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

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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.

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Authors and Affiliations

  1. Dipartimento di Ingegneria Enzo Ferrari-University of Modena and Reggio Emilia, Via Vignolese, 905-41125, Modena, Italy

    Davide Baltieri, Roberto Vezzani & Rita Cucchiara

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  1. Davide Baltieri
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  2. Roberto Vezzani
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  3. Rita Cucchiara
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Correspondence to Roberto Vezzani.

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Communicated by Yasushi Yagi.

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Baltieri, D., Vezzani, R. & Cucchiara, R. Mapping Appearance Descriptors on 3D Body Models for People Re-identification. Int J Comput Vis 111, 345–364 (2015). https://doi.org/10.1007/s11263-014-0747-z

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  • DOI: https://doi.org/10.1007/s11263-014-0747-z

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