Towards intelligent camera networks: a virtual vision approach

The goals of this paper are two-fold: (i) to present our initial efforts towards the realization of a fully autonomous sensor network of dynamic video cameras capable of providing perceptive coverage of a large public space, and (ii) to further the cause of exploiting visually and behaviorally realistic virtual environments in the development and testing of machine vision systems. In particular, our proposed sensor network employs techniques that enable a collection of active (pan-tilt-zoom) cameras to collaborate in performing various visual surveillance tasks, such as keeping one or more pedestrians within view, with minimal reliance on a human operator. The network features local and global autonomy and lacks any central controller, which entails robustness and scalability. Its functionality is the result of local decision-making capabilities at each camera node and communication between the nodes. We demonstrate our surveillance system in a virtual train station environment populated by autonomous, lifelike virtual pedestrians. Our readily reconfigurable virtual cameras generate synthetic video feeds that emulate those generated by real surveillance cameras monitoring public spaces. This type of research would be difficult in the real world given the costs of deploying and experimenting with an appropriately complex camera network in a large public space the size of a train station.