Surveillance in Virtual Reality: System Design and Multi-Camera Control

Abstract

This paper advocates a virtual vision paradigm and demonstrates its usefulness in camera sensor network research. Virtual vision prescribes the use of a visually and behaviorally realistic virtual environment simulator in the design and evaluation of surveillance systems. Impediments to deploying and experimenting with appropriately complex camera networks makes virtual vision an attractive alternative for many vision researchers who are motivated to investigate high level multi-camera control issues within such networks. In particular, we present two prototype surveillance systems comprising passive and active pan/tilt/zoom cameras. We deploy these systems in a virtual train station environment populated by autonomous, lifelike virtual pedestrians. The easily reconfigurable virtual cameras situated throughout this environment generate synthetic video feeds that emulate those acquired by real surveillance cameras monitoring extensive public spaces. Our novel multi-camera control strategies enable the cameras to collaborate in persistently observing pedestrians of interest that move across their fields of view and in capturing close-up videos of pedestrians as they travel through designated areas. The sensor networks support task-dependent camera node selection and aggregation through local decision-making and inter-node communication. Our approach to multi-camera control is robust to node failures and message loss.