In most sports, the ability to forecast motions and trajectories is among the highest priority, which can be only earned from experience. How to predict the motion from image and visualize for training is a challenging topic for computer vision. 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 landing point of a serve before the pingpong ball is even hit using the previous and present motions of a player, which is captured only using a single RGB camera. In the precision evaluation, our system shows an acceptable accuracy and the max difference is 8.9 cm. From another pilot study, we know that our system could help the amateur to return an expert's serve. As training application, this system can be either used for training beginner's prediction skill, or used for practitioners to train how to hide their serve from being predicted.
Erwin Wu and Hideki Koike. 2020. FuturePong: Real-time Table Tennis Trajectory Forecasting using Pose Prediction Network. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems(CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–8. DOI:https://doi.org/10.1145/3334480.3382853