Design of a Lightweight Human Pose Estimation Algorithm Based on AlphaPose

This paper presents a lightweight human pose estimation algorithm, M-AlphaPose, based on AlphaPose. Firstly, we integrate YOLOV5s for human detection, and use MobileNetV3-S as the backbone for pose estimation. Finally, we achieve an effective system for estimating full-body poses of multiple individuals. The study conducts ablation experiments to analyze the impact of different components and compares the performance with other algorithms, emphasizing the importance of lightweight design. Compared to traditional AlphaPose, M-AlphaPose significantly reduces computational complexity and parameter count while maintaining high detection accuracy, achieving an AP⁵⁰ score of 0.921.