People Counting by Video Segmentation and Tracking
In this paper, we present a novel approach to counting number of people that pass the view of an overhead mounted camera. Moving people are first detected as blobs and represented by binary masks, based on which possible multi-person blobs are further segmented into isolated persons according to their areas and locations. Each single person is tracked through consecutive frames using a correlation-based algorithm and a state diagram is proposed to count people entering and leaving the scene. Experimental results show that our approach is able to achieve promising results