Sports Action Recognition Based on Particle Swarm Optimization Neural Networks

Abstract

Video acquisition has become more convenient as science and technology have progressed, and the development of mobile Internet has resulted in a large amount of video data being generated every day. The question of how to analyze these videos automatically has become urgent. Among them, the study of sports movement recognition in video has important theoretical implications in sports research as well as practical application value. This paper proposes a PSO-NN-based sports action recognition model. Kernel principal component analysis is used to extract and analyze the characteristics of sports movements. The improved neural network is used to identify common human postures in sports, and the classification and block background estimation method is used to detect human targets. The feature extraction of targets is completed according to the edge features, and the feature extraction of targets is completed according to the edge features. Finally, the feature vectors are trained using a backpropagation neural network (BPNN), and the parameters of the BPNN are chosen using the PSO algorithm to create a classifier for sports action recognition. The results show that this model improves the accuracy of sports video recognition and is an effective method of sports action recognition when compared to the comparison model.