# Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene
## 4 Experiments
### Evaluation Result  
#### 4.2.1 Implement Detail  
This paper used two Nvidia GeForce GTX 1080 Ti graphics cards to parallel train the model during the training process; the deep learning framework used was PyTorch1.6. The original learning rate was 0.1, the CosineAnnealing method was used to adjust the learning rate, and batch_size was set as 256. Finally, we used the information cross-entropy loss function to perform deviation iterative calculation specifically to instantiate loss function in each module and summarize all the loss. In the positive iteration process, to avoid contingency, we chose training samples randomly and made the skeleton frame data of \begin{table}
\begin{tabular}{c c} \hline \hline
**Condition** & **Settings** \\ \hline People & Eight volunteers differing in age, gender, and height \\ Light conditions & Four light conditions: in the morning, noon, late afternoon, and evening \\ Scene & Four scenes: with pedestrians, no pedestrian, T-junction, and criss-cross crossing \\ Clothes & Two kinds of clothes: summer clothes and winter clothes \\ Cameras & Five cameras from different angles \\ Gesture & Eight kinds of standard traffic police gesture as the national standard \\ Distance & 5 m, 70 m \\ Total amount & 20,480 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The condition settings of data collection. Figure 13: Part of the Kinetics–Skeleton dataset. As shown in Table 2, the first set of data are related data of the base network, which this paper is based on. The second set of data verify the recognition efforts of different modules related to gestures used by traffic police. The third set of data combine different modules, and the fourth set of data show the effort of the final solution proposed in this paper. Table 2 shows that networks that combine the CBAM, AGS, and TAS modules separately are better than the base network; the top-1 improved by 5.03%, 4.56%, and 7.54%, respectively. The experiment shows that the introduction of the CBAM can overcome the data imbalance distribution problem on the feature level to a certain extent, which readjusts the weight of different joints, enhances the effective features of gestures used by traffic police, and inhibits the less-effective or non-effective features. The AGS is not limited to the physical structure of humans; it can self-learn the potential relationship between skeleton joints, which improves the accuracy of traffic police gesture recognition effectively. The TAS module shows outstanding performance among all modules, because gestures used by traffic police consist of a series of actions and it can extract the trajectory features between actions, enriching the features of traffic police g \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **ST-GCN** & **CBAM** & **AGS** & **TAS** & **Top-1 (\%)** & **Top-3 (\%)** \\ \hline
1 & \(\surd\) & & & & 79.16 & 91.72 \\ \hline
2 & \(\surd\) & \(\surd\) & & & 84.19 & 94.6 \\
3 & \(\surd\) & & \(\surd\) & & 83.72 & 94.6 \\
4 & \(\surd\) & & & \(\surd\) & 86.7 & 95.72 \\ \hline
5 & \(\surd\) & \(\surd\) & \(\surd\) & & 86.05 & 94.6 \\
6 & \(\surd\) & & \(\surd\) & \(\surd\) & 86.5 & 95.44 \\
7 & \(\surd\) & \(\surd\) & & \(\surd\) & 86.7 & 95.72 \\ \hline
8 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 87.72 & 95.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The accuracy of traffic police gesture test set. Figure 16: The trend of loss on temporal dimension. Figure 17: The trend of accuracy on temporal dimension. results also verify the fact that information in the temporal dimension is critical in traffic police gesture recognition; while performing an ablation study on different modules, we found the accuracy of all module combinations to be improved, which further proves the effectiveness of every module. The method proposed in this paper had the highest accuracy among all, improving the top-1 by 8.56%. Eight kinds of gestures used by traffic police consist of different actions, including repetitive actions and one-time actions.