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dc.contributor.authorKumar, S.
dc.contributor.authorYadav, J. S.
dc.date.accessioned2016-08-12T06:57:38Z
dc.date.available2016-08-12T06:57:38Z
dc.date.issued2016-06cs
dc.identifier.citationRadioengineering. 2016 vol. 25, č. 2, s. 399-408. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/63063
dc.description.abstractBackground subtraction is an extensively used approach to localize the moving object in a video sequence. However, detecting an object under the spatiotemporal behavior of background such as rippling of water, moving curtain and illumination change or low resolution is not a straightforward task. To deal with the above-mentioned problem, we address a background maintenance scheme based on the updating of background pixels by estimating the current spatial variance along the temporal line. The work is focused to immune the variation of local motion in the background. Finally, the most suitable label assignment to the motion field is estimated and optimized by using iterated conditional mode (ICM) under a Markovian framework. Performance evaluation and comparisons with the other well-known background subtraction methods show that the proposed method is unaffected by the problem of aperture distortion, ghost image, and high frequency noise.en
dc.formattextcs
dc.format.extent399-408cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2016/16_02_0399_0408.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectBackground subtractionen
dc.subjectbackground modelingen
dc.subjectinitial motion fielden
dc.subjectmorphology.en
dc.titleSegmentation of Moving Object Using Background Subtraction Method in Complex Environmentsen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue2cs
dc.coverage.volume25cs
dc.identifier.doi10.13164/re.2016.0399en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 3.0 Unported License