A Layered Approach to People Detection in 3D Range Data

People tracking is a key technology for autonomous systems, intelligent cars and social robots operating in populated environments. What makes the task di cult is that the appearance of humans in range data can change drastically as a function of body pose, distance to the sensor, self-occlusion and occlusion by other objects. In this paper we propose a novel approach to pedestrian detection in 3D range data based on supervised learning techniques to create a bank of classifiers for di erent height levels of the human body. In particular, our approach applies AdaBoost to train a strong classifier from geometrical and statistical features of groups of neighboring points at the same height. In a second step, the AdaBoost classifiers mutually enforce their evidence across di erent heights by voting into a continuous space. Pedestrians are finally found e ciently by mean-shift search for local maxima in the voting space. Experimental results carried out with 3D laser range data illustrate the robustness and e ciency of our approach even in cluttered urban environments. The learned people detector reaches a classification rate up to 96% from a single 3D scan.