Table Tennis Stroke Detection and Recognition Using Ball Trajectory Data

Understand

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.