Fusion and Inference in Surveillance Networks

Project Leader: Dr. Mark Coates, McGill University

With the widespread deployment of networked sensors and cameras throughout cities, there is an incredible opportunity for improving our safety and security by harnessing the information available in these surveillance networks. Presently, the mathematics and algorithms to utilize fully surveillance networks of such scales do not exist. Surveillance networks incorporate the cameras mounted on traffic lights and overpasses, the mobile cameras attached to emergency vehicles, and chemical and biological sensors for detecting dangerous contaminants. In the near future, unmanned aerial vehicles (UAVs) will fly over our cities and provide aerial camera footage as well as other sensor information such as laser range data. The surveillance networks can comprise several thousand sensors and cameras throughout a city, and the control of them is a very challenging engineering task. Our project focuses on the design of strategies for managing the sensors and cameras in the network and harnessing the information provided by them.