Bayesian framework for video surveillance application

The goal of this paper is to describe and demonstrate the application of Bayesian networks in a generic automatic video surveillance system. Taking image features of tracked moving regions from an image sequence as input, mobile object properties are first computed and noise is suppressed by statistical methods. The probability that a scenario occurs is then computed from these mobile object properties through several layers of naive Bayesian classifiers (or a Bayesian network). Several issues and solutions regarding the efficiency of the Bayesian network are discussed. For example, the parameters of the networks, which represent rare activities (typical of video surveillance applications), can be learned from image sequences of similar scenarios which are more common. We demonstrate the effectiveness of our approach by training the networks with 600 image frames belonging to one domain of interest and applying them to image sequences in a different domain.