A Method for Prediction the Trajectory of Table Tennis in Multirotation State Based on Binocular Vision

The research on the space trajectory of high-speed moving and flying objects has very important research significance and application value in the fields of sports, military, aerospace, and industry. Table tennis has the characteristics of small size, fast flight speed, and complex motion model. It is very suitable as an experimental object for the study of flying object trajectory. This study takes table tennis as the research object to carry out research on the trajectory prediction of flying objects and builds a trajectory prediction system based on the trajectory prediction model, combining the constraints of the simple physical motion model and the deviation correction of the double LSTM neural network. Aiming at the problem of trajectory extraction of flying table tennis balls, a high-speed industrial camera was used to build a table tennis trajectory extraction system based on binocular vision. A multicamera information fusion method based on dynamic weights is proposed for the prediction of the trajectory of flying table tennis. In order to solve the problems that some model parameters are difficult to measure and the model is too complicated in the traditional physical motion model of table tennis trajectory, a method combining simple physics is proposed. This paper proposes a trajectory prediction model with motion model constraints and dual LSTM neural network bias correction. Experiments show that the proposed method can greatly improve the accuracy of the trajectory extraction and prediction system and can achieve a certain success rate of hitting.