Citation

Liu F, Zeng Z, Jiang R. PLoS One 2017; 12(11): e0186098.

Affiliation

Huawei Corporation, Shenzhen, China.

Copyright

(Copyright © 2017, Public Library of Science)

DOI

10.1371/journal.pone.0186098

PMID

29135984

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.

Language: en