Deep learning methods have achieved excellent performance in pose estimation, but the lack of robustness causes the keypoints to change drastically between similar images. In view of this problem, a stable heatmap regression method is proposed to alleviate network vulnerability to small perturbations. We utilize the correlation between different rows and columns in a heatmap to alleviate the multi-peaks problem, and design a highly differentiated heatmap regression to make a keypoint discriminative from surrounding points. A maximum stability training loss is used to simplify the optimization difficulty when minimizing the prediction gap of two similar images. The proposed method achieves a significant advance in robustness over state-of-the-art approaches on two benchmark datasets and maintains high performance.
@article{arxiv.2105.03569,
title = {Improving Robustness for Pose Estimation via Stable Heatmap Regression},
author = {Yumeng Zhang and Li Chen and Yufeng Liu and Xiaoyan Guo and Wen Zheng and Junhai Yong},
journal= {arXiv preprint arXiv:2105.03569},
year = {2021}
}