Efficient Pedestrian Detection via Rectangular Features Based on a Statistical Shape Model

Automatic pedestrian detection for advanced driver assistance systems (ADASs) is still a challenging task. Major reasons are dynamic and complex backgrounds in street scenes and variations in clothing or postures of pedestrians. We propose a simple yet effective detector for robust pedestrian detection. Observing that pedestrians usually appear upright in video data, we employ a statistical model of the upright human body in which the head, upper body, and lower body are treated as three distinct components. Our main contribution is to systematically design a pool of rectangular features that are tailored to this shape model. As we incorporate different kinds of low-level measurements, the resulting multimodal and multichannel Haar-like features represent characteristic differences between parts of the human body but are robust against variations in clothing or environmental settings. Our approach avoids exhaustive searches over all possible configurations of rectangular features nor does it rely on random sampling. It thus marks a middle ground among recently published techniques and yields efficient low-dimensional yet highly discriminative features. Experimental results on the well-established INRIA, Caltech, and KITTI pedestrian data sets show that our detector reaches state-of-the-art performance at low computational costs and that our features are robust against occlusions.