Real time stereo vision based pedestrian detection using full body contours

There are many approaches to pedestrian detection in collision avoidance systems depending on the sensors (visible light, thermal infrared, radar, laser scanner) used for acquiring the data and the features (depth, shape, motion) used for detection. In this paper we present a method for shape based pedestrian detection in traffic scenes using a stereo vision system for acquiring the image frames and a contour matching technique for classifying the scene objects as belonging or not to the pedestrian class. The 3D information is used for determining the foreground points of each 2D object and a contour extraction and merging algorithm is performed on these points. A 2D image filtering is performed and edges are extracted and used for objects contours refinement. A hierarchy of pedestrian full body contours and a matching technique are used for classifying the extracted objects contours from the scene.