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Tracking System using GPS, vision and 3D virtual Model

In order to improve the vehicle tracking quality in the cities and especially in urban area, the following article handles the correspondence between real and virtual images to find the closest virtual image to the real one. Real image are extracted from cameras equipped by a GPS system, together installed in the vehicle. Virtual images are extracted using a GPS from a database managed by 3D geographical information system (3D- GIS). It is known that GPS cannot give accurately the coordinates of a vehicle so it is necessary to use other kind of information using embedded sensors like camera. A way to compute a position using vision is to find the closest image in a 3D cartographical database which corresponds to the real one seen by the camera. Two methods are developed and tested with real data : the first method uses the Hough transform where each line corresponds to a point in the polar coordinate then we compare the image transformations. The second method is based on the Ransac fitting homography method. This method based on taking the two images real and virtual image, find the corners of each image using a harris corner detector, use the maximally correlated points to connect them, robustly fits a homography to a set of putatively matched image points, find the number of putatively matched image points that are called inliers and the greatest the number of inliers the closer is the virtual image to a real one. It uses homography, harris corner detector, and correlation functions. Results with real data are presented to illustrate performance of developed method.