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Traffic Density Recognition Based on Image Global Texture Feature

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Abstract

Traffic state recognitions can provide a strategic support for control and management of urban traffic, which is crucial to ease traffic congestion, reduce road accidents, and ensure road traffic efficiency. This paper proposes an effective traffic density estimation method based on image processing. In the beginning, a whole image is divided into several cells, and then a region of interest (ROI) is extracted based on calculating varieties of pixel values in a temporal sequence of each cell. Then a texture feature descriptor, a histogram of multi-scale block local binary pattern (HMBLBP) is proposed for local feature representation. The HMBLBP of all cells in the ROI are concatenated as a global feature. Furthermore, principle component analysis is performed for dimensionality reduction to save computational cost. At last, the method proposed is tested with two datasets captured from real-world traffic scenarios. By using the support vector machine (SVM) classifier, traffic states are classified into heavy, medium and light densities. Reliable performances are shown in the experimental tests.

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Acknowledgments

This work is supported by the National Key Research and Development Program of China (No. 2018YFB0105205), the National Science Foundation of China (No. 51675224, No. 51775236, and No. U1564214), the Industrial Innovation Special Fund Project of Jilin Province of China (No. 2017C045-1), and the Foundation of State Key Laboratory of Automotive Simulation and Control (20180106).

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Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, China

    Hongyu Hu, Zhenhai Gao, Yuhuan Sheng & Chi Zhang

  2. Institute of Industrial Science, the University of Tokyo, Tokyo, Japan

    Rencheng Zheng

  3. School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China

    Rencheng Zheng

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  1. Hongyu Hu
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Correspondence to Hongyu Hu.

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Hu, H., Gao, Z., Sheng, Y. et al. Traffic Density Recognition Based on Image Global Texture Feature. Int. J. ITS Res. 17, 171–180 (2019). https://doi.org/10.1007/s13177-019-00187-0

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  • DOI: https://doi.org/10.1007/s13177-019-00187-0

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