Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network

Abstract

Human multipose motion behavior is similar; there are many actions. However, it is difficult to recognize abnormal behavior. The existing human motion behavior anomaly recognition methods have the problems of low accuracy and being time-consuming. Therefore, an anomaly recognition method of human multipose motion behavior using Generative Adversarial Network (GAN) is proposed. The Gauss model is used to segment the human multipose motion behavior image, and the image foreground of the segmentation result is the human motion target detection result. The Shi-Tomasi algorithm is selected to extract contour feature points from human motion object detection results. The extracted contour features are set as hidden space random variables and input into the GAN. The GAN uses the generator and discriminator to recognize the multipose human motion behavior and determine whether the multipose human motion behavior is abnormal. The results show that the proposed algorithm can accurately recognize abnormal human multipose motion behavior, the recognition accuracy is higher than 99%, and the average recognition time is less than 200 ms. The shadow removal effect of the foreground image obtained by the proposed algorithm can realize the accurate recognition of human multipose motion behavior abnormalities and provide a reliable basis for research in related fields.