CenterNet-SPP based on multi-feature fusion for basketball posture recognition

Abstract

Aiming at the problem that the existing posture recognition algorithms can not fully reflect the dynamic characteristics of athletes' posture, this paper proposes a CenterNet-SPP model based on multi-feature fusion algorithm for basketball posture recognition. Firstly, motion posture images are collected by optical image collector, and then gray scale transformation is performed to improve the image quality. Furthermore, body contour and motion posture region are obtained based on shadow elimination technology and inter-frame difference method. Finally, radon transform and discrete wavelet transform are used to extract the motion posture region and body contour, and the two complementary features are fused and then input into the CenterNet-SPP network to realize the final posture recognition. Experimental results show that the recognition accuracy of the proposed method is higher than that of other new methods.