Traffic Police Gestures Recognition Based on Graph Convolution with Height Layering Partitioning Strategy

Aiming at the needs for auto-driving vehicles to recognize continuous traffic police gestures,a graph convolutional traffic police gesture recognizer with the height layering partitioning strategy is pro-posed.Firstly,according to the natural,assist,self-connection spatial relationships and temporal associations between human parts,a spatial-temporal traffic gesture model is established.Secondly,the spatial-temporal convolutional network is introduced,and the height layering partitioning strategy is proposed,which uses the relative height differences of parts as labels and breaks the limitations of existing partitioning strategies on graph structure.Finally,the spatial mean layer output structure which retains the length of the temporal dimension is designed to adapt the many-to-many prediction pattern for recognizing continuous traffic police gestures.The experiments show that the proposed method significantly improves the recognizing perform-ance,the confusion rate between gestures is 0.1%and the Jaccard index surpasses comparison methods.