Airborne vehicle detection with wrong-way drivers based on optical flow

Now-a-day vehicle detection has been an active research field in image processing. This research area interest is increase due to high traffic problems and limited roads and space. This paper described an algorithms and important methods to handle the high traffic volume. We described some important factors which are treated as an input i.e. Road Database, Global DEM, and Images. The road data base includes maps and information of roads and global DEM provide the height information of the image object. By using this all the information we calculate the disparity map. This disparity map is used to exclude elevated object like buildings and vegetation. In this paper we use two concept i.e. Histogram of oriented Gradients (HoG) and disparity mapping. The Histogram of oriented Gradients (HoG) concept is used to classify the images. We introduced here two new concepts, 1) To treat shady areas differently to identify the object covered by shady region and 2) To detect wrong direction moving object.