Pedestrian Detection Directing at the Region of Interest in Videos

In this paper we present a novel and robust framework that decomposes continuous people detection into three parts, including off-line detection, tracking and learning. We introduce temporal coherence and spatial constraints into off-line detection phase by collecting a dynamical model from tracker which is estimated and updated by the learning algorithm. This method makes the detector aim at regions where a potential target will appear in the next frame, capable of handling pedestrians with occlusions and variety of scales, which as result greatly improves performance of pedestrian detection. We carry out a quantitative and qualitative evaluation on the public datasets.