# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 6 Conclusions  
In this paper, we proposed a new bottom-up multi-person pose estimation method which strikes a balance between efficiency and accuracy. The grouping of candidate joints into a corresponding pose in a limited amount of time is the main challenge in bottom-up multi-person pose estimation. To solve this problem, we first introduced Partition Pose Representation (PPR) for multi-person pose estimation. PPR builds relationships between each joint and the corresponding instance of a person using the offset between the joint and the body center. Moreover, PPR further divides the human body into five constituent parts and utilizes another offset to the center of these parts to rebuild relationships between adjacent joints. With PPR, it is possible to group candidate joints simply and quickly without the need for any additional complex algorithms.  
To leverage the advantages of PPR, we proposed the Partitioned CenterPose (PCP) Network to estimate instances of people and their body joints, PCP then groups all body joints by joint offset. By considering the different characteristics of the offsets of joints on different parts of the human body, we proposed an improved \(l_{1}\) loss to enhance the accuracy of the predicted joint offsets. Extensive experiments and subjective evaluation of predictions on the COCO and CrowdPose datasets demonstrate that our method performs well both in terms of efficiency and prediction accuracy. A future study that extends PPR to 3D human pose estimation is planned. Considering the complexity of human poses in 3D space, we must reconsider how we define the center of the human body and design different loss functions to obtain more accurate offsets.  
Conception and design of the proposed method: [PERSON] and [PERSON]; performance of the experiments: [PERSON]; writing of the paper: [PERSON]; paper review and editing: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the NRF of Korea funded by the Ministry of Education (GR 2019R1D1A3A03103736). This research was also conducted by the 'Leaders in Industry-university Cooperation +\({}^{\prime}\), supported by the Ministry of Education and National Research Foundation of Korea  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Backbone** & \begin{tabular}{c} **Input** \\ **Size** \\ \end{tabular} & **AP** & \begin{tabular}{c} **AP\({}^{0.5}\)** \\ \end{tabular} & \begin{tabular}{c} **AP\({}^{0.75}\)** \\ \end{tabular} & \begin{tabular}{c} **AP\({}^{\mathbf{E}}\)** \\ \end{tabular} & \
\begin{tabular}{c} **AP\({}^{\mathbf{M}}\)** \\ \end{tabular} &
\begin{tabular}{c} **AP\({}^{\mathbf{H}}\)** \\ \end{tabular} \\ \hline \multicolumn{8}{c}{Top-down methods} \\ \hline Mask-RCNN [5] & ResNet-101 & - & 0.572 & 0.835 & 0.603 & 0.694 & 0.579 & 0.458 \\ AlphaPose [11] & - & - & 0.610 & 0.813 & 0.660 & 0.712 & 0.614 & 0.511 \\ SPPE [38] & ResNet-101 & - & 0.660 & 0.842 & 0.715 & 0.755 & 0.663 & 0.574 \\ \hline \multicolumn{8}{c}{Bottom-up methods} \\ \hline CMU-Pose [12] & - & - & - & - & - & 0.627 & 0.487 & 0.323 \\ HigherHRNet [16] & HRNet-W48 & 640 & 0.659 & 0.864 & 0.706 & 0.733 & 0.665 & 0.579 \\ HigherHRNet * [16] & HRNet-W48 & 640 & **0.676** & **0.874** & **0.726** & **0.758** & **0.681** & **0.589** \\ Ours (HRNet) & HRNet-W32 & 512 & 0.657 & 0.855 & 0.705 & 0.742 & 0.668 & 0.574 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of our model to other state-of-the-art models on the CrowdPose test dataset. Superscripts E, M, H of AP stand for easy, medium and hard. * indicates multi-scale testing. The highest values are indicated in bold.