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Vehicle Queue Length Measurement Based on a Modified Local Variance and LBP

  • Qin Chai
  • Cheng Cheng
  • Chunmei Liu
  • Hongzhong Chen
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)

Abstract

The real-time traffic parameters are necessary to dynamic traffic light control at intersection due to the serious traffic congestion. In this paper, we describe an approach for the real-time vehicle queue length measurement in a video-based traffic monitoring system. It is built on the property of a modified local variance in video frames, which does not rely on any sort of motion detection. In addition, a shadow removal approach is also presented with a simplified LBP as well as this local variance. Experimental results show that the proposed approach can highly improve the performance of the queue length measurement in real time, and it can be efficiently applied in a variety of traffic scenes.

Keywords

Modified local variance local binary pattern vehicle queue length 

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References

  1. 1.
    Zanin, M..., Modena, C.M...: An efficient vehicle queue detection system based on image processing. In: Proceeding of 12th International Conference on Image Analysis and Processing, pp. 232–237 (2003)Google Scholar
  2. 2.
    Iwasaki, Y.: An image processing system to measure vehicle queues and an adaptive traffic signal control by using the information of the queues. In: Proceeding of IEEE International Conference on Intelligent Transportation System, pp. 195–200 (1997)Google Scholar
  3. 3.
    Siyal, M.Y., Fathy, M.: Real-time image processing approach to measure traffic queue parameters. IEE Proc.-Vis. Image Signal Process 142(5), 297–303 (1995)CrossRefGoogle Scholar
  4. 4.
    Siyal, M.Y., Fathy, M.: A neural-vision based approach to measure traffic queue parameters in real-time. Pattern Recognition Letters 20(8), 761–770 (1999)CrossRefGoogle Scholar
  5. 5.
    Qiao, Y., Shi, Z.K.: Traffic parameters detection using edge and texture. Procedia Engineering 29, 3858–3862 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Albiol, A., Mossi, J.M.: Video-based traffic queue length estimation. In: International Conference on Computer Vision Workshops, pp. 1928–1932 (2011)Google Scholar
  7. 7.
    Cai, Y.F..., Wang, H...: Measurement of Vehicle Queue Length Based on Video Processing in Intelligent Traffic Signal Control System. In: International Conference on Measuring Technology and Mechatronics Automation, Changsha, China, pp. 615–618 (2010)Google Scholar
  8. 8.
    Ojal, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qin Chai
    • 1
  • Cheng Cheng
    • 1
  • Chunmei Liu
    • 1
  • Hongzhong Chen
    • 1
  1. 1.Key Laboratory of Embedded System and Service ComputingMinistry of Education Tongji UniversityShanghaiChina

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