Human Action Pattern Recognition and Semantic Research Based on Embodied Cognition Theory

Abstract

Human body motion pattern recognition in video images is an important research direction in the field of pattern recognition. It has a very broad application prospect in many fields such as intelligent video surveillance, human-computer interaction, motion analysis, video retrieval, etc. Research has also received extensive attention from scholars at home and abroad. Pattern recognition is essentially a branch of artificial intelligence. It has its unique role in the field of artificial intelligence. Accurate recognition of human body motion patterns in video images is of great help in image classification, retrieval, human tracking and video surveillance. Based on the human visual perception mechanism, this paper proposes a human behavior recognition algorithm based on semantic saliency map. Through the combination of sliding window and similarity measure, the behavioral region that best exhibits the semantic features of the image is found, which is the semantically significant region. The semantic significant region and the original image are used as the dual input source to study the human behavior recognition, and the image is enhanced. The utilization of significant regional information better reveals the identifiable area of the image and contributes to the recognition of human behavior.