## 4 Experimental Study
### Discussion  
Among the methods we propose, the STN and attention mechanism are two core innovations that play a key role in action recognition in industrial workflows. In this section, we review the mechanisms of these two parts and their impact on the results.  
As described in Section 3.3, the STN is used to adaptively adjust the pose and size of the humans in the skeleton images. Taking the operator in Figure 5 as an example, when performing the spraying gelcoat operation, he needs to face the fan blade or walk back and forth along the blade, which causes the position of the keypoints on the human body to be in a rotating state in the space, which makes it difficult to extract spatial and temporal information using the GCN. By the same token, when the operator is at the top of the blade and the bottom of the blade, different sizes will appear in the video. Without the adjustment of the STN, the same action can lead to different spatial information, resulting in erroneous recognition.  
We also experimentally tested the effect of removing the STN on the results, as shown by our method without STN in Table 4. Using STN significantly improved the performance of our method by  
Figure 5: Visualization of the results of the method we proposed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **iDT+SVM [42]** & **Two-Stream [16]** & **C3D [43]** & **ST-GCN [28]** & **R(2+1)D [44]** & **Ours** \\ \hline False recognition & \multirow{2}{*}{36.5\%} & \multirow{2}{*}{32.5\%} & \multirow{2}{*}{21.33\%} & \multirow{2}{*}{14.56\%} & \multirow{2}{*}{18.93\%} & \multirow{2}{*}{9.50\%} \\ rate (\%) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of field tests in real workshops.  
nearly 10%. Considering the different influences of different keypoints of the human images on the action recognition results in the actual production environment, the attention mechanism is necessary for accurate motion recognition. As also shown in Figure 5, we visualize the weights of different keypoints contributing to the final result, and use the shadow size near the keypoints to indicate the difference in weight. Our method considers that the action in the video belongs to the spraying gelcoat category, which is mainly determined by the keypoints on the right hand of the operator.  
The attention mechanism automatically assigns different weights to different keypoints to make our model more in line with actual industrial production. We also tested the effect of removing the attention mechanism on the results, as shown by our method without attention mechanism in Table 4.  
In addition, we also study the effect of pose estimation on the results. As shown in Table 6, we compared the accuracy of using different pose estimation methods and the impact on action recognition results. The experimental results show that different pose estimation methods have an impact on the accuracy of constructing the skeleton image; however, with the use of dropout and other methods in subsequent models, our model is robust to skeleton data and is not susceptible to subtle changes. Therefore, when the accuracy difference is small, we choose the fastest processing method to recognize the action in the video more quickly. In the industrial workflow, our method leaves a certain margin for the difference in performance of the same kind of operation, which also makes our method more suitable for actual production.