Pose Estimation Network Based on High-Order Spatial Interactions

Human pose estimation is a crucial research area in computer vision. With the advancement of deep learning technologies, existing pose estimation models have achieved remarkable success in predicting human keypoints. However, when dealing with complex scenes such as severe occlusion, complex backgrounds, extreme poses, multi-scale variations, and lighting changes, these models still face challenges and their accuracy is often affected. To address this issue, this paper proposes an improved human pose estimation method based on HRNet, which significantly improves the performance of the model in complex scenes by introducing high-order spatial interaction and attention mechanisms. It employs recursive gated convolution and convolutional attention modules to enhance the model's ability to extract high-order spatial features. The experimental results show that the proposed method outperforms existing mainstream approaches on the COCO2017 dataset and achieves higher pose estimation accuracy.