Visual Measurement of the Racket Trajectory in Spinning Ball Striking for Table Tennis Player

A high-speed monocular vision system is established to track the racket trajectory for estimating the spinning ball initiated by a human. A new fast and robust method is proposed to extract the feature sequence of a racket´s pose during the process of striking. Considering the complexity of the working environment and fast movement of the racket, a novel corner extraction algorithm with good robustness and high efficiency is presented, which mainly consists of two parts, i.e., target segmentation and line detection. Then PnP-based algorithm is adopted to get the rough pose of the racket, which is then optimized by orthogonal iteration algorithm to guarantee the orthogonality of the racket´s orientation matrix. In order to classify the spinning mode of the ball with a higher accuracy, the racket´s movement is represented by the sequence of normal vector of the racket´s surface. Experiments are thoroughly conducted to verify the effectiveness of the proposed method.