Involuntary Table Tennis Recognition using Recurrent Neural Network on The Perception-Based Data

Sport performance analysis and sports practise are inextricably linked. It is critical to assist coaches in analysing and improving the performance of their athletes throughout training or game sessions. Because of technological advancements, notational analysis of video footage using multiple software packages is now possible. Unfortunately, the coach must manually recognise the acts before proceeding with additional analysis. The goal of this research is to create an automated system for recognising Table Tennis shots in widely available broadcasted videos using a pre-trained Recurrent Neural Network (RNN) approach. We provide a novel method for gathering video data from table tennis games and performing stroke detection and classification. Using the proposed setup, a diversified dataset encompassing video data of 4 basic strokes taken from 4 professional table tennis players, totalling 2000 films, was collected. With a validation accuracy of 97.02%, the temporal recurrent neural network model is created. Furthermore, the neural network generalises well over the data of a player who was not included in the training and validation datasets, categorising new strokes with an overall best accuracy of 91.12%. Several model architectures were trained for stroke recognition using machine learning and deep learning methodologies, and their results were compared and benchmarked. The model’s inferences, such as performance monitoring and stroke comparison, have been examined. As a result, we are contributing to the creation of a computer vision-based sports analytics system for table tennis, namely a player’s strokes, and is particularly insightful for performance enhancement.