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; José Henrique Brito; Jun Zhang; Junjie Li; Junwei Liang; Leqi Shen; Lin Ma; Lingchi Chen; Miguel Santos Marques; Mike Azatov; Nikita Kasatkin; Ning Wang; Qiong Jia; Quoc Cuong Pham; Ralph Ewerth; Ran Song; Rengang Li; Rikke Gade; Ruben Debien; Runze Zhang; Sangrok Lee; Sergio Escalera; Shan Jiang; Shigeyuki Odashima; Shimin Chen; Shoichi Masui; Shouhong Ding; Sin Wai Chan; Siyu Chen; Tallal El-Shabrawy; Tao He; Thomas B. Moeslund; Wan Chi Siu; Wei Zhang; Wei Li; Xiangwei Wang; Xiao Tan; Xiaochuan Li; Xiaolin Wei; Xiaoqing Ye; Xing Liu; Xinying Wang; Yandong Guo; Yaqian Zhao; Yi Yu; Yingying Li; Yue He; Yujie Zhong; Zhenhua Guo; Zhiheng Li

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, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, Rengang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin Wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan Chi Siu, Wei Zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, Yaqian Zhao, Yi Yu, Yingying Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

11 Citationer (Scopus)

70 Downloads (Pure)

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.

Originalsprog	Engelsk
Titel	MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports
Antal sider	12
Forlag	Association for Computing Machinery
Publikationsdato	14 okt. 2022
Sider	75-86
ISBN (Elektronisk)	9781450394888
DOI	https://doi.org/10.1145/3552437.3558545
Status	Udgivet - 14 okt. 2022
Begivenhed	5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022 - Lisboa, Portugal Varighed: 14 okt. 2022 → 14 okt. 2022

Konference

Konference	5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022
Land/Område	Portugal
By	Lisboa
Periode	14/10/2022 → 14/10/2022
Sponsor	ACM SIGMM

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

Bibliografisk note

Publisher Copyright:
© 2022 Owner/Author.

Adgang til dokumentet

10.1145/3552437.3558545Licens: CC BY 4.0

Open Access articleForlagets udgivne version, 1,7 MBLicens: CC BY 4.0

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

Andre filer og links

Link to publication in Scopus

Citationsformater

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., ... Li, Z. (2022). SoccerNet 2022 Challenges Results. I MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports (s. 75-86). Association for Computing Machinery. https://doi.org/10.1145/3552437.3558545

@inproceedings{8cd46d2352fd46da942497a63c35fa14,

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 Brito, {Jos{\'e} Henrique} and Jun Zhang and Junjie Li and Junwei Liang and Leqi Shen and Lin Ma and Lingchi Chen and {Santos Marques}, Miguel and Mike Azatov and Nikita Kasatkin and Ning Wang and Qiong Jia and Pham, {Quoc Cuong} and Ralph Ewerth and Ran Song and Rengang Li and Rikke Gade and Ruben Debien and Runze Zhang and Sangrok Lee and Sergio Escalera and Shan Jiang and Shigeyuki Odashima and Shimin Chen and Shoichi Masui and Shouhong Ding and Chan, {Sin Wai} and Siyu Chen and Tallal El-Shabrawy and Tao He and Moeslund, {Thomas B.} and Siu, {Wan Chi} and Wei Zhang and Wei Li and Xiangwei Wang and Xiao Tan and Xiaochuan Li and Xiaolin Wei and Xiaoqing Ye and Xing Liu and Xinying Wang and Yandong Guo and Yaqian Zhao and Yi Yu and Yingying Li and Yue He and Yujie Zhong and Zhenhua Guo and Zhiheng Li",

note = "Funding Information: This work was supported by the Service Public de Wallonie (SPW) Recherche under the DeepSport project and Grant No. 2010235 (ARIAC by https://DigitalWallonia4.ai), the FRIA, the FNRS, and KAUST Oce of Sponsored Research through the Visual Computing Center funding. 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 Through 14-10-2022",

year = "2022",

month = oct,

day = "14",

doi = "10.1145/3552437.3558545",

language = "English",

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

pages = "75--86",

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

publisher = "Association for Computing Machinery",

address = "United States",

}

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, Brito, JH, Zhang, J, Li, J, Liang, J, Shen, L, Ma, L, Chen, L, Santos Marques, M, Azatov, M, Kasatkin, N, Wang, N, Jia, Q, Pham, QC, Ewerth, R, Song, R, Li, R, Gade, R, Debien, R, Zhang, R, Lee, S, Escalera, S, Jiang, S, Odashima, S, Chen, S, Masui, S, Ding, S, Chan, SW, Chen, S, El-Shabrawy, T, He, T, Moeslund, TB, Siu, WC, Zhang, W, Li, W, Wang, X, Tan, X, Li, X, Wei, X, Ye, X, Liu, X, Wang, X, Guo, Y, Zhao, Y, Yu, Y, Li, Y, He, Y, Zhong, Y, Guo, Z & Li, Z 2022, SoccerNet 2022 Challenges Results. i MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery, MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports, s. 75-86, 5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022, Lisboa, Portugal, 14/10/2022. 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, 2022. s. 75-86 (MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › 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 - Brito, José Henrique

AU - Zhang, Jun

AU - Li, Junjie

AU - Liang, Junwei

AU - Shen, Leqi

AU - Ma, Lin

AU - Chen, Lingchi

AU - Santos Marques, Miguel

AU - Azatov, Mike

AU - Kasatkin, Nikita

AU - Wang, Ning

AU - Jia, Qiong

AU - Pham, Quoc Cuong

AU - Ewerth, Ralph

AU - Song, Ran

AU - Li, Rengang

AU - Gade, Rikke

AU - Debien, Ruben

AU - Zhang, Runze

AU - Lee, Sangrok

AU - Escalera, Sergio

AU - Jiang, Shan

AU - Odashima, Shigeyuki

AU - Chen, Shimin

AU - Masui, Shoichi

AU - Ding, Shouhong

AU - Chan, Sin Wai

AU - Chen, Siyu

AU - El-Shabrawy, Tallal

AU - He, Tao

AU - Moeslund, Thomas B.

AU - Siu, Wan Chi

AU - Zhang, Wei

AU - Li, Wei

AU - Wang, Xiangwei

AU - Tan, Xiao

AU - Li, Xiaochuan

AU - Wei, Xiaolin

AU - Ye, Xiaoqing

AU - Liu, Xing

AU - Wang, Xinying

AU - Guo, Yandong

AU - Zhao, Yaqian

AU - Yu, Yi

AU - Li, Yingying

AU - He, Yue

AU - Zhong, Yujie

AU - Guo, Zhenhua

AU - Li, Zhiheng

N1 - Funding Information: This work was supported by the Service Public de Wallonie (SPW) Recherche under the DeepSport project and Grant No. 2010235 (ARIAC by https://DigitalWallonia4.ai), the FRIA, the FNRS, and KAUST Oce of Sponsored Research through the Visual Computing Center funding. Publisher Copyright: © 2022 Owner/Author.

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

UR - http://www.scopus.com/inward/record.url?scp=85137720519&partnerID=8YFLogxK

U2 - 10.1145/3552437.3558545

DO - 10.1145/3552437.3558545

M3 - Article in proceeding

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

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

Y2 - 14 October 2022 through 14 October 2022

ER -

Giancola S, Cioppa A, Deliège A, Magera F, Somers V, Kang L et al. SoccerNet 2022 Challenges Results. I MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports. Association for Computing Machinery. 2022. s. 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

Konference

Bibliografisk note

Adgang til dokumentet

AUB Link

Andre filer og links

Fingeraftryk

Citationsformater