Lightweight and efficient human pose estimation with enhanced priori skeleton structure

A lightweight and efficient human pose estimation method with an enhanced priori skeleton structure was proposed to better utilize the unique distribution properties of human pose keypoints. The high-resolution network was used to preserve spatial location information better. The lightweight inverse residual module was employed to reduce the number of model parameters. The postural enhancement module was designed to strengthen the priori information of human pose and the connection between human pose keypoints using global spatial feature information and context information. The direction-enhanced convolution module was proposed to address the problem of missing spatial feature information of keypoints caused by blurred pixel positions and directional shifts of convolution kernel optimization when fusing multi-resolution feature images. The prior distribution of keypoints was combined by utilizing the properties of the horizontal and vertical directions of the keypoints on the torso. The experimental results demonstrate that the network can efficiently estimate human pose. The model achieves an average precision score of 78.4 on the COCO test-dev set and reduces the number of parameters by 17.4×106 compared with the benchmark network, balancing accuracy and efficiency. © 2024 Zhejiang University. All rights reserved.