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SoccerNet 2024 Challenges Results

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arxiv 2409.10587 v1 pith:QI4GF5L2 submitted 2024-09-16 cs.CV

SoccerNet 2024 Challenges Results

Anthony Cioppa , Silvio Giancola , Vladimir Somers , Victor Joos , Floriane Magera , Jan Held , Seyed Abolfazl Ghasemzadeh , Xin Zhou
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Karolina Seweryn Mateusz Kowalczyk Zuzanna Mr\'oz Szymon {\L}ukasik Micha{\l} Ha{\l}o\'n Hassan Mkhallati Adrien Deli\`ege Carlos Hinojosa Karen Sanchez Amir M. Mansourian Pierre Miralles Olivier Barnich Christophe De Vleeschouwer Alexandre Alahi Bernard Ghanem Marc Van Droogenbroeck Adam Gorski Albert Clap\'es Andrei Boiarov Anton Afanasiev Artur Xarles Atom Scott ByoungKwon Lim Calvin Yeung Cristian Gonzalez Dominic R\"ufenacht Enzo Pacilio Fabian Deuser Faisal Sami Altawijri Francisco Cach\'on HanKyul Kim Haobo Wang Hyeonmin Choe Hyunwoo J Kim Il-Min Kim Jae-Mo Kang Jamshid Tursunboev Jian Yang Jihwan Hong Jimin Lee Jing Zhang Junseok Lee Kexin Zhang Konrad Habel Licheng Jiao Linyi Li Marc Guti\'errez-P\'erez Marcelo Ortega Menglong Li Milosz Lopatto Nikita Kasatkin Nikolay Nemtsev Norbert Oswald Oleg Udin Pavel Kononov Pei Geng Saad Ghazai Alotaibi Sehyung Kim Sergei Ulasen Sergio Escalera Shanshan Zhang Shuyuan Yang Sunghwan Moon Thomas B. Moeslund Vasyl Shandyba Vladimir Golovkin Wei Dai WonTaek Chung Xinyu Liu Yongqiang Zhu Youngseo Kim Yuan Li Yuting Yang Yuxuan Xiao Zehua Cheng Zhihao Li
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classification cs.CV
keywords challengesfocusingsoccernetunderstandingbroadcastfoulvideoball
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet.

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