Shape-based detection of humans for video surveillance applications
In this paper we describe a surveillance system that is not only able to detect blobs and track them but also determines if a blob is a person. The given blob is segmented into sub-regions. A person model is fit to these regions such that a likelihood measure is maximized. The likelihood measure depends on the number of identified body parts, their length, location, and aspect ratio. The method is translation, rotation, and scale invariant and computationally efficient. The results obtained for test video sequences are very encouraging.