An investigation of methods to improve the accuracy of classical dance steps based on machine vision recognition technology

Abstract

Methods that use motion capture data to analyze human behavior are highly interpretable and offer significant advantages in vision-based dance step analysis. In this study, we propose a fusion feature extraction method that describes the rotational information embedded in the skeleton and combines the advantages of single-skeleton feature extraction and Li group feature extraction methods for recognition. The feasibility and validity of the proposed model are verified by conducting recognition experiments on BVH data of classical dance steps performed by subjects. In the ablation experiments, the average accuracy of the classical dance step recognition model based on fused features for step movement recognition is improved by 3.01% and 1.86% compared with the neural network model and the Lie group network model, respectively. It has been proven that adding rotation information to skeletal features can effectively differentiate 3D motion trajectories in similar dance steps. Furthermore, the dance movement trajectories derived from this model are very clear and can be utilized to direct the correct joint point positions throughout the movement. The recognition accuracy of the fusion feature extraction-based recognition method for all seven classical dance basic foot positions is greater than 90%, which reflects the accuracy of the proposed machine vision model in recognizing classical dance steps.

Keywords

• motion capture
• skeletal features
• li group features
• machine vision
• classical dance step recognition
• 97m50