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A Bayesian approach to the detection of hazardous shipping activity

: Fischer, Yvonne; Geisler, Jürgen

Volltext urn:nbn:de:0011-n-2124883 (755 KByte PDF)
MD5 Fingerprint: 3fab2b71cc2b5e05da754583a86d1ef5
Erstellt am: 4.9.2012

North Atlantic Treaty Organization -NATO-, Brussels:
NATO Symposium on Port and Regional Maritime Security 2012. Proceedings : Lerici, Italy, 21.-23. Mai 2012
Lerici, 2012 (RTO-MP-SCI 247)
Symposium on Port and Regional Maritime Security <2012, Lerici>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Tracking of ships by means of various heterogeneous sensors is well understood and regularly practiced. But in order to carry out surveillance for security purposes, the tracking of single units is only the fundamental step for the detection and classification of activities. The decision about potentially hazardous activities shall not only be based on single tracks but rather on the relation of tracks from multiple objects. Particularly along the coastal strip and in the vicinity of ports the mostly crowded ship movement poses a huge challenge to activity detection and classification. In this article we describe the information flow in an intelligent surveillance system and clarify the separation of the real world and the world model, which is used for the representation of the real world in the system. 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.