Partitioned Centerpose Network For Bottom-Up Multi-Person Pose Estimation

In bottom-up multi-person pose estimation method, grouping joint candidates into corresponding person instance is a challenging problem. In this paper, a new bottom-up method, Partitioned CenterPose (PCP) Network, is proposed to better cluster all detected joints. To achieve this goal, a novel Partition Pose Representation (PPR) is proposed which integrate person instance and body joint by joint offset. PPR leverages the center of human body and the offset between center point and body joint to encode human pose. To better enhance the relationship of body joints, we divide human body into five parts, and generate sub-PPR in each part. Based on PPR, PCP Network can detect persons and body joints simultaneously, and then grouping all body joints by joint offset. Moreover, an improved l1 loss is designed to obtain more accurate joint offset. On the COCO keypoints dataset, the proposed method performs on par with the existing state-of-the-art bottom-up method in accuracy and speed.