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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 network (BBN) techniques support this as well as allowing us to model dynamic dependencies between parameters involved in visual interpretation. We illustrate these arguments using experimental results fro m a t r a c surveillance application. In particular, we show that using expectations of object trajectory, size and speed for the particular scene can improve robustness and sensitivity in dynamic tracking and segmentation. We also show that behavioural evaluation under attentional control can be achieved using a combination of a static BBN tasknet and dynamic network (DBN). The causal structure o f t h e s e networks provides a framework for the design and integration of advanced vision systems.

Although spatio-temporal gradient schemes are widely used in the computation of image motion, algorithms are ill conditioned for particular classes of input. This paper addresses this problem. Motion is computed as the space-time direction in which the difference in image illuminance from the local mean is conserved. This method can reliably detect motion in first-order and some second-order motion stimuli. Components of the model can be identified with directionally asymmetric and directionally selective simple cells. A stage in which we compute spatial and temporal derivatives of the difference between image illuminance and the local mean illuminance using a truncated Taylor series gives rise to a phase-invariant output reminiscent of the response of complex cells.

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