Traffic Police Gesture Recognition Based on Gesture Skeleton Extractor and Multichannel Dilated Graph Convolution Network

Abstract

Traffic police gesture recognition is important in automatic driving. Most existing traffic police gesture recognition methods extract pixel-level features from RGB images which are uninterpretable because of a lack of gesture skeleton features and may result in inaccurate recognition due to background noise. Existing deep learning methods are not suitable for handling gesture skeleton features because they ignore the inevitable connection between skeleton joint coordinate information and gestures. To alleviate the aforementioned issues, a traffic police gesture recognition method based on a gesture skeleton extractor (GSE) and a multichannel dilated graph convolution network (MD-GCN) is proposed. To extract discriminative and interpretable gesture skeleton coordinate information, a GSE is proposed to extract skeleton coordinate information and remove redundant skeleton joints and bones. In the gesture discrimination stage, GSE-based features are introduced into the proposed MD-GCN. The MD-GCN constructs a graph convolution with a multichannel dilated to enlarge the receptive field, which extracts body topological and spatiotemporal action features from skeleton coordinates. Comparison experiments with state-of-the-art methods were conducted on a public dataset. The results show that the proposed method achieves an accuracy rate of 98.95%, which is the best and at least 6% higher than that of the other methods.