Liu Fei, Zeng Zhiyuan, Jiang Rong
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, China.
Huawei Corporation, Shenzhen, China.
PLoS One. 2017 Nov 14;12(11):e0186098. doi: 10.1371/journal.pone.0186098. eCollection 2017.
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.
在发展中国家，许多不断扩张的城市正面临着因人口和车辆数量过多而带来的挑战。收集实时、可靠且精确的交通流信息对于城市交通管理至关重要。本文的主要目的是开发一种自适应模型，该模型能够利用计算机视觉技术评估城市道路上的实时车辆数量。本文提出了一种用于车辆检测的自动实时背景更新算法以及一种基于虚拟线圈和检测线方法的自适应车辆计数模式。此外，还引入了一种新的鲁棒检测方法来监测路段的实时交通拥堵状态。已开发出一个原型系统并安装在一条城市道路上进行测试。结果表明，该系统性能强大，在大多数现场场景下实时计数准确率超过99%。
