High-Resolution Human Keypoint Detection: A Unified Framework for Single and Multi-Person Settings
Yuhuai Lin, Kelei Li, Haihua Wang · Algorithms · 2025
Human keypoint detection has become a fundamental task in computer vision, underpinning a wide range of downstream applications such as action recognition, intelligent surveillance, and human–computer interaction. Accurate localization of keypoints is crucial for understanding human posture, behavior, and interactions in various environments. In this paper, we propose a deep-learning-based human skeletal keypoint detection framework that leverages a High-Resolution Network (HRNet) to achieve robust and precise keypoint localization. Our method maintains high-resolution representations throughout the entire network, enabling effective multi-scale feature fusion, without sacrificing spatial details. This approach preserves the fine-grained spatial information that is often lost in conventional downsampling-based methods. To evaluate its performance, we conducted extensive experiments on the COCO dataset, where our approach achieved competitive performance in terms of Average Precision (AP) and Average Recall (AR), outperforming several state-of-the-art methods. Furthermore, we extended our pipeline to support multi-person keypoint detection in real-time scenarios, ensuring scalability for complex environments. Experimental results demonstrated the effectiveness of our method in both single-person and multi-person settings, providing a comprehensive and flexible solution for various pose estimation tasks in dynamic real-world applications.