Human Motion Recognition in Dance Video Images Based on Attitude Estimation

Abstract

With the deep integration of science and technology and culture, the estimation of human movements in dance video images will become an important application field of computer vision technology, which can be used not only for professional dancers’ movement correction, dance self-help teaching, and other application scenarios but also for athletes’ movement analysis. Therefore, it will greatly promote the implementation of teaching students in accordance with their aptitude by applying information technology to estimate dancers’ movements and postures in real time and obtaining information of classroom dance teaching status in time. In this paper, human motion recognition in dance video images is studied based on an attitude estimation algorithm. When the number of experiments reaches 20, the average value of deep learning algorithm and particle swarm optimization algorithm is 76.23 and 75.23, respectively, while the average value of attitude estimation algorithm in this paper is 77.95. Therefore, the average results of attitude estimation algorithm in this paper are slightly higher than those of other algorithms.