Improvement of a Human Pose Estimation Strategy
Yang Chen, Yaqian Wang, Da Chen, Jun Gu · Advances in Computer and Communication · 2025
In recent years, the study of human posture estimation based on deep learning has become one of the hottest research directions in the field of computer vision, which has very great research value. To address the challenges of excessive network parameters and high computational complexity in human pose estimation networks, we propose a lightweight human pose estimation network, named Pose, which is inspired by the Lightweight OpenPose architecture. Specifically, we introduce an enhanced GhostNet network for efficient feature extraction. Under identical image resolution and environmental configurations, experimental results on the COCO validation set demonstrate that Pose achieves a 6.7% reduction in parameter count and a 22.2% decrease in computational complexity compared to Lightweight OpenPose. These findings indicate that Pose not only maintains competitive performance in human pose estimation but also significantly reduces both model size and computational demands when compared to conventional networks such as OpenPose and Lightweight OpenPose.