Technischer Bericht | Links: | PDF DownloadBibTeX-Datensatz | |
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Quelle: | Technischer Bericht IES-2011-10. In: Jürgen Beyerer, Alexey Pak (Hrsg.), Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory, Karlsruher Schriften zur Anthropomatik, KIT Scientific Publishing, 2012. | ||

ISBN: | 978-3-86644-855-1 | ||

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 surveillance applications and inferring 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, which make use of the underlying network modeling.
Due to this modeling, we get a probability of all the situations in the network
in every time step. By collecting more evidences over time, the probability
of a specific situation is either increasing or decreasing. Finally, we give an
example of a situation of interest in the maritime domain and show how the
probability of the situation of interest evolves over time.