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

• Published in: PloS one Vol. 12; no. 11; p. e0186098
• Main Authors: Liu, Fei, Zeng, Zhiyuan, Jiang, Rong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.11.2017; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Computer and Information Sciences; Control; Demographic aspects; Engineering and Technology; Machine vision; Physical Sciences; Research and Analysis Methods; Social Sciences; Traffic congestion; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0186098

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.