A nonparametric adaptive tracking algorithm based on multiple feature distributions

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dc.contributor.authorPolat, Ediz
dc.contributor.authorÖzden, Mustafa
dc.date.accessioned2020-06-25T17:41:20Z
dc.date.available2020-06-25T17:41:20Z
dc.date.issued2006
dc.description.abstractThis paper presents an object tracking framework based on the mean-shift algorithm, which is a nonparametric technique that uses statistical color distribution of objects. Tracking objects through highly similar-colored background is one of the problems that need to be addressed. In various cases where object and background color distributions are very similar, the color distribution obtained from single frame alone is not sufficient to track objects reliably. To deal with this problem, the proposed algorithm utilizes an adaptive statistical background and foreground modeling to detect the change due to motion using kernel density estimation techniques based on multiple recent frames. The use of multiple frames supplies more information than single frame and thus it provides more accurate modeling of both background and foreground. In addition to color distribution, this statistical multiple frame-based motion representation is integrated into a modified mean-shift algorithm to create more robust object tracking framework. The use of motion distribution provides additional discriminative power to the framework. The superior performance with quantitative results of the framework has been validated using experiments on synthetic and real sequence of images.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1109/TMM.2006.884624
dc.identifier.endpage1163en_US
dc.identifier.issn1520-9210
dc.identifier.issn1941-0077
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-33845682521
dc.identifier.scopusqualityQ1
dc.identifier.startpage1156en_US
dc.identifier.urihttps://doi.org10.1109/TMM.2006.884624
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3685
dc.identifier.volume8en_US
dc.identifier.wosWOS:000242311700006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions On Multimedia
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectkernel density estimationen_US
dc.subjectmean-shift algorithmen_US
dc.subjectobject trackingen_US
dc.titleA nonparametric adaptive tracking algorithm based on multiple feature distributionsen_US
dc.typeArticle

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