Abstract: The accuracy of the existing estimation methods of human pose in the motion evaluation scene needs to be further improved. The methods rely on high-performance computing devices, and the reasoning speed on edge computing devices needs to be further enhanced. Therefore, improvement is made to the classic high-resolution network model to solve the problem of low real-time performance of the existing human pose estimation methods. To address the frequent occlusion issues in motion evaluation scene, random erasure enhancement is applied to the images in the dataset. After experimental comparison and verification, the improved method significantly reduces the number of model parameters and improves the inference speed of the model while ensuring the accuracy of attitude estimation. The algorithm exhibits stronger robustness for occlusion problems, and the improved method can meet the needs of motion evaluation scenarios.

Key words: human pose estimation, high-resolution network(HRNet), lightweight, occlusion