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Many of the Advanced Driver Safety Systems (ADAS) provide vision based driver assistance. All vision based systems are not the same since they differ in algorithms. Thus, it is necessary to compare how robust these different algorithms are and provide the best possible solution to the end user. Such characterization is often difficult due to changing environmental conditions under which the algorithms have to perform. In these systems, performance becomes a critical parameter since any slight lapse could translate to serious danger on road. The reliability of any algorithm depends on its accuracy and consistency under varying environmental conditions. It is important to define the boundary conditions of the algorithm to evaluate these performance parameters. Often one finds out system performance under varying conditions only by expensive road testing.

Traffic information is very useful in planning and designing of road transport, ensuring efficient administration of road traffic, transportation agencies as well as for the convenience of road users. Traffic can be measured in terms of speed, density and flow. In this paper, we propose two different methods to measure traffic in terms of density and flow. The set up for the proposed traffic monitoring system includes a camera placed at a height from ground looking downward on the road, such that its field of view is perpendicular to the direction of motion of the traffic. The images of the road are continuously captured by the camera and processed to determine the traffic. The first method uses Gaussian Mixture Modeling (GMM) to detect vehicles. Density is calculated in terms of area occupied by the vehicles on the road. Another method of measuring the traffic flow is proposed that is based on calculation of edge points on a horizontal line drawn in the image.