Improving Robustness for Pose Estimation via Stable Heatmap Regression

• URL: http://arxiv.org/abs/2105.03569v1
• Date: Sat, 8 May 2021 03:07:05 GMT
• Title: Improving Robustness for Pose Estimation via Stable Heatmap Regression
• Authors: Yumeng Zhang, Li Chen, Yufeng Liu, Xiaoyan Guo, Wen Zheng, Junhai Yong
• Abstract summary: A heatmap regression method is proposed to alleviate network vulnerability to small perturbations. A maximum stability training loss is used to simplify the optimization difficulty. The proposed method achieves a significant advance in robustness over state-of-the-art approaches on two benchmark datasets.
• Score: 19.108116394510258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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