# Comparative Analysis of Skeleton-Based Human Pose Estimation
## 4 Experiment Results
### Video Dataset
Figure 5 shows the video frame and detection results of Action 8, where the two challenges, self-occlusion and inappropriate camera position, are absent. Hence, the performance of HPE should be better when there are less challenges affecting keypoint detection. On the other hand, the performance of HPE is reduced when there are more challenges. MediaPipe Pose showed the poorest performance in Action 12 (squat); OpenPose and PoseNet showed the worst performance in Action 2 (bench press); while MoveNet showed the poorest performance in Action 4 (bowling). Figures 6-8 show the sample video frames and detection results of each HPE library for Actions 12, 2, and 4, respectively. In addition, the PDJ values for each body part in each action were also calculated. Frequent changes in keypoint positions also affects the performance of keypoint detection. In Actions 1, 3, 4, 6, 13, and 14, the PDJ values for elbows and wrists in all the tested libraries were lower than those of other body parts, as shown in Figures 9-11. This was due to frequent changes in keypoint positions in the elbows and wrists compared to other body parts. For instance, the person who plays bowling only needs to use his elbow and wrist to release the ball to the bowling lane, hence showing fewer changes of movement in other body parts. Likewise, the performance of OpenPose (blue line) showed the lowest PDJ among these actions, while the fluctuations in PDJ values between other HPE libraries were relatively small. Figure 8: Sample video frame and detection result of Action 4 (bowling). Green lines indicate the ground truth, red lines indicate the tested HPE library. Figure 7: Sample video frame and detection result of Action 2 (bench press). Green lines indicate the ground truth, red lines indicate the tested HPE library. Figure 9: PDJ for each body part in Action 1-baseball pitch. When the common challenges (self-occlusion and inappropriate camera position) occurred in the videos, the performance of keypoint detection was affected in all HPE libraries. Figure 12 shows the video frames of Action 9 (pull up), showing the self-occlusion effect in the action. In this action, all libraries showed much lower performance in some of the body parts compared to that of other body parts due to this self-occlusion effect. The corresponding PDJ values are reported in Figure 13. The PDJ values for ankles (black dotted box) were much lower than other parts because of self-occlusion from the crossed ankles, as shown in Figure 14. OpenPose (blue line) achieved the lowest performance for all body parts, particularly for elbows and wrists. Additionally, the performance of MoveNet for the ankles was lower than that of MediaPipe Pose and PoseNet when facing the self-occlusion effect. In addition, inappropriate camera position is one of the common challenges for human pose estimation. In Action 7 (jump rope), the camera was placed on the right side of the person and the video frames were recorded from the right side, as shown in Figure 4:0 Hence, self-occlusion occurred to the left body parts, which reduced the performance of keypoint detection for the left body parts. Figure 16 clearly shows that the performance of all HPE libraries for the right body parts was higher than that for the left body parts, where the PDJ values for the right shoulder and right elbow were always higher than those for the left shoulder and left elbow. Figure 4:1 (**a**) Frame 4 of Action 9 (pull up). In (**b**–**e**), green lines indicates the ground truth while red lines indicates the tested HPE library. Figure 4:2 PDJ for each body part in Action 9–pull up. Figure 4:3 Sample video frames of Action 7 (jump rope). In the overall comparison, MoveNet achieved the highest PDJ values in terms of minimum, first quartile, and third quartile values compared to the other HPE libraries. The PDJ values for MoveNet in all actions were more than 50%. MediaPipe Pose achieved the highest PDJ values in terms of maximum and median values.