Investigation of Posture Similarity Metrics for Online Dance Learning Support

Abstract

Dance has played a major role in expressing the characteristics of each country and culture. It is necessary to have a teacher who can teach dance accurately. This paper proposes a system that can analyze skeleton data from recorded videos and calculate similarities between teacher’s and student’s motions. The system extracts the positions of the joints of each dancer in the video using OpenPose and identifies how different are the dance movements using dynamic time warping (DTW), Pearson correlation, and time lagged cross correlation (TLCC). The results obtained in this study show that the information of skeletons identified from recorded videos can be accurately extracted and the similarity between the teacher’s and student’s motions can be determined for scoring the dance quality.