LEADER 02046nam  2200421z- 450 
001 9910346775403321
005 20210212
010   $a1000051065
035   $a(CKB)4920000000100769
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/62761
035   $a(oapen)doab62761
035   $a(EXLCZ)994920000000100769
100   $a20202102d2016      |y         0
101 0 $ager
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aWissensbasierte probabilistische Modellierung fu?r die Situationsanalyse am Beispiel der maritimen U?berwachung
210   $cKIT Scientific Publishing$d2016
215   $a1 online resource (XVI, 217 p. p.)
225 1 $aKarlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe
311 08$a3-7315-0460-X 
330   $aIn today's surveillance systems, a multitude of sensors are used. Thus, the data volume is clearly increasing and the human decision maker has to be supported in analyzing this data in an intelligent way. This contribution deals with the process of situation assessment, which is analyzing real-time data with respect to pre-modeled situations of interest with a dynamic Bayesian network. The quality of the recognition is evaluated with a maritime dataset.
610   $adata fusion
610   $aDatenfusion
610   $adynamic Bayesian networks
610   $adynamische Bayes'sche Netze
610   $amaritime surveillance
610   $amaritime U?berwachung
610   $asituation awareness
610   $aSituationsanalyse
610   $aSituationsbewusstseinSituation assessment
700   $aFischer$b Yvonne$4auth$0609868
906   $aBOOK
912   $a9910346775403321
996   $aWissensbasierte probabilistische Modellierung für die Situationsanalyse am Beispiel der maritimen Überwachung$93035816
997   $aUNINA