## 4 Results and Discussion
### Performance between Multiple Datasets  
The accuracy of the 17 human keypoints predicted by the five models on Microsoft COCO 2014 is shown in Table 3, while Table 4 lists the accuracy of the 15 human keypoints predicted by them on the CMU Panoptic test set (as the front image of the ankle joint in the CMU Panoptic dataset is not visible, the final prediction result has 15 keypoints). The last columns of Tables 3 and 4 show the average accuracy of the predictions of the keypoints. It can be observed that the prediction accuracy of the basic model's keypoints is significantly lower than that of the other four models. The introduction of an anchor-pose-based regression module for the design and use of anchor frames has long been considered an essential part of high-precision object detection (Faster RCNN/SSD/YOLOv2&v3). The anchor box represents the initial state of the candidate target, and is an effective method for obtaining the potential distribution area of the target [37, 38]. By combining a feature pyramid network (FPNet) and anchor-box-based detectors (FPN/RetinaNet/RefineDet), the accuracy of the method was improved to new heights [43, 44], which in turn improved the prediction accuracy of each keypoint. The accuracy of the model directly projected from a 3D pose to a frontal 2D pose was slightly higher than that of adding an anchor pose. The introduction of a module that integrates 3D pose data inputs has improved the basic model, especially at keypoints such as the left ear, right ear, left shoulder, left crotch, and right crotch. The average accuracy of predicting key points is the highest when introducing a regression module based on the anchor pose and a module fused with 3D pose data input, with only a few keypoints showing little decrease. Overall, the FPNet has the highest prediction accuracy, but there is still room for improvement [45]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Joint Point**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**2D-3D Pose**} & \multicolumn{2}{c}{**Base +**} & \multirow{2}{*}{**Base + Anchor**} & \multirow{2}{*}{**Our Method**} \\  & & & **Anchor Pose** & & & \\ \hline Nose & 22.2 & 55 & 60.1 & 61.7 & 67.2 \\ Left eye & 20.1 & 56.9 & 58.2 & 58.9 & 70.5 \\ Right eye & 22.5 & 49.7 & 54.8 & 56.8 & 64.0 \\ Lest ear & 52.9 & 62.5 & 55.5 & 56.8 & 70.5 \\ Right ear & 35.1 & 58.9 & 56.5 & 55.5 & 56.1 \\ Left shoulder & 51.5 & 63.5 & 58.2 & 56.2 & 63.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of different models on the CMU Panoptic dataset (%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Joint Point**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**2D-3D Pose**} & \multicolumn{2}{c}{**Base +**} & \multirow{2}{*}{**Base + Anchor**} & \multirow{2}{*}{**Our Method**} \\  & & & **Anchor Pose** & & & \\ \hline Nose & 32.9 & 44.0 & 43.2 & 49.3 & 55.9 \\ Left eye & 38.6 & 46.7 & 45.6 & 50.9 & 58.6 \\ Right eye & 34.3 & 46.7 & 40.9 & 52.3 & 65.1 \\ Lest ear & 53.2 & 62.5 & 61.0 & 66.8 & 61.0 \\ Right ear & 49.9 & 63.9 & 60.9 & 68.9 & 65.5 \\ Left shoulder & 44.8 & 73.5 & 14.3 & 73.2 & 75.3 \\ Right shoulder & 45.0 & 73.5 & 1 Some typical qualitative examples of application of models on the Microsoft COCO 2014 dataset, as shown in Figure 7, exhibit significant attitude changes and scale variances. The first row represents the input human body image, the second row represents the annotated positive posture in red, and the blue row represents the output of the model. It can be observed that with the introduction of the anchor pose regression module and 3D pose fusion module, the extracted frontal posture also had good expression for some backside human body images or side human body images with a large angle with the front side human body image. In addition, it performed well for images with missing data caused by human self-occlusion [46; 47]. However, there is still room for improvement in the prediction of some keypoints in the human body, particularly the wrists, ankles, and knees.