# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 1 Introduction  
Driven by extensive research efforts, significant progress has been made in human pose estimation. The goal of human pose estimation is to obtain the posture of a human body from monocular images or videos. Pose estimation is a fundamental computer vision task providing vital information for many applications such as action detection and recognition [1], human tracking [2], and medical assistance among others [3]. With the rapid progress in deep learning technology, human pose estimation performance has improved greatly over recent years. However, finding a balance between efficiency and accuracy remains challenging. Multi-person pose estimation methods are generally classified based on their starting point for prediction as either top-down or bottom-up [4]. Top-down methods [5; 6; 7; 8; 9; 10; 11] first identify and localize instances of people using an existing person detector system and then conduct pose estimation for each person individually. Generally, top-down methods are effective since these methods profit from advances in person detectors. However, the computational cost of such methods linearly increases with the number of people in an image because single-person pose estimation must be carried out repeatably, in sequence, for each person in the image, as such, such methods are usually too slow to achieve real-time detection. In contrast, bottom-up strategies [12; 13; 14; 15; 16] first identify all the body joints in the entire image, then these joints are grouped into corresponding instances of people. Unlike top-down methods, bottom-up methods avoid higher joint detection and are more robust as the number of people in an image increase. In many cases, performance when clustering the joint candidates determines the final accuracy of detection. [PERSON] et al. [12] proposed the use of Part Affinity Fields (PAFs) to encode the coordinates and angles of limbs toassist in grouping joints into different people; this approach ignores the relationship between each body joint and instance of a person. [PERSON] et al. (2013) constructed associative embedding maps to tag each joint on the corresponding person pose. This method adds a link between each body joint and the corresponding instance of a person, however, it neglects information relevant to adjacent body joints. Consequently, it is difficult to simultaneously maintain relationships between different joints in a single limb and link each joint from the corresponding instance of a person. To overcome this issue, we first propose a novel pose representation technique, termed Partition Pose Representation (PPR), which combines the position information from instances of people and their body joints. Inspired by (2017), we first represent each instance of a person with a single point at the center of their bounding box. Then, the positions of body joints are encoded by their offset from the center point, as shown in Figure 1b. In this way, the relationship between adjacent body joints is severed. To maintain some correlation between adjacent body joints, we further divide the human body into five parts: the head, left arm, right arm, left leg, and right leg, we then extend PPR to sub-PPR for each part. The respective center points of each part are the nose, left elbow, right elbow, left knee, and right knee. With the addition of sub-PPR, human poses generate stable connections with their instance of a person, as shown in Figure 1c. To exploit the advantages of PPR, we introduce a new bottom-up model, the Partitioned CenterPose (PCP) Network, to identify the poses of multiple people. The PCP Network can simultaneously locate the position of an instance of a person and identify all joint candidates. Meanwhile, a parallel prediction branch in the PCP Network, called the offset prediction head, builds an associative embedding map to predict the offset for each body center. Here we introduce an improved \(l_{1}\) loss to obtain more accurate joint offset values.