# Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene
###### Abstract
Thus, spatial features and temporal features should both be considered in the recognition of gestures used by traffic police. Figure 1: Actions used in gestures used by Chinese traffic police. Frequently used traffic police gesture recognition methods can roughly be divided into contact methods, based on external devices, and noncontact methods, based on computer vision. Contact methods based on external devices can recognize gestures used by traffic police with high speed and precision, but in this method, traffic police officers need to wear extra sensors, and the operation process is cumbersome, which increases the workload of traffic police. In addition, the equipment is expensive, which also limits the promotion of the application of this kind of method. Noncontact traffic police gesture recognition methods based on computer vision mainly capture picture data or video data using vision sensor equipment such as high-definition cameras to recognize gestures used by traffic police through digital image processing. These types of methods have an advantage in that the recognition process can be carried out without traffic police having to wear any extra equipment. However, the existing traffic police gesture recognition methods based on computer vision simply compile gestures into simple sequence data or pseudo-images, which leads to the problem of the original features of the image data being lost. To summarize, there are two main issues present in existing traffic police gesture recognition technologies that should be addressed: one challenge is the construction of a traffic police gesture dataset, as there has been no complete and public traffic police dataset until now. The other challenge is the question of how to extract information regarding gestures used by traffic police effectively. As the urban road transport environment is unpredictable, the extraction of traffic police gesture features in video images may be influenced by many factors such as light conditions, road conditions, etc., which directly affect the efficiency of traffic police gesture recognition. Figure 2: Two actions with similar spatial features belonging to different gestures. To solve the problems outlined above, this paper designs a solution for use in the collection of a traffic police gesture dataset on real roads and established a dataset which is reasonable and valid. In addition, this paper proposes a traffic police gesture action technology based on the GCN. First, the skeleton joint data of traffic police images were obtained using the OpenPose algorithm. Then, an ST-GCN base network was used to collect the spatial-temporal features of gestures used by traffic police. As the skeleton map structure of the ST-GCN is predefined and only represents the physical structure of the human body, it cannot effectively characterize the logical association factors between gestures used by traffic police. Thus, this paper proposes the use of an AGS model to extract the associated feature information in the node data of the skeletons traffic police members. In addition, this paper proposes a TAS to extract the temporal sequence feature information regarding gestures used by traffic police in the temporal dimension. In addition, joints have different levels importance in different gesture actions. For gestures used by traffic police, the key joints of action mainly focus on the six joints of the arm, which leads to the problem of imbalanced distribution of information in the feature space. Thus, a spatial attention mechanism was introduced into the improved ST-GCN model, and the attention module was able to adjust the weight of key gesture joints dynamically, choose the effective gesture node feature information, and improve the accuracy of the recognition of gestures used by traffic police. This paper extracted features of gestures used by traffic police with an improved ST-GCN model and recognized actions in the gestures of traffic police. The contributions of this paper are as follows:  
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