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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.06357 |
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| _version_ | 1866917981148151808 |
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| author | Golovkin, Vladimir Nemtsev, Nikolay Shandyba, Vasyl Udin, Oleg Kasatkin, Nikita Kononov, Pavel Afanasiev, Anton Ulasen, Sergey Boiarov, Andrei |
| author_facet | Golovkin, Vladimir Nemtsev, Nikolay Shandyba, Vasyl Udin, Oleg Kasatkin, Nikita Kononov, Pavel Afanasiev, Anton Ulasen, Sergey Boiarov, Andrei |
| contents | Game State Reconstruction (GSR), a critical task in Sports Video Understanding, involves precise tracking and localization of all individuals on the football field-players, goalkeepers, referees, and others - in real-world coordinates. This capability enables coaches and analysts to derive actionable insights into player movements, team formations, and game dynamics, ultimately optimizing training strategies and enhancing competitive advantage. Achieving accurate GSR using a single-camera setup is highly challenging due to frequent camera movements, occlusions, and dynamic scene content. In this work, we present a robust end-to-end pipeline for tracking players across an entire match using a single-camera setup. Our solution integrates a fine-tuned YOLOv5m for object detection, a SegFormer-based camera parameter estimator, and a DeepSORT-based tracking framework enhanced with re-identification, orientation prediction, and jersey number recognition. By ensuring both spatial accuracy and temporal consistency, our method delivers state-of-the-art game state reconstruction, securing first place in the SoccerNet Game State Reconstruction Challenge 2024 and significantly outperforming competing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06357 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction Golovkin, Vladimir Nemtsev, Nikolay Shandyba, Vasyl Udin, Oleg Kasatkin, Nikita Kononov, Pavel Afanasiev, Anton Ulasen, Sergey Boiarov, Andrei Computer Vision and Pattern Recognition Machine Learning Game State Reconstruction (GSR), a critical task in Sports Video Understanding, involves precise tracking and localization of all individuals on the football field-players, goalkeepers, referees, and others - in real-world coordinates. This capability enables coaches and analysts to derive actionable insights into player movements, team formations, and game dynamics, ultimately optimizing training strategies and enhancing competitive advantage. Achieving accurate GSR using a single-camera setup is highly challenging due to frequent camera movements, occlusions, and dynamic scene content. In this work, we present a robust end-to-end pipeline for tracking players across an entire match using a single-camera setup. Our solution integrates a fine-tuned YOLOv5m for object detection, a SegFormer-based camera parameter estimator, and a DeepSORT-based tracking framework enhanced with re-identification, orientation prediction, and jersey number recognition. By ensuring both spatial accuracy and temporal consistency, our method delivers state-of-the-art game state reconstruction, securing first place in the SoccerNet Game State Reconstruction Challenge 2024 and significantly outperforming competing methods. |
| title | From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2504.06357 |