Visual Surveillance in a Dynamic and Uncertain World

Advanced visual surveillance systems not only need to track moving objects but also interpret their patterns of behaviour. This means that solving the information integration problem becomes very important. We use conceptual knowledge of both the scene and the visual task to provide constraints. We also control the system using dynamic attention and selective processing. Bayesian belief networks support this and allow us to model dynamic dependencies between parameters involved in visual interpretation. We illustrate these arguments using experimental results from a traffic surveillance application. In particular, we demonstrate that using expectations of object trajectory, size and speed for the particular scene improves robustness and sensitivity in dynamic tracking and segmentation. We also demonstrate behavioral evaluation under attentional control using a combination of a static BBN TASKNET and dynamic network. The causal structure of these networks provides a framework for the design and integration of advanced vision systems.