Uncertainty prediction in human pose estimation based on Gaussian heatmap
Xiaonan Wu, Zengzhao Chen, Hai Liu · 2023
As a fundamental task in computer vision, human pose estimation has undergone in-depth and thorough research, but we found that the key point labeling uncertainty is rarely paid attention to, but this uncertainty will affect the detection performance. Motivated by this finding, this paper first uses the patch-adaptive loss to address the label ambiguity problem inherent in keypoint annotation. In addition, this paper combines the advantages of convolution and Transformer architectures to inject long-range and short-range information into the network. Experimental results on publicly available datasets demonstrate that our method improves the performance of human poses outperforming state-of-the-art methods and improves the robustness of the network.