Integrating bottom-up and top-down processes for accurate pedestrian counting

This paper presents a novel method for pedestrian counting in surveillance videos, which localizes and tracks the head-shoulders of pedestrians via the integrated bottom-up/top-down processes. In the bottom-up stage, we extract and match informative local image features crossing frames to obtain the initial moving regions (i.e. potential pedestrians). The top-down stage comprises two steps: (i) head-shoulder verification via a part-based classifier and (ii) head-shoulder tracking guided by the motion and appearance consistency. Moreover, the geometric context of the camera is employed to effective narrow the searching space of inference. We apply the method with the challenging videos and outperform the state-of-the-arts approach.