Pedestrian detection on moving vehicle using stereovision and 2D cue

Abstract

We present a novel approach for pedestrian detecting on moving vehicle which equipped with low-cost cameras. Our approach is working in a framework which combines two-dimensional human body characteristics and three-dimensional information such as parallax and distance. By constructing a SPM (surface parallax map), it calculates parallax of object which do not belong to the road plane such as human body and obstacles. After recording the scores of all road area, an occlusion image is created, in which high density area indicates people's most likely appearance. Then a SVM (support vector machine) classifier is trained to classify pedestrian and non-pedestrian windows in candidate area. We also propose an algorithm to maintain SPM in real time. We evaluate our approach on real data which are taken from crowded city areas, the efficient and accurate results are demonstrated. © Springer-Verlag 2013.