Table Tennis ball kinematic parameters estimation from non-intrusive single-view videos

The context of this research is the use of computer vision to assess the quality of sport gestures in non-intrusive conditions, i.e. without any body-worn sensors. This paper addresses the estimation of Table Tennis ball kinematic parameters from single-view videos. These parameters are important for analyzing effects given on the ball by the players, a key factor in the Table Tennis game. We introduce 3D ball trajectories extraction and analysis with very few acquisition constraints. To obtain ball to camera distance, the estimation of the apparent ball size is performed with a 2D CNN trained on a generated dataset. By formulating the problem of trajectory estimation as the solution of an Ordinary Differential Equation (ODE) with initial conditions, we can extract the ball kinematic parameters such as tangential and rotation speeds. Validation experiments are presented on both a synthetic dataset and on real video sequences.