Real-time pedestrian classification exploiting 2D and 3D information

A new approach for standing and walking pedestrian detection using pattern matching and exploiting both 2D image information and 3D dense stereo information is proposed. Because 3D information accuracy does not allow the direct classification of the 3D shape, a combined 3D-2D method is proposed. The 3D data are used in an innovative way for pedestrian hypotheses generation, scale and depth estimation and 2D models selection. Also the 3D hypotheses allow the corresponding 2D image region of interest selection and the 2D hypothesis generation. The 2D hypothesis consists of the object-s external edges obtained by an edge extraction and a depth coherency-based filtering out 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.