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DC poleHodnotaJazyk
dc.contributor.authorRiachy, Chirine
dc.contributor.authorAl-Maadeed, Noor
dc.contributor.authorOrganisciak, Daniel
dc.contributor.authorKhelifi, Fouad
dc.contributor.authorBouridane, Ahmed
dc.contributor.editorSkala, Václav
dc.date.accessioned2019-10-22T11:50:34Z
dc.date.available2019-10-22T11:50:34Z
dc.date.issued2019
dc.identifier.citationWSCG 2019: full papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 173-181.en
dc.identifier.isbn978-80-86943-37-4 (CD/-ROM)
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464-4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/35622
dc.format9 s.cs
dc.format.mimetypeapplication/odt
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectopakovaná identifikace osobycs
dc.subjectčasoprostorový deskriptorcs
dc.subjectextrakce funkcícs
dc.subjectGaussovo rozdělenícs
dc.subjectdohledcs
dc.title3D Gaussian Descriptor for Video-based Person Re-Identificationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedDespite being often considered less challenging than image-based person re-identification (re-id), video-based person re-id is still appealing as it mimics a more realistic scenario owing to the availability of pedestrian sequences from surveillance cameras. In order to exploit the temporal information provided, a number of feature extraction methods have been proposed. Although the features could be equally learned at a significantly higher computational cost, the scarce nature of labelled re-id datasets encourages the development of robust hand-crafted feature representations as an efficient alternative, especially when novel distance metrics or multi-shot ranking algorithms are to be validated. This paper presents a novel hand-crafted feature representation for video-based person re-id based on a 3-dimensional hierarchical Gaussian descriptor. Compared to similar approaches, the proposed descriptor (i) does not require any walking cycle extraction, hence avoiding the complexity of this task, (ii) can be easily fed into off-shelf learned distance metrics, (iii) and consistently achieves superior performance regardless of the matching method adopted. The performance of the proposed method was validated on PRID2011 and iLIDS-VID datasets outperforming similar methods on both benchmarks.en
dc.subject.translatedperson re-identificationen
dc.subject.translatedspatio-temporal descriptoren
dc.subject.translatedfeature extractionen
dc.subject.translatedGaussian distributionen
dc.subject.translatedsurveillanceen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2019.2901.1.20
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2019: Full Papers Proceedings

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