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Self adaptive background modeling for identifying person's falls

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dc.contributor.author Doulamis, A en
dc.contributor.author Kalisperakis, I en
dc.contributor.author Stentoumis, C en
dc.contributor.author Matsatsinis, N en
dc.date.accessioned 2014-03-01T02:46:58Z
dc.date.available 2014-03-01T02:46:58Z
dc.date.issued 2010 en
dc.identifier.uri http://hdl.handle.net/123456789/32966
dc.subject Background Modeling en
dc.subject Gaussian Mixture en
dc.subject Motion Estimation en
dc.subject Foreground Background en
dc.subject.other Adaptive background model en
dc.subject.other Background modeling en
dc.subject.other Descriptors en
dc.subject.other Foreground/background en
dc.subject.other Gaussian mixtures en
dc.subject.other High confidence en
dc.subject.other Illumination changes en
dc.subject.other Low costs en
dc.subject.other Motion estimation algorithm en
dc.subject.other Motion-based algorithm en
dc.subject.other Real time en
dc.subject.other Self-adaptive en
dc.subject.other Cameras en
dc.subject.other Models en
dc.subject.other Motion estimation en
dc.subject.other Semantics en
dc.subject.other Algorithms en
dc.title Self adaptive background modeling for identifying person's falls en
heal.type conferenceItem en
heal.identifier.primary 10.1109/SMAP.2010.5706861 en
heal.identifier.secondary 5706861 en
heal.identifier.secondary http://dx.doi.org/10.1109/SMAP.2010.5706861 en
heal.publicationDate 2010 en
heal.abstract This paper presents a new scheme for detecting humans' falls in highly dynamic house environments. The scheme distinguishes falls from other humans' activities, like sitting, walking, lying, under (a) sudden and abrupt illumination changes (b) non-periodic/significant motions in the background (chairs, curtains, tables), (c) humans' movements towards all possible directions across camera. In particular, we combine adaptive background models - able to capture slight modifications of the background patterns with motion-based algorithms that define with high confidence parts of an image that should be considered as foreground/background after a significant visual change. We adopt Gaussian Mixtures for the adaptive background modeling, while we propose hierarchical motion estimation algorithms implemented on selective descriptors. The algorithms are of real time and require single low cost cameras. © 2010 IEEE. en
heal.journalName Proceedings - 2010 5th International Workshop on Semantic Media Adaptation and Personalization, SMAP 2010 en
dc.identifier.doi 10.1109/SMAP.2010.5706861 en
dc.identifier.spage 57 en
dc.identifier.epage 63 en


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