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Recognition of human behavior patterns using depth information and Gaussian feature maps

dc.contributor.authorSpehr, Jens
dc.contributor.authorIslami, Mensur
dc.contributor.authorWinkelbach, Simon
dc.contributor.authorWahl, Friedrich M.
dc.contributor.editorGoltz, Ursula
dc.contributor.editorMagnor, Marcus
dc.contributor.editorAppelrath, Hans-Jürgen
dc.contributor.editorMatthies, Herbert K.
dc.contributor.editorBalke, Wolf-Tilo
dc.contributor.editorWolf, Lars
dc.date.accessioned2018-11-06T10:57:22Z
dc.date.available2018-11-06T10:57:22Z
dc.date.issued2012
dc.description.abstractThe representation of human behavior patterns is challenging due to the complex dependencies between features gathered by a sensor and their spatial and temporal context. In this work we propose a new Gaussian feature map representation that uses the Kinect depth sensor, can easily be integrated in home environments, and allows learning unsupervised behavior patterns. The approach divides the living space into grid cells and models each grid cell with a Gaussian distribution of features like height, duration, magnitude and orientation of the velocity. Experimental results show that the method is able to recognize anomalies regarding the spatial and temporal context.en
dc.identifier.isbn978-3-88579-602-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/17775
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2012
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-208
dc.titleRecognition of human behavior patterns using depth information and Gaussian feature mapsen
dc.typeText/Conference Paper
gi.citation.endPage1415
gi.citation.publisherPlaceBonn
gi.citation.startPage1405
gi.conference.date16.-21. September 2012
gi.conference.locationBraunschweig
gi.conference.sessiontitleRegular Research Papers

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