Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation
This work contributes to sports analytics for table tennis by focusing on stroke recognition, which is incremental as it applies existing computer vision methods to a new aspect of the sport.
The authors tackled the problem of recognizing table tennis strokes from video by collecting a dataset of 22,111 videos of 11 basic strokes from 14 professional players and developed a temporal convolutional neural network using 2D pose estimation, achieving a validation accuracy of 99.37% and generalization accuracy of 98.72% on excluded player data.
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.