Abstract
Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN) model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS) dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.
Highlights
People have a strong dependence on traffic, and requirements with respect to such traffic have recently been put forward
In order to improve the accuracy and robustness of the gesture recognition algorithm, this paper provides the traffic police command gesture skeleton (TPCGS) dataset of Chinese traffic police command gestures, which was completed by 10 volunteers
After a TPCGS dataset with time and spatial domains was obtained, the spatiotemporal convolution neural network (ST-convolution neural networks (CNNs)) model proposed in this paper could be used to fully analyze spatiotemporal characteristics and recognize the gestures of traffic police
Summary
People have a strong dependence on traffic, and requirements with respect to such traffic have recently been put forward. The concept of smart traffic aids in governmental decision-making and management and reduces traffic accidents [1]. Traffic command gestures help to alleviate traffic jams. The virtual traffic command gestures experience system proposed in this paper is helpful. The intelligent recognition of traffic command gestures can promote traffic safety awareness. Users experience the traffic police command process in the virtual environment. When users walk or drive on roads, they are able to identify traffic police’s actions accurately, so as to prevent traffic accidents
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