# Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene
## 5 Conclusions  
In this paper, we used skeleton data of traffic police to construct graph structure data as an input source of the model; using the ST-GCN as a base network, the spatial-temporal features of gestures used by traffic police were extracted, and finally, gestures used by traffic police were recognized. As the ST-GCN has a disadvantage in that it only learns the physical structure of the human body but ignores the potential relationship between joints in frames, we proposed the AGS to learn the potential relationship between nodes of traffic police skeletons. Based on the fact that the importance levels of nodes are different in different traffic police gestures, the attention mechanism was introduced to strengthen the weight of key nodes, to inhibit the nodes with little information volume or no information volume, and to improve the accuracy of traffic police gesture recognition. Another disadvantage is that joint feature information in the temporal dimension of the same joint in frames is too simple. This paper proposed the TAS to solve this, in which features in the temporal dimension are enriched by connecting the same joints in different frames to the same joints in one frame, which improves the accuracy of recognition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline Feature Encoding [39] & 14.9 & 25.8 \\ Deep LSTM [40] & 16.4 & 35.3 \\ Temporal Conv [41] & 20.3 & 40.0 \\ ST-GCN [17] & 30.7 & 52.8 \\ Ours-Conv-Chiral [42] & 30.9 & 52.6 \\ \hline Ours & 32.11 & 54.31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Benchmark comparison for Kinetics–Skeleton model.  
The method proposed in this paper can only recognize gestures used by traffic police in simple scenes; in the future, we will continue to study traffic police gesture recognition methods that can be applied in complex scenes, which means we should extend the existing traffic police gesture dataset we established and locate and detect traffic police in complex scenes.  
Conceptualization, K.L. and Y.Z.; methodology, K.L. and Y.Z.; software, J.Y.; validation, Y.Z. and J.Y.; formal analysis, K.L.; investigation, Y.Z. and H.Z.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, K.L. and H.B.; visualization, H.Z.; supervision, H.B.; project administration, K.L.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Key Project of National Nature Science Foundation of China under grant 61932012 and in part by Natural Science Foundation of Shanxi under 201901D111467.  
Not applicable.  
The data presented in this study are available from the corresponding author upon request. The data are not publicly available due to copyright. Please submit a formal application to the corresponding author for any citations.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CBAM & Convolutional block attention module \\ GCN & Graph convolutional networks \\ GNN & Graph neural network \\ LSTM & Long short-term memory \\ ST-GCN & Spatial-temporal graph convolutional network \\ TAS & Temporal attentions mechanism \\ AGS & Adaptive graph structure \\ \end{tabular}