# Chinese Traffic Police Gesture Recognition Based on Graph Convolutional Network in Natural Scene
## 2 Related Work  
### Traffic Police Gesture Recognition  
The worldwide use of traffic police gesture recognition technology can roughly be divided into two kinds of methods: methods based on external devices and methods based on computer vision. Methods based on external devices collect gesture features by placing sensor equipment on traffic police's bodies to collect traffic information regarding actions used in gestures. For example, Ref. [6; 7] both collected important information such as the motion trail of traffic police's arms and the position of hands, etc., through accelerometers, then recognized the actions of gestures used by traffic police according to these. Traffic police gesture recognition methods based on computer vision are noncontact methods which do not require equipment to be worn and are convenient. Literature [8; 9] has proposed a method that can be used to recognize gestures used by traffic police in complex environments. First, dark channel prior and kernel density estimation are used to extract the torso and arms of the police as the foreground region. Then, the coordinates of a pixel in the upper arms and forearms of traffic police are determined by the max-covering scheme method. Finally, the pose and action of the traffic police are recognized through the geometrical relationship of the rotation joint. [PERSON] et al. [10] proposed a five-part body model to recognize gestures used by traffic police in complex scenes; it differed greatly from the former methods which required a pretraining process or 3D measuring equipment to construct a human body model. They used the max-covering scheme to learn the five-part body model automatically. Literature [11; 12] has proposed a method based on cumulative block intensity vector (CBIV) using the n-frame cumulative difference to collect the features of traffic police.  
Compared with the action recognition methods based on video image data, skeleton-data-based action recognition technology has strong robustness when used in complex, dynamic scenes. Using sensors such as Kinect (2012) [13], ASUS Xtion PRO LIVE (2011) RGB-D cameras [14], etc., skeleton data can be collected easily. [PERSON] et al. [15] used Kinect2.0 to collect data from 10 volunteers and constructed a traffic police command gesture skeleton joint dataset. They used convolutional operation to analyze the location change of skeleton joints to extract features in the temporal dimension and extract spatial features by analyzing the relative position of skeleton nodes at the same time. The extracted spatial-temporal features of gestures used by traffic police were used to train the ST-CNN model and achieve the recognition of gestures used by traffic police. [PERSON] et al. [16] constructed a traffic police gesture model based on joints and skeletons after analyzing the hinged feature of gestures used by traffic police. Furthermore, a convolutional pose machine (CPM) was introduced to extract the key joints of gestures used by traffic police to collect the relative length of the skeleton in gestures used by traffic police and its angle between acceleration of gravity as the spatial context feature. Meanwhile, long short-term memory (LSTM) was used to extract the features of gestures used by traffic police in temporal series. At last, the spatial features and temporal features were integrated to recognize gestures used by traffic police.  
As the skeleton joint graph has the characteristic of graph structure data, the ST-GCN [17] was the first to use the GCN model to collect skeleton data concerning spatial-temporal features in a human skeleton topology graph, which achieved the aim of recognizing human actions. Most of the following recognition models were designed and modified based on the GCN model, for example, actional-structural graph convolutional networks (AS-GCNs) [18], two-stream adaptive graph convolutional networks (2S-AGCNs) [19], and channelwise topology refinement graph convolution (CTR-GCN) [20].