Enhanced keypoint recognition framework via multi-scale feature characteristics

Keypoint recognition plays a critical role in various computer vision tasks, such as human pose estimation, action recognition, and behavior analysis. Despite significant advancement, existing methods often struggle with accurately detecting small-scale keypoints within complex environments and maintaining the structural integrity of human poses. Therefore, we introduce an enhanced keypoint recognition framework that leverages multi-scale feature characteristics. Specifically, it utilizes Multi-Scale Feature Attention (MSFA) module to fuse features from multiple scales, enabling more effective recognition of small and challenging keypoints. In addition, a structural consistency loss is proposed to ensure the proper alignment of keypoints. Extensive experiments conducted on the MPII Human Pose datasets demonstrate that our approach outperforms existing methods in terms of both accuracy and robustness. This proposed framework advances the state-of-the-art in keypoint recognition, offering a computationally efficient solution for real-world applications that require precise human pose estimation.