# Comparative Analysis of Skeleton-Based Human Pose Estimation
## 1 Introduction  
Human pose estimation (HPE) aims to locate all of the human body parts from input images or videos. Nowadays, HPE has become a popular task in the field of computer vision. It is widely used in video surveillance [1; 2; 3; 4; 5; 6], medical assistance [7; 8; 9; 10; 11; 12; 13; 14; 15], and sport motion analysis [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Human keypoints are used to classify the poses and measure the correctness of poses in these applications. Using an intelligent video surveillance system, the human keypoints can be extracted from human body parts to classify poses between kidnapping and child abuse cases. In the aspect of medical assistance, detected keypoints from body parts can be used to evaluate the correctness of postures for physiotherapy exercises, fall detection, and in-home rehabilitation. In addition, the performance and correctness of an athlete's movements can be evaluated by comparing the detected keypoints from body parts with reference poses (ground truth). HPE can be classified into two-dimensional (2D) HPE and three-dimensional (3D) HPE. It can also be classified into single-person HPE and multi-person HPE based on the number of people captured in the input image. In both single-person and multi-person HPE, it can be further classified into top-down and bottom-up methods, based on the ways of detecting the skeleton keypoints [29]. This paper focuses on a comparative study of 2D single-person HPE. As the demand for HPE increases, many skeleton-based HPE algorithms have been developed and packaged into libraries to provide ease of use for researchers. The performance of these HPE libraries is important to ensure the reliability of the different practical applications for which they are integrated. For instance, when the HPE library is applied to an in-home rehabilitation system, it needs to accurately detect the poses of patients freely performing rehabilitation poses in different home environments to ensure the reliability ofthe application. This situation is even more complicated when common challenges, such as inappropriate camera position and self-occlusion [11; 20; 22; 23], affect skeleton keypoint detection. Recently, four state-of-the-art HPE libraries have been applied in various applications, namely PoseNet [30], MoveNet [31], OpenPose [32], and MediaPipe Pose [33]. Table 1 shows a list of applications in different domains that have utilized these four HPE libraries in the last 5 years. The four HPE libraries face two common challenges in pose estimation, including inappropriate camera position and the self-occlusion effect. Even though each HPE library uses different approaches to overcome these challenges, the strengths and weaknesses of these four existing HPE libraries have yet to be discovered. Therefore, the comparative performance of these libraries should be carried out to investigate their robustness in detecting different human poses. This paper aims to compare the performance of these four state-of-the-art HPE libraries for human pose detection and analyze the strengths and weaknesses of each HPE library. Hence, a comparative analysis of these four HPE libraries based on images and videos was carried out. To the best of the authors' knowledge, this paper is the first attempt to compare and analyze the performance of PoseNet, OpenPose, MoveNet, and MediaPipe Pose using both image and video datasets. The rest of this paper is organized as follows. Section 2 reviews the existing comparative analysis of the different HPE libraries and summarizes the functionalities of PoseNet, OpenPose, MoveNet, and MediaPipe Pose. The methodology used to evaluate the performance of each HPE library is presented in Section 3. Next, Section 4 provides the