Pedestrian Detection and Classification Based on 2D and 3D Information For Driving Assistance Systems

This paper proposes a new approach for a vehicle based pedestrian detection and classification system. The pedestrian detection is performed based on the 3D data by generating a density map. Pedestrian classification uses a pattern matching approach and exploits both 2D image information and 3D dense stereo information. Because 3D information accuracy does not allow the direct classification of the 3D shape, a combined 3D-2D method is proposed. The 3D data is used for effective generation of pedestrian hypotheses, scale and depth estimation, and 2D models selection. From the 3D hypothesis, the corresponding 2D image window is selected and the 2D hypothesis is generated. The 2D hypothesis consists in the objects external edges obtained by an edge extraction and depth based filtering process. The scaled models are matched against the selected hypothesis using an elastic high speed matching based on the Chamfer distance. The method has been tested on synthetic and real world scenarios.