SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| author | Somers, Vladimir Joos, Victor Cioppa, Anthony Giancola, Silvio Ghasemzadeh, Seyed Abolfazl Magera, Floriane Standaert, Baptiste Mansourian, Amir Mohammad Zhou, Xin Kasaei, Shohreh Ghanem, Bernard Alahi, Alexandre Van Droogenbroeck, Marc De Vleeschouwer, Christophe |
| author_facet | Somers, Vladimir Joos, Victor Cioppa, Anthony Giancola, Silvio Ghasemzadeh, Seyed Abolfazl Magera, Floriane Standaert, Baptiste Mansourian, Amir Mohammad Zhou, Xin Kasaei, Shohreh Ghanem, Bernard Alahi, Alexandre Van Droogenbroeck, Marc De Vleeschouwer, Christophe |
| contents | Tracking and identifying athletes on the pitch holds a central role in collecting essential insights from the game, such as estimating the total distance covered by players or understanding team tactics. This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i.e. a minimap). However, reconstructing the game state from videos captured by a single camera is challenging. It requires understanding the position of the athletes and the viewpoint of the camera to localize and identify players within the field. In this work, we formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration, as well as over 2.36 million athlete positions on the pitch with their respective role, team, and jersey number. Furthermore, we introduce GS-HOTA, a novel metric to evaluate game state reconstruction methods. Finally, we propose and release an end-to-end baseline for game state reconstruction, bootstrapping the research on this task. Our experiments show that GSR is a challenging novel task, which opens the field for future research. Our dataset and codebase are publicly available at https://github.com/SoccerNet/sn-gamestate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11335 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap Somers, Vladimir Joos, Victor Cioppa, Anthony Giancola, Silvio Ghasemzadeh, Seyed Abolfazl Magera, Floriane Standaert, Baptiste Mansourian, Amir Mohammad Zhou, Xin Kasaei, Shohreh Ghanem, Bernard Alahi, Alexandre Van Droogenbroeck, Marc De Vleeschouwer, Christophe Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Tracking and identifying athletes on the pitch holds a central role in collecting essential insights from the game, such as estimating the total distance covered by players or understanding team tactics. This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i.e. a minimap). However, reconstructing the game state from videos captured by a single camera is challenging. It requires understanding the position of the athletes and the viewpoint of the camera to localize and identify players within the field. In this work, we formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration, as well as over 2.36 million athlete positions on the pitch with their respective role, team, and jersey number. Furthermore, we introduce GS-HOTA, a novel metric to evaluate game state reconstruction methods. Finally, we propose and release an end-to-end baseline for game state reconstruction, bootstrapping the research on this task. Our experiments show that GSR is a challenging novel task, which opens the field for future research. Our dataset and codebase are publicly available at https://github.com/SoccerNet/sn-gamestate. |
| title | SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.11335 |