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In advanced intelligent transport systems, detection of the vehicles has become very popular in the traffic area and also to identify the density of the vehicles in that particular area. As per the survey background subtraction is identified as one of the best approaches in identifying the vehicles for static camera. An improvised background subtraction model is adopted, wherein it works for real time tracking and also solves the problems of shadow detection. In background subtraction each pixel is updated with update equations. A component labeling technique is introduced after background subtraction to label the different objects so as to bifurcate between the two objects and each region is labelled with the different label values. Detections of the moving vehicles are identified and the density of vehicles travelling in the sight of the camera is determined

This paper presents a new method of detecting vehicles by using a simple and effective algorithm. The features of a vehicle are the most important aspects in detection of vehicles. The corner points are considered for the proposed algorithm. A large number of points are densely packed within the area of a vehicle, and the points are calculated by using the Harris corner detector. Making use of the fact that they are densely packed, grouping of these points is carried out. This grouping indicates that the group of corners belongs to each vehicle, and such groupings play a vital role in the algorithm. Once grouping is done, the next step is to eliminate the background noise. The Lucas-Kande algorithm is used to track the extracted corner points. Each corner point of the vehicle is tracked to make the output stable and reliable. The proposed algorithm is new, detect vehicles in multiple conditions, and also works for complex environments.

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