High-Dynamic Dance Motion Recognition Method Based on Video Visual Analysis

Abstract

In the field of computer vision, high-dynamic dance motion recognition is a difficult problem to solve. Its goal is to recognize human motion by analyzing video data using image processing and classification recognition technology. Video multifeature fusion has sparked a surge in research in a variety of fields. Several pixel points that can be distinguished and displayed in several adjacent images that can reflect their characteristics are referred to as multifeature fusion. It is responsible for a significant portion of the similarity results between the two video segments. Motion recognition relies heavily on video multifeature fusion, which has a direct impact on the robustness and accuracy of recognition results. The directional gradient histogram features, optical flow direction histogram features, and audio features extracted from dance video are used to characterize dance movements after all of the characteristics of dance movements have been considered. This paper focuses on the high-dynamic dance action recognition method based on video multifeature fusion, which aims to combine high-dynamic dance action recognition and video multifeature fusion.