In modern driver assistance systems the environment perception especially the detection of vulnerable road users plays an important role. The analysis of such traffic scenes demands reliable and robust information on objects and their position. In this context a 3D-camera, offers a promising concept in providing both lateral resolution and depth information to supply this task. This paper presents models and methods for the detection and the tracking of objects using the range data of a 3D-camera. For that purpose the depth information of a 3D-camera is used for the reconstruction of the traffic scenario. Special and adapted methods of the field of machine learning allow to analyze the re-projected structures in order to extract object measurements from the sensors raw data. Finally stochastic state estimation is applied to propagate object hypotheses taking into account the measurements. The proposed methodology facilitates the integration and support of standard environment perception techniques used in todays advanced driver assistance systems
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