Real-time Table Tennis Forecasting System based on Long Short-term Pose Prediction Network

Humans’ ability to forecast motions and trajectories, are one of the most important abilities in many sports. With the development of deep learning and computer vision, it is becoming possible to do the same thing with real-time computing. In this paper, we present a real-time table tennis forecasting system using a long short-term pose prediction network. Our system can predict the trajectory of a serve before the pingpong ball is even hit based on the previous and present motions of a player, which is captured only using a single RGB camera. The system can be either used for training beginner’s prediction skill, or used for practitioners to train a conceal serve.