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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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References

  1. Kong, Q.J., Li, Z.P., Chen, Y.K., Liu, Y.C.: An approach to urban traffic state estimation by fusing multisource information. IEEE Trans. Intell. Transp. Syst. 10(3), 499–511 (2009)

    Article  Google Scholar 

  2. Ma, D.F., Wang, D.H., Bie, Y.M., Di, S.: A method of signal timing optimization for spillover dissipation in urban street networks. Math. Probl. Eng. 2013(11), 1–11 (2013)

    Google Scholar 

  3. Kerner, B.S., Demir, C., Herrtwich, R.G., Klenov, S.L., Rehborn, H., Aleksiü, M., DaimlerChrysler, A.G.: Traffic state detection with floating car data in road networks. In: Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, pp. 44–49 (2005)

    Google Scholar 

  4. Ozkurt, C., Camci, F.: Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Math. Computational Appl. 14(3), 187–196 (2009)

    Google Scholar 

  5. Horng, G.J., Li, J.P., Cheng, S.T.: Traffic congestion reduce mechanism by adaptive road routing recommendation in smart city. Inter. Conference Consumer Elect., Comm. Networks. 714–717 (2013)

  6. Bi, S., Han, L.Q., Zhong, Y.X., Wang, X.J.: All-day traffic states recognition system without vehicle segmentation. J China Universities Posts Telecom. 18(Suppl. 2), 1–18(Suppl. 2),11 (2011)

  7. Yuan, J., Zheng, Y., Xie, X., Sun, G.Z.: T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowledge Data Engr. 25(1), 220–232 (2013)

    Article  Google Scholar 

  8. Kastrinaki, V., Zervakis, M., Kalaitzakis, K.: A survey of video processing techniques for traffic applications. Image Vis. Comput. 21(4), 359–381 (2003)

    Article  Google Scholar 

  9. Xu, D.W., Dong, H.H., Jia, L.M., Qin, Y.: Virtual speed sensors based algorithm for expressway traffic state estimation. Sci. CHINA Technol. Sci. 55(5), 1381–1390 (2012)

    Article  Google Scholar 

  10. Tao, S., Manolopoulos, V., Rodriguez, S., Rusu, A.: Real-time urban traffic state estimation with a-gps mobile phones as probes. J Trans Technol. 02(1), 22–31 (2012)

    Google Scholar 

  11. Van Hinsbergen, C.P.I.J., Schreiter, T., Zuurbier, F.S., Van Lint, J.W.C., Van Zuylen, H.J.: Localized extended Kalman filter for scalable real-time traffic state estimation. IEEE Trans. Intell. Transp. Syst. 13(1), 385–394 (2012)

    Article  Google Scholar 

  12. Beymer, D., Mclauchlan, P., Coifman, B., Malik, J.: A real-time computer vision system for measuring traffic parameters. Conference Computer Vision Pattern Recognition. 495–501 (1997)

  13. Zhang, W., Wu, Q., J, M., Yin, H.B.: Moving vehicles detection based on adaptive motion histogram. Digital Signal Processing. 10(3), 793–805 (2010)

    Article  Google Scholar 

  14. Ji, X.P., Wei, Z.Q., Feng, Y.W.: Effective vehicle detection technique for traffic surveillance systems. J. Vis. Commun. Image Represent. 17(3), 647–658 (2006)

    Article  Google Scholar 

  15. Spagnolo, P., Orazio, T.D., Leo, M., Distante, A.: Moving object segmentation by background subtraction and temporal analysis. Image Vis. Comput. 24(5), 411–423 (2006)

    Article  Google Scholar 

  16. Wang, W.Q., Yang, J., Gao, W.: Modeling background and segmenting moving objects from compressed video. IEEE Transactions Circuits Syst. Video Technol. 18(5), 670–681 (2008)

    Article  Google Scholar 

  17. Porikli F., Li X.K.: Traffic congestion estimation using HMM models without vehicle tracking. 2004 IEEE Intelligent Vehicles Symposium 188–193 (2004)

  18. Tan, E., Chen, J.: Vehicular traffic density estimation via statistical methods with automated state learning. IEEE Conference Advanced Video Signal Based Surveillance. 164–169 (2007)

  19. Zhang, Y., Jia, K.B.: Traffic state identification for expressway based on video sequences. J. Transport Inform. Safety. 31(4), 14–20 (2013)

    Google Scholar 

  20. Ojala, T., Pietikäinen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Machine Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  21. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  22. Guo, L., Ge, P.S., Zhang, M.H., Li, L.H., Zhao, Y.B.: Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine. Expert Syst. Appl. 39(4), 4274–4286 (2012)

    Article  Google Scholar 

  23. Chan, A.B., Vasconcelos, N.: Probabilistic kernels for the classification of auto-regressive visual processes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 846–851 (2005)

    Google Scholar 

Download references

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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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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