# Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation

arXiv: https://arxiv.org/abs/2104.09907 · HTML: https://mlsanity.com/paper/2104.09907/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [TTNet: Real-time temporal and spatial video analysis of table tennis](https://mlsanity.com/paper/2004.09927/) (2020)
- [SPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping Pong](https://mlsanity.com/paper/1912.06640/) (2019)

## Built on by (most cited first, 23 of 23)

- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [MonoTrack: Shuttle trajectory reconstruction from monocular badminton video](https://mlsanity.com/paper/2204.01899/) (2022)
- [FenceNet: Fine-grained Footwork Recognition in Fencing](https://mlsanity.com/paper/2204.09434/) (2022)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024)
- [SpinDOE: A ball spin estimation method for table tennis robot](https://mlsanity.com/paper/2303.03879/) (2023)
- [All Keypoints You Need: Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes](https://mlsanity.com/paper/2304.02939/) (2023)
- [Estimation of control area in badminton doubles with pose information from top and back view drone videos](https://mlsanity.com/paper/2305.04247/) (2023)
- [Recognition of Freely Selected Keypoints on Human Limbs](https://mlsanity.com/paper/2204.06326/) (2022)
- [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)
- [GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correction](https://mlsanity.com/paper/2411.06725/) (2024)
- [TT3D: Table Tennis 3D Reconstruction](https://mlsanity.com/paper/2504.10035/) (2025)
- [Multi-Camera Asynchronous Ball Localization and Trajectory Prediction with Factor Graphs and Human Poses](https://mlsanity.com/paper/2401.17185/) (2024)
- [Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion](https://mlsanity.com/paper/2310.17462/) (2023)
- [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)
- [Enhancing Predictive Accuracy in Tennis: Integrating Fuzzy Logic and CV-GRNN for Dynamic Match Outcome and Player Momentum Analysis](https://mlsanity.com/paper/2503.21809/) (2025)
- [LGM-Pose: A Lightweight Global Modeling Network for Real-time Human Pose Estimation](https://mlsanity.com/paper/2506.04561/) (2025)
- [Calisthenics Skills Temporal Video Segmentation](https://mlsanity.com/paper/2507.12245/) (2025)
- [Extended OpenTT Games Dataset: A table tennis dataset for fine-grained shot type and point outcome](https://mlsanity.com/paper/2512.19327/) (2025)
- [How good was my shot? Quantifying Player Skill Level in Table Tennis](https://mlsanity.com/paper/2603.25736/) (2026)
- [TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification](https://mlsanity.com/paper/2605.02558/) (2026)

## Abstract (arXiv)

We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players, summing up to a total of 22111 videos has been collected using the proposed setup. The temporal convolutional neural network model developed using 2D pose estimation performs multiclass classification of these 11 table tennis strokes with a validation accuracy of 99.37%. Moreover, the neural network generalizes well over the data of a player excluded from the training and validation dataset, classifying the fresh strokes with an overall best accuracy of 98.72%. Various model architectures using machine learning and deep learning based approaches have been trained for stroke recognition and their performances have been compared and benchmarked. Inferences such as performance monitoring and stroke comparison of the players using the model have been discussed. Therefore, we are contributing to the development of a computer vision based sports analytics system for the sport of table tennis that focuses on the previously unexploited aspect of the sport i.e., a player's strokes, which is extremely insightful for performance improvement.