TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909735777730560 |
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| author | Xu, Hao Baniya, Arbind Agrahari Wells, Sam Bouadjenek, Mohamed Reda Dazely, Richard Aryal, Sunil |
| author_facet | Xu, Hao Baniya, Arbind Agrahari Wells, Sam Bouadjenek, Mohamed Reda Dazely, Richard Aryal, Sunil |
| contents | Robust ball tracking under occlusion remains a key challenge in sports video analysis, affecting tasks like event detection and officiating. We present TOTNet, a Temporal Occlusion Tracking Network that leverages 3D convolutions, visibility-weighted loss, and occlusion augmentation to improve performance under partial and full occlusions. Developed in collaboration with Paralympics Australia, TOTNet is designed for real-world sports analytics. We introduce TTA, a new occlusion-rich table tennis dataset collected from professional-level Paralympic matches, comprising 9,159 samples with 1,996 occlusion cases. Evaluated on four datasets across tennis, badminton, and table tennis, TOTNet significantly outperforms prior state-of-the-art methods, reducing RMSE from 37.30 to 7.19 and improving accuracy on fully occluded frames from 0.63 to 0.80. These results demonstrate TOTNets effectiveness for offline sports analytics in fast-paced scenarios. Code and data access:\href{https://github.com/AugustRushG/TOTNet}{AugustRushG/TOTNet}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos Xu, Hao Baniya, Arbind Agrahari Wells, Sam Bouadjenek, Mohamed Reda Dazely, Richard Aryal, Sunil Computer Vision and Pattern Recognition Robust ball tracking under occlusion remains a key challenge in sports video analysis, affecting tasks like event detection and officiating. We present TOTNet, a Temporal Occlusion Tracking Network that leverages 3D convolutions, visibility-weighted loss, and occlusion augmentation to improve performance under partial and full occlusions. Developed in collaboration with Paralympics Australia, TOTNet is designed for real-world sports analytics. We introduce TTA, a new occlusion-rich table tennis dataset collected from professional-level Paralympic matches, comprising 9,159 samples with 1,996 occlusion cases. Evaluated on four datasets across tennis, badminton, and table tennis, TOTNet significantly outperforms prior state-of-the-art methods, reducing RMSE from 37.30 to 7.19 and improving accuracy on fully occluded frames from 0.63 to 0.80. These results demonstrate TOTNets effectiveness for offline sports analytics in fast-paced scenarios. Code and data access:\href{https://github.com/AugustRushG/TOTNet}{AugustRushG/TOTNet}. |
| title | TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.09650 |