Research on human posture recognition based on multi-feature fusion chunk sampling particle filtering algorithm

As a developing research direction in the field of computer vision, human posture recognition(HPR) involves many related technologies such as pattern recognition, artificial intelligence, image processing and machine vision. This paper focuses on the application of multi feature fusion(MFF) and block sampling(BS) particle filter algorithm(PFA) in HPR, and briefly analyzes the design of human posture data acquisition terminal, the analysis and extraction of human contour features, and the analysis and extraction of stride features; This paper discusses the MFF and BS PFA, and applies it to HPR. Through the statistical experiments of recognition rate(RR) and sensitivity of virtual reality actions, the effectiveness of MFF and BS PFA applied to HPR is verified.