Exploring the Path of Innovative College Dance Teaching Models with Motion Capture Technology

Abstract

Abstract The continuous development of artificial intelligence has brought new exploration paths and ideas for the teaching field, however, the current application and research of motion capture technology in college dance teaching mode is less. In this study, the human body and the appearance model are modeled in three dimensions: the simulated annealing particle swarm algorithm is used to optimize the human body modeling posture. Then, the feature vector matching algorithm is used to capture the human body’s movement posture, construct the college dance teaching mode, and carry out practical application. According to the study, the capture effect of the five movements in the dance teaching classroom video clip is between 0.865 and 0.945, and each movement is recognized with an accuracy of above 95%. After using this model, it was concluded by analyzing the effect of dance teaching that the number of excellent grades in the experimental group was 2 more than that in the control group, and the number of good grades was 2 more than that in the control group. This study provides a new development path for dance teaching in colleges and universities.