A top-down-view on intelligent surveillance systems

In today's surveillance systems, there is a need for enhancing the situation awareness of an operator. Supporting the situation assessment process can be done by extending the system with a module for automatic interpretation of the observed environment. In this article the information flow in an intelligent surveillance system is described and the separation of the real world and the world model, which is used for the representation of the real world in the system, is clarified. The focus of this article is on modeling situations of interest in a human-understandable way and how to infer them from sensor observations. For the representation in the system, concepts of objects, scenes, relations, and situations are introduced. Situations are modeled as nodes in a dynamic Bayesian network, in which the evidences are based on the content of the world model. Several methods for inferring situations of interest are suggested. Following this approach, even high-level situations of interest can be modeled by using different abstraction levels. Finally, an example of a situation of interest in the maritime domain is given.