Predicting 3D Projectile Motion in Table Tennis Using Computer Vision and Physics-Informed Neural Network

Vision-based methods for gesture analysis in sports can provide valuable insights to players and coaches, without any disturbing body-worn sensors. In table tennis, accurately predicting the trajectories of the ball and its 3D kinematics is crucial for assessing the effectiveness of a stroke. While several computer vision methods have investigated this area, they often focus on trajectory prediction solely, without considering physical parameters. Additionally, these studies are typically limited to controlled laboratory settings, which can be invasive and require extensive data collection. In this paper, we present a novel visionbased approach to 3D ball trajectory analysis. Our proposed method relies on Physics-Informed Learning to predict the 3D trajectory of a table tennis ball and to infer its 3D kinematics parameters using a small amount of data acquired from video sequences. To the best of our knowledge, this is the first attempt to integrate data-driven approaches with physical constraints in order to predict the 3D trajectory and kinematics parameters of a table tennis ball.