Robust people counting in video surveillance: Dataset and system

Abstract

As an important application in civilian surveillance, pedestrian counting is challenging due to the occlusion and cluttered background. In this paper, we present an efficient people counting system based on regression and template matching. This method can effectively overcome the shortcomings of pedestrian detecting and tracking-based method and feature regression-based method. At the same time, we also introduce a challenging and practical public dataset named CASIA Pedestrian Counting Dataset. It contains richly annotated video and images captured from daily surveillance scenes. Experimental results on the proposed dataset show that our counting system is robust and accurate.