Отрывок: For comparison, we run 3 state-of-the-art algorithms with the same initial position of the target. The first tracking algorithm (SURF) [28] is based on matching of local features and descriptors. The second tracking algori...
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dc.contributor.authorRuchay, A.N.-
dc.contributor.authorKober, V.I.-
dc.contributor.authorChernoskulov, I.E.-
dc.date.accessioned2017-05-12 16:26:57-
dc.date.available2017-05-12 16:26:57-
dc.date.issued2017-
dc.identifierDspace\SGAU\20170512\63730ru
dc.identifier.citationRuchay A.N. Real-time tracking of multiple objects with locally adaptive correlation filters / A.N. Ruchay, V.I. Kober, I.E. Chernoskulov // Сборник трудов III международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2017) - Самара: Новая техника, 2017. - С. 513-517.ru
dc.identifier.urihttp://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Realtime-tracking-of-multiple-objects-with-locally-adaptive-correlation-filters-63730-
dc.description.abstractA tracking algorithm using locally adaptive correlation filtering is proposed. The algorithm is designed to track multiple objects withinvariancetopose,occlusion,clutter,andilluminationvariations. Thealgorithmemploysapredictionschemeandcomposite correlationfilters. Thefiltersaresynthesizedwiththehelpofaniterativealgorithm,whichoptimizesdiscriminationcapabilityfor each target. The filters are adapted online to targets changes using information of current and past scene frames. Results obtained with the proposed algorithm using real-life scenes, are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.ru
dc.description.sponsorshipThis work was supported by the Russian Science Foundation, grant no. 15-19-10010.ru
dc.language.isoen_USru
dc.publisherНовая техникаru
dc.subjecttrackingru
dc.subjectlocally adaptive filtersru
dc.subjectcorrelation filtersru
dc.subjectmatchingru
dc.titleReal-time tracking of multiple objects with locally adaptive correlation filtersru
dc.typeArticleru
dc.textpartFor comparison, we run 3 state-of-the-art algorithms with the same initial position of the target. The first tracking algorithm (SURF) [28] is based on matching of local features and descriptors. The second tracking algori...-
Располагается в коллекциях: Информационные технологии и нанотехнологии

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