Multi-Person Pose Estimation with LIMB Detection Heatmaps

Human pose estimation for multiple people is more challenging than single-person case because of the unknown number of people in an image and occlusion between people. In this paper, we present a generic bottom-up approach for multi-person pose estimation. We introduce limb detection heatmaps as a representation of body joint pairs association, which are simultaneously learned with joint detections. The bottom-up inference is formulated as a set of bipartite graph matching. Our approach can be easily incorporated with any single-person pose estimator that produces joint detection heatmaps with minimal modification. Moreover, we adopt focal loss to address the data imbalance in human pose estimation. Promising results are achieved on the MPII benchmark.