A Dance Somersault Pose Recognition Model Using Multifeature Fusion Algorithm

Abstract

With the continuous progress of society, dance art has gradually entered the lives of ordinary people, and people’s appreciation level and artistic attention have gradually improved. How to improve the level of dance art has become an urgent problem to be resolved. In order to solve the problems encountered in dance tumbling pose recognition, this paper proposes a dance tumbling pose recognition model based on multifeature fusion algorithm. According to the characteristics of dance movements, an effective feature extraction method is studied. By combining the key information of bones, the fusion features of the relative position of human joints, the angle of joints, and the ratio of limb length are selected to classify the movements in the dance scene. Through the residual block automatic motion detection method, a multifeature fusion module is used to fuse multiple features, so as to improve the estimation of complex pose by the algorithm and finally complete the dance action identify. The test and analysis results on the data set show that the algorithm can identify the dance somersault pose and can effectively improve the accuracy of dance movement recognition, with strong recognition performance. It is convenient for people to recognize dance tumble posture in the video, thereby realizing the action correction function for dancers.