Video and image processing have been used for traffic supervision, analysis and monitoring of traffic condition in many cities and urban areas. The system described in this paper aims to approach the precise method to obtain the traffic flow, time headway and traffic volume through a sequence of images captured with a stationary video camera. The method consists of three algorithms. First, background modeling and update, second, a boosting method to enhance the foreground image and reduce the noise and at last determining best match of region of interest (ROI) to extract information to conclude if there is a vehicle in the detection zone or not. Based on this structure, the traffic quantity measurement (TQM) algorithm is represented to compute the important parameters in traffic sense that will be useful for traffic condition observation and management as well. In this research, the traffic quantities such as time headway and traffic flow have been measured. The experimental result shows this method obtains traffic flow and time headway with around 91% of accuracy in shadow free area and can be used in real time condition.
Key words: Vehicle time headway, traffic flow, computer vision.
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