Research on badminton take-off recognition method based on improved deep learning

Abstract

Because of the fast take-off speed of badminton, a single action recognition method can’t quickly and accurately identify the action. Therefore, a new badminton take-off recognition method based on improved deep learning is proposed to capture badminton take-off accurately. Collect badminton sports videos and get images of athletes’ activity areas by tracking the moving targets in badminton competition videos. The static characteristics of badminton players’ take-off actions are extracted from the athletes’ activity areas’ images using 3D ConvNets. According to the human joint points in the badminton player’s target tracking image, the human skeleton sequence is constructed by using a 2D coordinate pseudo-image and 2D skeleton data design algorithm, and the dynamic characteristics of badminton take-off action are extracted from the human skeleton sequence by using LSTM (Long-term and Short-term Memory Network). After the static and dynamic features are fused by weighted summation, badminton take-off feature fusion results are input into a convolutional neural network (CNN) to complete badminton take-off recognition. The CNN pool layer is improved by adaptive pooling, and the network convergence is accelerated by combining batch normalization to further optimize the recognition results of badminton take-off. Experiments show that the human skeleton model can accurately match human movements and assist in extracting action features. The improved CNN has greatly improved the accuracy of recognition of take-off actions. When recognizing real images, it can accurately identify human movements and judge whether there is a take-off action.