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A video-based real-time adaptive vehicle-counting system for urban roads

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  • Fei Liu
  • Zhiyuan Zeng
  • Rong Jiang

Abstract

In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios.

Suggested Citation

  • Fei Liu & Zhiyuan Zeng & Rong Jiang, 2017. "A video-based real-time adaptive vehicle-counting system for urban roads," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    • Handle: RePEc:plo:pone00:0186098
    DOI: 10.1371/journal.pone.0186098
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    References listed on IDEAS

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    1. Hu, Xiaosong & Zou, Yuan & Yang, Yalian, 2016. "Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization," Energy, Elsevier, vol. 111(C), pages 971-980.
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