  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.
