{"ID":2882714,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09650","arxiv_id":"2508.09650","title":"TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos","abstract":"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}.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.09650","url_pdf":"https://arxiv.org/pdf/2508.09650v1","authors":"[\"Hao Xu\",\"Arbind Agrahari Baniya\",\"Sam Wells\",\"Mohamed Reda Bouadjenek\",\"Richard Dazely\",\"Sunil Aryal\"]","published":"2025-08-13T09:33:23Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":610916,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2882714,"paper_url":"https://arxiv.org/abs/2508.09650","paper_title":"TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos","repo_url":"https://github.com/AugustRushG/TOTNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}