# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 2 Related Work  
### Multi-Person Pose Estimation  
Multi-person pose estimation is a comprehensive task that combines the challenges of person detection and keypoint estimation. With the incredible advancements over recent years in object detection and single-person pose estimation methods (2017; 2018; 2019; 2020; 2021; 2020; 2021; 2022),  
Figure 1: Different pose representations captured from image (a). (b) Traditional pose representation, in which each joint is represented by absolute coordinates. (c) Proposed partition pose representation.  
the performance of multi-person pose estimation has also improved, getting good results even on some complex datasets. Based on how calculations for a particular method are started, multi-person pose estimation methods are often divided into top-down methods and bottom-up methods.  
Top-down methods. Top-down approaches typically first use an object detector to obtain an instance of a person and then independently estimate the pose for each person identified. G-RMI [6] produces a heatmap and offset map for each joint before combining this information using an aggregation procedure. RMPE [11] introduced using a parametric pose NMS for refining pose candidates. [PERSON] et al. [5] proposed an extension of the Mask R-CNN framework that synchronously predicts keypoints and human masks. In these top-down methods, predicting keypoint heatmaps is made easier by restricting the search to the detected person's bounding box. However, the top-down strategy incurs extra computational costs while initially detecting each person's bounding box.  
Bottom-up methods. Bottom-up approaches first detect body joints and then assign these joints to individuals. With the increasing demands to carry out image processing tasks on mobile devices, finding appropriate lightweight methods has become a new research hotspot. Motivated by bottom-up approaches being faster and more capable of achieving real-time estimation, our approach is based on previous bottom-up approaches and aims to obtain better performance while maintaining high computational efficiency.  
Existing bottom-up methods mainly focus on how to associate detected keypoints with the corresponding instance of a person. The PersonLab approach [14] introduced a greedy decoding scheme together with Hough voting to determine grouping. [PERSON] [12] proposed Part Affinity Fields (PAFs) to encode the location and orientation of limbs, this work was further developed in the PifPaf technique [15]. However, the computational efficiency of these two-stage methods is limited by the quality of the greedy algorithm. [PERSON] et al. [13] propose a one-stage method to detect joints and group them in one pipeline. Based on this one-stage strategy and HRNet [8], [PERSON] et al. [16] presented a Scale-Aware High-Resolution Network (HigherHRNet) to solve the scale variation challenge. However, existing research only focuses on the features of joints (like in PifPaf), or only uses the connection between joints and an instance of a person to cluster (like in AssocEmbedding and HigherHRNet). The novelty of our method is to use Partition Pose Representation (PPR) to combine position information from instances of a person with structure information about body joints. In PPR, we utilize tailored semantic information and information on the offset of joints from the body center to replace information from tags in associative embedding maps. Moreover, we divide the human body into five parts, define the pivot joint in these parts as the part's center. Assisted by these part centers, the relationships between different joints in a single limb become enhanced by the offset of the body joint to the part center.