Pedestrian Detection Based on Informed Haar-like Features and Switchable Deep Network

As pedestrians usually appear up-right in image or video data, we therefore employ a statistical model of the up-right human body where the head, the upper body, and the lower body are treated as three distinct components. As we incorporate different kinds of low-level measurements, the resulting multi-modal & multi-channel Haar-like features represent characteristic differences between parts of the human body yet are robust against variations in clothing or environmental settings. Then we use a Switchable Deep Network(SDN) for pedestrian detection. The SDN automatically learns features of different body parts. Experimental results on many pedestrian datasets show that the proposed algorithm significantly improves the detection rates at 0.1FPPI compared with the state-of-the-art domain adaptation methods and that it is robust and accurate against cluttered dynamical background, occlusion and the object deformation.