People counting by learning their appearance in a multi-view camera environment
Pattern Recognition Letters
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Crowd counting and density estimation is still one of the important task in video surveillance. Usually a regression based method is used to estimate the number of people from a sequence of images. In this paper we investigate to estimate the count of people in a crowded scene. We detect the head region since this is the most visible part of the body in a crowded scene. The head detector is based on state-of-art cascade of boosted integral features. To prune the search region we propose a novel interest point detector based on gradient orientation feature to locate regions similar to the top of head region from gray level images. Two different background subtraction methods are evaluated to further reduce the search region. We evaluate our approach on PETS 2012 and Turin metro station databases. Experiments on these databases show good performance of our method for crowd counting.