# TTNet: Real-time temporal and spatial video analysis of table tennis

arXiv: https://arxiv.org/abs/2004.09927 · HTML: https://mlsanity.com/paper/2004.09927/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 30 of 36)

- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation](https://mlsanity.com/paper/2104.09907/) (2021)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation](https://mlsanity.com/paper/2111.08557/) (2021)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Widely Applicable Strong Baseline for Sports Ball Detection and Tracking](https://mlsanity.com/paper/2311.05237/) (2023)
- [F3Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos](https://mlsanity.com/paper/2504.08222/) (2025)
- [Puck localization and multi-task event recognition in broadcast hockey videos](https://mlsanity.com/paper/2105.10563/) (2021)
- [Estimation of control area in badminton doubles with pose information from top and back view drone videos](https://mlsanity.com/paper/2305.04247/) (2023)
- [Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis](https://mlsanity.com/paper/2109.14306/) (2021)
- [P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos](https://mlsanity.com/paper/2207.12730/) (2022)
- [Table Tennis Stroke Detection and Recognition Using Ball Trajectory Data](https://mlsanity.com/paper/2302.09657/) (2023)
- [LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos](https://mlsanity.com/paper/2503.20936/) (2025)
- [Detecting and Matching Related Objects with One Proposal Multiple Predictions](https://mlsanity.com/paper/2104.12574/) (2021)
- [Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control](https://mlsanity.com/paper/2504.17771/) (2025)
- [Quanta Video Restoration](https://mlsanity.com/paper/2410.14994/) (2024)
- [Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges](https://mlsanity.com/paper/2505.03991/) (2025)
- [Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction](https://mlsanity.com/paper/2506.07860/) (2025)
- [DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera](https://mlsanity.com/paper/2105.09880/) (2021)
- [Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021](https://mlsanity.com/paper/2112.11384/) (2021)
- [Two Stream Network for Stroke Detection in Table Tennis](https://mlsanity.com/paper/2112.12073/) (2021)
- [TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos](https://mlsanity.com/paper/2508.09650/) (2025)
- [AdaSpot: Spend Resolution Where It Matters for Precise Event Spotting](https://mlsanity.com/paper/2602.22073/) (2026)
- [Building Scalable Video Understanding Benchmarks through Sports](https://mlsanity.com/paper/2301.06866/) (2023)
- [Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks](https://mlsanity.com/paper/2302.02755/) (2023)
- [YO-CSA-T: A Real-time Badminton Tracking System Utilizing YOLO Based on Contextual and Spatial Attention](https://mlsanity.com/paper/2501.06472/) (2025)
- [BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking](https://mlsanity.com/paper/2509.18387/) (2025)
- [BFMD: A Full-Match Badminton Dense Dataset for Dense Shot Captioning](https://mlsanity.com/paper/2603.25533/) (2026)
- [TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos](https://mlsanity.com/paper/2605.01234/) (2026)
- [Monocular Visual Analysis for Electronic Line Calling of Tennis Games](https://mlsanity.com/paper/2107.09255/) (2021)

## Abstract (arXiv)

We present a neural network TTNet aimed at real-time processing of high-resolution table tennis videos, providing both temporal (events spotting) and spatial (ball detection and semantic segmentation) data. This approach gives core information for reasoning score updates by an auto-referee system. We also publish a multi-task dataset OpenTTGames with videos of table tennis games in 120 fps labeled with events, semantic segmentation masks, and ball coordinates for evaluation of multi-task approaches, primarily oriented on spotting of quick events and small objects tracking. TTNet demonstrated 97.0% accuracy in game events spotting along with 2 pixels RMSE in ball detection with 97.5% accuracy on the test part of the presented dataset. The proposed network allows the processing of downscaled full HD videos with inference time below 6 ms per input tensor on a machine with a single consumer-grade GPU. Thus, we are contributing to the development of real-time multi-task deep learning applications and presenting approach, which is potentially capable of substituting manual data collection by sports scouts, providing support for referees' decision-making, and gathering extra information about the game process.