Multiple-Sensor Indoor Surveillance System

A surveillance system that uses a network of sensors of different kind for localizing and tracking people in an office environment that implements a Bayesian framework that uses noisy, but redundant data from multiple sensor streams and incorporates it with the contextual and domain knowledge is described.

Abstract

This paper describes a surveillance system that uses a network of sensors of different kind for localizing and tracking people in an office environment. The sensor network consists of video cameras, infrared tag readers, a fingerprint reader and a PTZ camera. The system implements a Bayesian framework that uses noisy, but redundant data from multiple sensor streams and incorporates it with the contextual and domain knowledge. The paper describes approaches to camera specification, dynamic background modeling, object modeling and probabilistic inference. The preliminary experimental results are presented and discussed.