针对体育视频中球体遮挡问题，提出高效跟踪网络TOTNet。
TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos
- 用3D卷积和可见性加权损失建模时间连续性。
- 在全遮挡帧上准确率提升至0.80，误差降低至7.19。
- 专为残奥会赛事设计，适合实时体育分析场景。
在体育视频分析中，遮挡下的稳定球体跟踪仍是关键挑战，影响事件识别与裁判辅助。本文提出TOTNet——一种时序遮挡感知跟踪网络，结合3D卷积、可见性加权损失和遮挡增强策略，显著提升部分与完全遮挡下的表现。该模型由澳大利亚残奥委会合作开发，适用于真实世界体育数据分析。我们构建了TTA数据集，包含来自职业级残奥会比赛的9,159个样本，其中1,996例为遮挡情况。在网球、羽毛球和乒乓球共四个数据集上评估，TOTNet相比现有最优方法，将均方根误差从37.30降至7.19，并将全遮挡帧上的准确率从0.63提升至0.80。结果证明了TOTNet在高速体育场景下离线分析的有效性。代码与数据：https://github.com/AugustRushG/TOTNet。
原文摘要 · Abstract (English)
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}.
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