# Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene
###### Abstract  
For an automated driving system to be robust, it needs to recognize not only fixed signals such as traffic signs and traffic lights, but also gestures used by traffic police. With the aim to achieve this requirement, this paper proposes a new gesture recognition technology based on a graph convolutional network (GCN) according to an analysis of the characteristics of gestures used by Chinese traffic police. To begin, we used a spatial-temporal graph convolutional network (ST-GCN) as a base network while introducing the attention mechanism, which enhanced the effective features of gestures used by traffic police and balanced the information distribution of skeleton joints in the spatial dimension. Next, to solve the problem of the former graph structure only representing the physical structure of the human body, which cannot capture the potential effective features, this paper proposes an adaptive graph structure (AGS) model to explore the hidden feature between traffic police gesture nodes and a temporal attention mechanism (TAS) to extract features in the temporal dimension. In this paper, we established a traffic police gesture dataset, which contained 20,480 videos in total, and an ablation study was carried out to verify the effectiveness of the method we proposed. The experiment results show that the proposed method improves the accuracy of traffic police gesture recognition to a certain degree; the top-1 is 87.72%, and the top-3 is 95.26%. In addition, to validate the method's generalization ability, we also carried out an experiment on the Kinetics-Skeleton dataset in this paper; the results show that the proposed method is better than some of the existing action-recognition algorithms. graph convolution network; attention mechanism; traffic police gesture recognition 2021 2(J3016 is the taxonomy for driving automation proposed by the Society of Automobile Engineers (SAE) International. It defines six levels of driving automation, ranging from no driving automation (Level 0) to full driving automation (Level 5). Therefore, the key to solving this problem is the creation of interaction cognition technology, which should aim to understand traffic police body language in road driving. It is hard to restore order with traffic signals and lights alone in emergency situations such as traffic jams and pedestrians' retrograde in junctions. These types of situations are dangerous to both humans and cars; therefore, flexible gestures used by traffic police are needed to help resume traffic flow. According to [4], under testing situations, driverless vehicles should have the ability to understand traffic command gestures. In other words, automated vehicles must recognize the gestures of traffic police correctly, and then, make a driving decision that fits the gesture given by police in real time. If the communication between automated driving vehicles and traffic police is unclear, or specifically, the vehicle cannot recognize the gestures of traffic police, this means the vehicle cannot make good decisions, which can cause accidents. The gestures given by Chinese traffic police are made and governed by the Ministry of Public Security of the People's Republic of China. According to \"Road Traffic Safety Law of the People's Republic of China\" [5] and its enforcement regulations, there are eight kinds of new traffic police gesture signals, as shown in Figure 1, which are stop, go straight ahead, turn left, turn left waiting, turn right, lane change, slow down, and pull over. The gestures used by traffic police are a set of normative gesture actions that are mainly actions of the upper part of the body. The gestures used by traffic police consist of a series of continuous actions; if one was to only distinguish gestures using their spatial features, this may lead to an incorrect interpretation. Figure 2 displays two examples of different gestures (stop and pull over); however, these two gestures have similar spatial features. On this occasion, temporal consistency also aids effective recognition.