Vehicle detection in dense urban areas is often complicated due to car-like objects on rooftops which result in false positive detections. This can be easily avoided by using a digital surface model (DSM) calculated from two consecutive images to exclude those regions. However, in the real-time case traffic information has to be gathered rapidly and the calculation of the DSM for the whole image takes a lot of time. The presented approach suggest a method where the disparity image is only calculated for areas of interest. These areas are selected by projecting the road segments from a road database in the original image using the collinearity equation. The local coordinates of the detected vehicles are then transformed back in the UTM coordinate system using the collinearity equation again. It can be shown that the search area for the detector is significantly reduced and which also leads to improved results of the detection.     «

Vehicle detection in dense urban areas is often complicated due to car-like objects on rooftops which result in false positive detections. This can be easily avoided by using a digital surface model (DSM) calculated from two consecutive images to exclude those regions. However, in the real-time case traffic information has to be gathered rapidly and the calculation of the DSM for the whole image takes a lot of time. The presented approach suggest a method where the disparity image is only calcul...     »