Human action tracking design of neural network algorithm based on GA-PSO in physical training

Abstract

In order to solve human action recognition algorithm in traditional physical training, the problem of some kind of action recognition without universality is usually solved emphatically, and a recognition system aimed at human action tracking in physical training is proposed. Firstly, local feature description of human action in physical training based on self-similarity matrix is constructed by using generalized self-similarity concept changed along with time and local feature extraction methods of optical flow field of spatio-temporal interest point; secondly, a new neural network training algorithm of particle swarm optimization (PSO) with self-adaptive genetic operator is proposed. Through probability control, at the same time of using PSO algorithm to optimize neural network, selection, intersection, variation and other genetic operation are implemented for optional particles adaptively to realize promotion of algorithm performance; simulation experiment indicates that proposed scheme can obviously improve algorithm efficiency of human action recognition and recognition accuracy in physical training.