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dc.contributor.authorGhedia, Navneet-
dc.contributor.authorVithalani, C.-
dc.contributor.authorKothari, Ashish-
dc.date.accessioned2023-05-17T06:02:41Z-
dc.date.available2023-05-17T06:02:41Z-
dc.date.issued2020-
dc.identifier.citationGhedia, N. ,Vithalani, C. ,Kothari, A. (2020). A comprehensive Survey: Background Estimation and Motion Detection Approaches. GIS Science Journal, 7(8), 92-98,ISSN 1869-9391.en_US
dc.identifier.issn1869-9391-
dc.identifier.urihttp://10.9.150.37:8080/dspace//handle/atmiyauni/994-
dc.description.abstractVisual Monitoring System requires efficient estimation of the background, background modelling and accurate motion detection. Background subtraction is the simplest approach to classify the background and foreground. Now a day’s most of the modern smart video surveillance system requires robust detection of the foregrounds which effectively handle various environment challenges. Literature shows not a single algorithm ably handle all constraints, on the basis of the applications researchers generally, focuses on the classification or segmentation techniques. The objective of this paper is to provide the rigorous and comprehensive study of the various background modeling approaches and foreground detection techniques. This survey discussed some of the popular statistical pixel based parametric and non parametric approaches, region based approached and some of the popular hybrid approaches for the background handles. The aim of the study is to handle the various datasets and real time video sequences challenges. in spite of of all the literature survey still some of the challenges like object abrupt motion, object complex silhouette, object appearance and camera motion are challenging issues for the researchers.en_US
dc.language.isoenen_US
dc.publisherGIS Science Journalen_US
dc.subjectBackground Estimationen_US
dc.subjectBackground Modelingen_US
dc.subjectForeground Detectionen_US
dc.subjectVisual Monitoringen_US
dc.titleA comprehensive Survey: Background Estimation and Motion Detection Approachesen_US
dc.typeArticleen_US
Appears in Collections:01. Journal Articles

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