@article{ref1,
title="A video-based real-time adaptive vehicle-counting system for urban roads",
journal="PLoS one",
year="2017",
author="Liu, Fei and Zeng, Zhiyuan and Jiang, Rong",
volume="12",
number="11",
pages="e0186098-e0186098",
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.<p /> <p>Language: en</p>",
language="en",
issn="1932-6203",
doi="10.1371/journal.pone.0186098",
url="http://dx.doi.org/10.1371/journal.pone.0186098"
}