# TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos

arXiv: https://arxiv.org/abs/2508.09650 · HTML: https://mlsanity.com/paper/2508.09650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Lite-HRNet: A Lightweight High-Resolution Network](https://mlsanity.com/paper/2104.06403/) (2021)
- [Amodal Instance Segmentation](https://mlsanity.com/paper/1604.08202/) (2016)
- [Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion](https://mlsanity.com/paper/2003.04490/) (2020)
- [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection](https://mlsanity.com/paper/1704.03414/) (2017)
- [TTNet: Real-time temporal and spatial video analysis of table tennis](https://mlsanity.com/paper/2004.09927/) (2020)
- [TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications](https://mlsanity.com/paper/1907.03698/) (2019)
- [Spatio-temporal video autoencoder with differentiable memory](https://mlsanity.com/paper/1511.06309/) (2015)
- [Occlusion Handling in Generic Object Detection: A Review](https://mlsanity.com/paper/2101.08845/) (2021)
- [MonoTrack: Shuttle trajectory reconstruction from monocular badminton video](https://mlsanity.com/paper/2204.01899/) (2022)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [DeepBall: Deep Neural-Network Ball Detector](https://mlsanity.com/paper/1902.07304/) (2019)
- [Widely Applicable Strong Baseline for Sports Ball Detection and Tracking](https://mlsanity.com/paper/2311.05237/) (2023)
- [Basketball-SORT: An Association Method for Complex Multi-object Occlusion Problems in Basketball Multi-object Tracking](https://mlsanity.com/paper/2406.19655/) (2024)
- [Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking](https://mlsanity.com/paper/1909.02406/) (2019)

## Built on by (most cited first, 2 of 2)

- [Improving Robustness of Vision-Language-Action Models by Restoring Corrupted Visual Inputs](https://mlsanity.com/paper/2602.01158/) (2026)
- [TrackNetV5: Residual-Driven Spatio-Temporal Refinement and Motion Direction Decoupling for Fast Object Tracking](https://mlsanity.com/paper/2512.02789/) (2025)

## Abstract (arXiv)

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:hrefhttps://github.com/AugustRushG/TOTNetAugustRushG/TOTNet.