Using Bayesian Reasoning from Sensor Network for Indoor Surveillance

In this paper we define a Bayesian framework that uses noisy, but redundant data from a network of sensors that include multiple sensor streams of different types. It merges the data with the contextual and domain knowledge that is provided by both the physical constraints imposed by the local environment and by the people that are involved in the surveillance tasks. The paper also presents the results of applying the Bayesian framework to the people localization problem in indoor environment using a sensor network that consists of video cameras, infrared tag readers and a fingerprint reader.