Traffic Control Gesture Recognition Based on Masked Decoupling Adaptive Graph Convolution Network

To promote the application of visual pattern recognition technology in the field of intelligent transportation systems. In this paper, we proposed a novel method, i.e., masked decoupling adaptive graph convolution network (MDe-GCN), which can be utilized to classify traffic control gesture signals. Based on the adaptive graph convolution network, the decoupling graph generation method was adopted to obtain channel-by-channel adaptive topology and introduce spatial/temporal attention mechanisms. Besides, this study innovatively proposed a regularized training method for adaptive graph convolutional networks by generating random graph masks, which can cut off the information transfer between some joint pairs during training. In the experiment, a Chinese traffic control gesture dataset (CTCGD) was collected from filming three actors performing eight traffic control gestures. Through the test, MDe-GCN achieved an action classification performance exceeds other GCNs, with an accuracy of 90.71% in CTCGD.