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ISPRS Int. J. Geo-Inf. 2018, 7(1), 37; https://doi.org/10.3390/ijgi7010037

Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

1
Marine Information Technology Laboratory (Ocean University of China), Ministry of Education, Qingdao 266100, China
2
Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China
*
Author to whom correspondence should be addressed.
Received: 11 November 2017 / Revised: 21 December 2017 / Accepted: 16 January 2018 / Published: 22 January 2018
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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. View Full-Text
Keywords: traffic command gesture recognition; VGE; spatiotemporal CNN; HCI traffic command gesture recognition; VGE; spatiotemporal CNN; HCI
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Ma, C.; Zhang, Y.; Wang, A.; Wang, Y.; Chen, G. Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network. ISPRS Int. J. Geo-Inf. 2018, 7, 37.

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