A Hierarchical Children’s Dance Movement Pose Estimation Method Based on Sequence Multiscale Feature Fusion Representation

Abstract

Aiming at the problem that traditional human motion pose estimation methods cannot accurately capture and estimate the movement changes of children dancers, a hierarchical dance pose estimation method for children based on sequence multiscale feature fusion representation is proposed. By comparing the pose feature extraction algorithm with the actual recognition effect, the recognition rates of the dancer’s upper body, infiltration, and whole body have increased by 14.2, 10.6, and 12.6, respectively. The experimental results show that the proposed pose estimation algorithm achieves good pose estimation results on both the standard human pose estimation dataset and the self-built dance dataset.