A framework for Object Classification in Fareld Videos

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dc.citation.volume8fr_FR
dc.contributor.authorSetitra, Insaf
dc.contributor.authorLarabi, Slimane
dc.date.accessioned2014-10-23T12:21:52Z
dc.date.available2014-10-23T12:21:52Z
dc.date.issued2014-12
dc.description.abstractObject classification in videos is an important step in many applications such as abnormal event detection in video surveillance, traf- fic analysis is urban scenes and behavior control in crowded locations. In this work, propose a framework for moving object classification in farfield videos. Much works have been dedicated to accomplish this task. We overview existing works and combine several techniques to implement a real time object classifier with offline training phase. We follow three main steps to classify objects in steady background videos : background subtraction, object tracking and classification. We measure accuracy of our classifier by experiments done using the PETS 2009 dataset.fr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/690
dc.publisherSpringerfr_FR
dc.relation.ispartofWicon 2014fr_FR
dc.relation.placeLisbon, Portugalfr_FR
dc.rights.holderSpringerfr_FR
dc.structureTechnologies des Systèmes Web et Multimédia et de Gestion de Contenufr_FR
dc.subjectbackground subtractionfr_FR
dc.subjectfeature extractionfr_FR
dc.subjectvideo analysisfr_FR
dc.subjecttrackingfr_FR
dc.subjectobject classificationfr_FR
dc.titleA framework for Object Classification in Fareld Videosfr_FR
dc.typeConference paper
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