Anthony

AU - Deliège, Adrien

AU - Magera, Floriane

AU - Somers, Vladimir

AU - Kang, Le

AU - Zhou, Xin

AU - Barnich, Olivier

AU - De Vleeschouwer, Christophe

AU - Alahi, Alexandre

AU - Ghanem, Bernard

AU - Van Droogenbroeck, Marc

AU - Darwish, Abdulrahman

AU - Maglo, Adrien

AU - Clapés, Albert

AU - Luyts, Andreas

AU - Boiarov, Andrei

AU - Xarles, Artur

AU - Orcesi, Astrid

AU - Shah, Avijit

AU - Fan, Baoyu

AU - Comandur, Bharath

AU - Chen, Chen

AU - Zhang, Chen

AU - Zhao, Chen

AU - Lin, Chengzhi

AU - Chan, Cheuk Yiu

AU - Hui, Chun Chuen

AU - Li, Dengjie

AU - Yang, Fan

AU - Liang, Fan

AU - Da, Fang

AU - Yan, Feng

AU - Yu, Fufu

AU - Wang, Guanshuo

AU - Chan, H. Anthony

AU - Zhu, He

AU - Kan, Hongwei

AU - Chu, Jiaming

AU - Hu, Jianming

AU - Gu, Jianyang

AU - Chen, Jin

AU - Soares, João V.B.

AU - Theiner, Jonas

AU - De Corte, Jorge

AU - Wang, Ning

AU - Zhang, Wei

AU - Li, Wei

AU - Liu, Xing

AU - Yu, Yi

PY - 2022/10/14

Y1 - 2022/10/14

N2 - The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet.

AB - The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet.

KW - challenges

KW - computer vision

KW - datasets

KW - neural networks

KW - soccer

KW - video understanding

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U2 - 10.1145/3552437.3558545

DO - 10.1145/3552437.3558545

M3 - Conference contribution

AN - SCOPUS:85137720519

T3 - MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports

SP - 75

EP - 86

BT - MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports

PB - Association for Computing Machinery, Inc

T2 - 5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022

Y2 - 14 October 2022

ER -

Giancola S, Cioppa A, Deliège A, Magera F, Somers V, Kang L et al. SoccerNet 2022 Challenges Results. In MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, Inc. 2022. p. 75-86. (MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports). doi: 10.1145/3552437.3558545

SoccerNet 2022 Challenges Results

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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