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Modeling and recognizing situations of interest in surveillance applications

 
: Fischer, Yvonne; Reiswich, A.; Beyerer, Jürgen

:
Postprint urn:nbn:de:0011-n-2986123 (428 KByte PDF)
MD5 Fingerprint: 56784bcc282716ea90136227b2a8dfab
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Erstellt am: 28.5.2015


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, CogSIMA 2014 : 3-6 March 2014, San Antonio, Texas
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-3563-5 (Print)
ISBN: 978-1-4799-3564-2
S.209-215
International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) <4, 2014, San Antonio/Tex.>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Today's surveillance systems are very powerful in performing the process of object assessment, i.e., to estimate an object's position and attributes over time. However, the interpretation of the object's behavior, i.e., the situation assessment process, is still done by human experts. In this article, we describe an approach of how expert knowledge about situations of interest can be modeled in a situational dependency network (SDN). Based on the SDN, we present an approach of constructing a probabilistic model, namely a dynamic Bayesian network (DBN). We will describe in detail how the structure and the parameters of such a DBN can be specified automatically. The DBN can then be applied to observations made over time. Finally, we will show some evaluation results on simulated observation data with different amount of noise and show that the model yields the expected results.

: http://publica.fraunhofer.de/dokumente/N-298612.html