View : 747 Download: 0

Full metadata record

DC Field Value Language
dc.contributor.author우현균-
dc.date.accessioned2017-01-05T02:01:45Z-
dc.date.available2017-01-05T02:01:45Z-
dc.date.issued2010-
dc.identifier.issn1057-7149-
dc.identifier.otherOAK-7006-
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/233448-
dc.description.abstractMost video surveillance systems require to manually set a motion detection sensitivity level to generate motion alarms. The performance of motion detection algorithms, embedded in closed circuit television (CCTV) camera and digital video recorder (DVR), usually depends upon the preselected motion sensitivity level, which is expected to work in all environmental conditions. Due to the preselected sensitivity level, false alarms and detection failures usually exist in video surveillance systems. The proposed motion detection model based upon variational energy provides a robust detection method at various illumination changes and noise levels of image sequences without tuning any parameter manually. We analyze the structure mathematically and demonstrate the effectiveness of the proposed model with numerous experiments in various environmental conditions. Due to the compact structure and efficiency of the proposed model, it could be implemented in a small embedded system. © 2006 IEEE.-
dc.languageEnglish-
dc.titleEnvironmentally robust motion detection for video surveillance-
dc.typeArticle-
dc.relation.issue11-
dc.relation.volume19-
dc.relation.indexSCI-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.startpage2838-
dc.relation.lastpage2848-
dc.relation.journaltitleIEEE Transactions on Image Processing-
dc.identifier.doi10.1109/TIP.2010.2050644-
dc.identifier.wosidWOS:000283445600005-
dc.identifier.scopusid2-s2.0-78049305245-
dc.author.googleWoo H.-
dc.author.googleJung Y.M.-
dc.author.googleKim J.-G.-
dc.author.googleSeo J.K.-
dc.contributor.scopusid우현균(35811616900)-
dc.date.modifydate20230620092339-
Appears in Collections:
연구기관 > 수리과학연구소 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

BROWSE