Improving Occluded Human Pose Estimation Via Linked Joints

Keypoint heatmaps, which produce peak values of Gaussian distributions for individual human joints, are crucial components in 2D human pose estimation. However, existing merits using keypoint heatmaps are usually defeated in body occlusion, resulting in inaccurate joint predictions. We consider that the failure is mainly due to keypoint heatmaps’ insufficiency for distinguishing the joints from two occluded bodies. Therefore, in this paper, we propose a method termed SkeletonMap (SMap), which introduces the prior knowledge of body structure to constrain relative connection of joints. As an extension of keypoint heatmaps, SMap can be efficiently plugged into existing 2D human pose estimation models with negligible increase in computational cost. Extensive experiments are conducted to show the effectiveness and generalization of SMap. Without bells and whistles, SMap brings a significant performance boost to the existing heatmap-based 2D human pose estimation models. On the MPII dataset, SMap improves SimpleBaseline (ResNet-152) from89.7 PCKh@0.5 to 90.4, and HRNet (W32) from 90.5 to 90.9. On the COCO dataset, SMap improves SimpleBaseline (ResNet-152) from 72.4 AP to 73.9. On the more challenging OCHuman dataset, SMap improves HRNet (W32) from 61.9 AP to 64.5, achieving 2.6 AP gains. We hope our simple and efficient approach will serve as a solid component for future research in 2D human pose estimation.