Detection and analysis of traffic jams using computer vision technologies

Many expanding cities in developing countries are faced with traffic congestion problems due to the overabundance of people and vehicles. Collecting real-time, reliable and accurate traffic flow information is essential for urban traffic management. The main goal of this article is to develop an adaptive model that can estimate the number of vehicles on city roads in real time using computer vision technologies. This paper proposes a real-time automatic background update algorithm for vehicle detection and an adapted model for vehicle counting based on virtual ring and detection line methods. In addition, a new reliable detection method was introduced to monitor the traffic situation of the road sector in real time. A system prototype was developed for testing and installed on a city road.

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