Traffic Police Gesture Recognition by Pose Graph Convolutional Networks

Abstract

Gestures from traffic police give the authorized information, especially in some urgent situation. Thus, understanding of traffic police instruction accurately and promptly is particularly crucial for the automated driving system. However, this task is a great challenge not only because of the dynamic and diversity characteristics of the human gesture, but also the high requirement for real-time performance in each frame. We propose an online activity recognition method based on pose estimation and Graph Convolutional Networks (GCN) to recognize the traffic police gesture in frame level. The main contribution in this work is the development of an online framework based on graph convolutional networks for traffic police recognition. Our approach obtained the state-of-the-art results on Traffic Police Gesture Recognition (TPGR) dataset.