TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos
This addresses a key challenge in sports video analysis for tasks like event detection and officiating, particularly in fast-paced scenarios, though it appears incremental as it builds on existing tracking methods.
The paper tackled robust ball detection under occlusion in sports videos by introducing TOTNet, which reduced RMSE from 37.30 to 7.19 and improved accuracy on fully occluded frames from 0.63 to 0.80.
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}.