  In this work, the novel task of detecting and classifying table tennis
strokes solely using the ball trajectory has been explored. A single camera
setup positioned in the umpire's view has been employed to procure a dataset
consisting of six stroke classes executed by four professional table tennis
players. Ball tracking using YOLOv4, a traditional object detection model, and
TrackNetv2, a temporal heatmap based model, have been implemented on our
dataset and their performances have been benchmarked. A mathematical approach
developed to extract temporal boundaries of strokes using the ball trajectory
data yielded a total of 2023 valid strokes in our dataset, while also detecting
services and missed strokes successfully. The temporal convolutional network
developed performed stroke recognition on completely unseen data with an
accuracy of 87.155%. Several machine learning and deep learning based model
architectures have been trained for stroke recognition using ball trajectory
input and benchmarked based on their performances. While stroke recognition in
the field of table tennis has been extensively explored based on human action
recognition using video data focused on the player's actions, the use of ball
trajectory data for the same is an unexplored characteristic of the sport.
Hence, the motivation behind the work is to demonstrate that meaningful
inferences such as stroke detection and recognition can be drawn using minimal
input information.
