SimCC coordinate based learning of human pose constraint information

Abstract

This paper proposes a novel approach to improve the accuracy of human pose estimation, which is a technique to predict the coordinates of human body keypoints in an input image. Existing methods often focus on keypoint localization without considering their positional relationships. We introduce two regularization terms based on SimCC coordinates: Bone loss and Sum loss of visible keypoints. They constrain the spatial relationship and visible number of keypoints respectively. To better leverage these loss functions, we enhance the deconvolution module with a combination of convolution and bilinear interpolation, which effectively recovers image resolution and preserves more feature map details. We conduct a comprehensive evaluation of our approach on the COCO dataset. Our approach enhances the accuracy of the top-down method by incorporating HRNet-W48. We achieve 77.7AP on the COCO validation set, which is competitive with state-of-the-art methods.