Motion clustering on video sequences using a competitive learning network

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dc.contributor.authorGorgunoglu, Salih
dc.contributor.authorAltay, Safak
dc.date.accessioned2024-09-29T16:08:18Z
dc.date.available2024-09-29T16:08:18Z
dc.date.issued2014
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIt is necessary to track human movements in crowded places and environments such as stations, subways, metros, and schoolyards, where security is of great importance. As a result, undesired injuries, accidents, and unusual movements can be determined and various precautionary measures can be taken against them. In this study, real-time or existing video sequences are used within the system. These video sequences are obtained from objects such as humans or vehicles, moving actively in various environments. At first, some preprocesses are made respectively, such as converting gray scale, finding the edges of the objects existing in the images, and thresholding the images. Next, motion vectors are generated by utilizing a full search algorithm. Afterwards, k-means, nearest neighbor, image subdivision, and a competitive learning network are used as clustering methods to determine dense active regions on the video sequence using these motion vectors, and then their performances are compared. It is seen that the competitive learning network significantly determines the classification of dense active regions, including motion. Moreover, the algorithms are analyzed in terms of their time performances.en_US
dc.identifier.doi10.3906/elk-1203-37
dc.identifier.endpage411en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-84894124171en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage400en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1203-37
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7481
dc.identifier.volume22en_US
dc.identifier.wosWOS:000330573900013en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMotion estimationen_US
dc.subjectcompetitive learning networken_US
dc.subjectvideo processingen_US
dc.subjectclusteringen_US
dc.titleMotion clustering on video sequences using a competitive learning networken_US
dc.typeArticleen_US

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