Human Action Recognition Technology in Dance Video Image

Abstract

In order to effectively improve the recognition rate of human action in dance video image, shorten the recognition time of human action, and ensure the recognition effect of dance motion, this study proposes a human motion recognition method of dance video image. This recognition method uses neural network theory to transform and process the human action posture in the dance video image, constructs the hybrid model of human motion feature pixels according to the feature points of human action in the image coordinate system, and extracts the human motion features in dance video image. This study uses the background probability model of human action image to sum the variance of human action feature function and update the human action feature function. It can also use Kalman filter to detect human action in dance video image. In the research process, it gets the human multiposture action image features according to the linear combination of human action features. Combined with the feature distribution matrix, it processes the human action features through pose transformation and obtains the human action feature model in the dance video image to accurately identify the human action in the dance video image. The experimental results show that the dance motion recognition effect of the proposed method is good, which can effectively improve the recognition rate of human action in dance video image and shorten the recognition time.