This paper describes a vehicle detection method using 3D data derived from a disparity map available in real-time. The integration of a flat road model reduces the search space in all dimensions. Inclination changes are considered for the road model update. The vehicles, modeled as a cuboid, are detected in an iterative refinement process for hypotheses generation on the 3D data. The detection of a vehicle is performed by a mean-shift clustering of plane filtered segments potentially belonging together in a first step. In the second step a u/v-disparity approach generates vehicle hypotheses covering differently appearing vehicles. The system was evaluated in real-traffic-scenes using a GPS system.     «

This paper describes a vehicle detection method using 3D data derived from a disparity map available in real-time. The integration of a flat road model reduces the search space in all dimensions. Inclination changes are considered for the road model update. The vehicles, modeled as a cuboid, are detected in an iterative refinement process for hypotheses generation on the 3D data. The detection of a vehicle is performed by a mean-shift clustering of plane filtered segments potentially belonging t...     »