An efficient self-learning people counting system

People counting is a challenging task and has attracted much attention in the area of video surveillance. In this paper, we present an efficient self-learning people counting system which can count the exact number of people in a region of interest. This system based on bag-of-features model can effectively detect the pedestrians some of which are usually treated as background because they are static or move slowly. The system can also select pedestrian and non-pedestrian samples automatically and update the classifier in real-time to make it more suitable for certain specific scene. Experimental results on a practical public dataset named CASIA Pedestrian Counting Dataset show that the proposed people counting system is robust and accurate.