## 4 Experimental Study
### Experimental Results  
To evaluate the performance of the human detection and pose estimation models, we extracted 1200 frames from the videos in train set, including 200 per operation. These images contain a total of 1316 human images, and we manually annotate 18 keypoints of the humans according to the format of the Microsoft COCO dataset. The evaluation results of human detection are shown in Table 2. Here we consider the effect of different thresholds on the test results. The _Precision_ in the test results is obtained by calculating the ratio of the correctly detected quantity to the total predicted quantity. The _Recall_ is obtained by calculating the ratio of correctly detected to 1316 people in these images. F-measure and G-mean are computed based on Equations (9) and (10). \[F-\text{ measure }=\frac{2\times\text{ Precision }\times\text{Recall}}{\text{ Precision }+\text{Recall}} \tag{9}\] \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Threshold** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **G-Mean (\%)** \\ \hline
0.15 & 73.80 & 99.54 & 84.76 & 85.71 \\
0.2 & 76.68 & 99.47 & 86.60 & 87.34 \\
0.25 & 78.16 & 99.01 & 87.36 & 87.97 \\
0.3 & 88.62 & 98.78 & 93.42 & 93.56 \\
0.35 & 93.64 & 98.40 & 95.96 & 95.99 \\
0.4 & 95.34 & 97.87 & 96.59 & 96.60 \\
0.45 & 95.92 & 91.19 & 93.49 & 93.52 \\
0.5 & 96.56 & 76.75 & 85.52 & 86.09 \\
0.55 & 97.86 & 72.95 & 83.59 & 84.49 \\
0.6 & 98.08 & 69.91 & 81.63 & 82.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the human detection (considering different thresholds). \[G-mean=\sqrt{\text{Precision}\times\text{Recall}}. \tag{10}\]  
After the humans in images are detected, we evaluate the results of the pose estimation. When the predicted keypoints and the corresponding manually annotated keypoints are within a certain distance range, and the connection between the points is correct, the pose estimation is considered correct. Table 3 shows the results of the pose estimation of each operation. We also evaluate the performance of the entire model in the recognition of the actions and compare it with the existing research. As shown in Table 4, the video used in the test is derived from real industrial production, and the mainstream methods in the field of action recognition are used for comparison. Figure 4 shows the confusion matrix of the results of our method on the test data. At the same time, we visualize the results in Figure 5, showing the specific implementation process and corresponding results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Blasting** & **Spraying** & **Laying** & **Pumping** & **Plastering** & **Using Remote** & **Total (\%)** \\  & **Sand (\%)** & **Gelccat (\%)** & **Materials (\%)** & **Gas (\%)** & **Adhesive (\%)** & **Controller (\%)** & **Total (\%)** \\ \hline iDT+SVM [42] & 75.26 & 83.51 & 72.16 & 80.41 & 82.47 & 79.38 & 76.50 \\ Two-stream [16] & 82.47 & 85.57 & 83.51 & 77.32 & 78.35 & 91.75 & 80.83 \\ G3D [43] & 85.57 & 90.72 & 90.72 & 90.72 & 87.63 & 61.86 & 82.17 \\ ST-GCN [28] & 85.57 & 92.78 & 92.78 & 91.75 & 88.66 & 87.63 & 88.33 \\ R(2+1)D [44] & 88.66 & 93.81 & 93.81 & 91.75 & 90.67 & 92.78 & 90.67 \\ \hline Ours & 92.78 & 95.88 & 94.85 & 93.81 & 89.69 & 96.91 & 91.17 \\ W/o attention & 85.57 & 86.60 & 87.63 & 88.66 & 89.69 & 90.72 & 88.50 \\ W/o STN & 82.22 & 88.92 & 84.79 & 85.05 & 85.57 & 80.41 & 81.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of our methods and existing methods on test data. Figure 4: Confusion matrix of the results of our proposed action recognition method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Blasting** & **Spraying** & **Laying** & **Pumping** & **Plastering** & **Using Remote** & **Autroller** & **Average** \\  & **Sand** & **Gelccat** & **Materials** & **Gas** & **Adhesive** & **Controller** & **Average** \\ \hline Accuracy & 96.5\% & 92.5\% & 91.0\% & 93.5\% & 93.0\% & 90.5\% & 93.6\% \\ \hline \hline \end{tabular}
\end{table}