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

Fei Liu, Zhiyuan Zeng and Rong Jiang

PLOS ONE, 2017, vol. 12, issue 11, 1-16

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0186098

DOI: 10.1371/journal.pone.0186098

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