Název:	3D Gaussian Descriptor for Video-based Person Re-Identification
Autoři:	Riachy, Chirine Al-Maadeed, Noor Organisciak, Daniel Khelifi, Fouad Bouridane, Ahmed
Citace zdrojového dokumentu:	WSCG 2019: full papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 173-181.
Datum vydání:	2019
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/35622
ISBN:	978-80-86943-37-4 (CD/-ROM)
ISSN:	2464–4617 (print) 2464-4625 (CD/DVD)
Klíčová slova:	opakovaná identifikace osoby;časoprostorový deskriptor;extrakce funkcí;Gaussovo rozdělení;dohled
Klíčová slova v dalším jazyce:	person re-identification;spatio-temporal descriptor;feature extraction;Gaussian distribution;surveillance
Abstrakt v dalším jazyce:	Despite 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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2019: Full Papers Proceedings

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