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International Journal of Computer Vision

Erschienen in:

01.02.2015

Mapping Appearance Descriptors on 3D Body Models for People Re-identification

verfasst von: Davide Baltieri, Roberto Vezzani, Rita Cucchiara

Erschienen in: International Journal of Computer Vision | Ausgabe 3/2015

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Abstract

People Re-identification aims at associating multiple instances of a person’s appearance acquired from different points of view, different cameras, or after a spatial or a limited temporal gap to the same identifier. The basic hypothesis is that the person’s appearance is mostly constant. Many appearance descriptors have been adopted in the past, but they are often subject to severe perspective and view-point issues. In this paper, we propose a complete re-identification framework which exploits non-articulated 3D body models to spatially map appearance descriptors (color and gradient histograms) into the vertices of a regularly sampled 3D body surface. The matching and the shot integration steps are directly handled in the 3D body model, reducing the effects of occlusions, partial views or pose changes, which normally afflict 2D descriptors. A fast and effective model to image alignment is also proposed. It allows operation on common surveillance cameras or image collections. A comprehensive experimental evaluation is presented using the benchmark suite 3DPeS.

Vorheriger Artikel Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization

Nächster Artikel Neither Global Nor Local: Regularized Patch-Based Representation for Single Sample Per Person Face Recognition

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Titel: Mapping Appearance Descriptors on 3D Body Models for People Re-identification
verfasst von: Davide Baltieri
Roberto Vezzani
Rita Cucchiara
Publikationsdatum: 01.02.2015
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 3/2015
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-014-0747-z

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