SoccerNet 2025 Challenges Results

• URL: http://arxiv.org/abs/2508.19182v1
• Date: Tue, 26 Aug 2025 16:37:07 GMT
• Title: SoccerNet 2025 Challenges Results
• Authors: Silvio Giancola, Anthony Cioppa, Marc Gutiérrez-Pérez, Jan Held, Carlos Hinojosa, Victor Joos, Arnaud Leduc, Floriane Magera, Karen Sanchez, Vladimir Somers, Artur Xarles, Antonio Agudo, Alexandre Alahi, Olivier Barnich, Albert Clapés, Christophe De Vleeschouwer, Sergio Escalera, Bernard Ghanem, Thomas B. Moeslund, Marc Van Droogenbroeck, Tomoki Abe, Saad Alotaibi, Faisal Altawijri, Steven Araujo, Xiang Bai, Xiaoyang Bi, Jiawang Cao, Vanyi Chao, Kamil Czarnogórski, Fabian Deuser, Mingyang Du, Tianrui Feng, Patrick Frenzel, Mirco Fuchs, Jorge García, Konrad Habel, Takaya Hashiguchi, Sadao Hirose, Xinting Hu, Yewon Hwang, Ririko Inoue, Riku Itsuji, Kazuto Iwai, Hongwei Ji, Yangguang Ji, Licheng Jiao, Yuto Kageyama, Yuta Kamikawa, Yuuki Kanasugi, Hyungjung Kim, Jinwook Kim, Takuya Kurihara, Bozheng Li, Lingling Li, Xian Li, Youxing Lian, Dingkang Liang, Hongkai Lin, Jiadong Lin, Jian Liu, Liang Liu, Shuaikun Liu, Zhaohong Liu, Yi Lu, Federico Méndez, Huadong Ma, Wenping Ma, Jacek Maksymiuk, Henry Mantilla, Ismail Mathkour, Daniel Matthes, Ayaha Motomochi, Amrulloh Robbani Muhammad, Haruto Nakayama, Joohyung Oh, Yin May Oo, Marcelo Ortega, Norbert Oswald, Rintaro Otsubo, Fabian Perez, Mengshi Qi, Cristian Rey, Abel Reyes-Angulo, Oliver Rose, Hoover Rueda-Chacón, Hideo Saito, Jose Sarmiento, Kanta Sawafuji, Atom Scott, Xi Shen, Pragyan Shrestha, Jae-Young Sim, Long Sun, Yuyang Sun, Tomohiro Suzuki, Licheng Tang, Masato Tonouchi, Ikuma Uchida, Henry O. Velesaca, Tiancheng Wang, Rio Watanabe, Jay Wu, Yongliang Wu, Shunzo Yamagishi, Di Yang, Xu Yang, Yuxin Yang, Hao Ye, Xinyu Ye, Calvin Yeung, Xuanlong Yu, Chao Zhang, Dingyuan Zhang, Kexing Zhang, Zhe Zhao, Xin Zhou, Wenbo Zhu, Julian Ziegler,
• Abstract summary: SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open effort, dedicated to advancing computer vision research in football video understanding.<n>This year's challenges span four vision-based tasks: Team Ball Action Spotting, Monocular Depth Estimation, Multi-View Foul Recognition, and Game State Reconstruction.<n>Report presents the results of each challenge, highlights the top-performing solutions, and provides insights into the progress made by the community.
• Score: 205.71032061537747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understanding. This year's challenges span four vision-based tasks: (1) Team Ball Action Spotting, focused on detecting ball-related actions in football broadcasts and assigning actions to teams; (2) Monocular Depth Estimation, targeting the recovery of scene geometry from single-camera broadcast clips through relative depth estimation for each pixel; (3) Multi-View Foul Recognition, requiring the analysis of multiple synchronized camera views to classify fouls and their severity; and (4) Game State Reconstruction, aimed at localizing and identifying all players from a broadcast video to reconstruct the game state on a 2D top-view of the field. Across all tasks, participants were provided with large-scale annotated datasets, unified evaluation protocols, and strong baselines as starting points. This report presents the results of each challenge, highlights the top-performing solutions, and provides insights into the progress made by the community. The SoccerNet Challenges continue to serve as a driving force for reproducible, open research at the intersection of computer vision, artificial intelligence, and sports. 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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