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Proceedings Paper

Person re-identification by pose priors
Author(s): Slawomir Bak; Filipe Martins ; Francois Bremond
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Paper Abstract

The person re-identification problem is a well known retrieval task that requires finding a person of interest in a network of cameras. In a real-world scenario, state of the art algorithms are likely to fail due to serious perspective and pose changes as well as variations in lighting conditions across the camera network. The most effective approaches try to cope with all these changes by applying metric learning tools to find a transfer function between a camera pair. Unfortunately, this transfer function is usually dependent on the camera pair and requires labeled training data for each camera. This might be unattainable in a large camera network. In this paper, instead of learning the transfer function that addresses all appearance changes, we propose to learn a generic metric pool that only focuses on pose changes. This pool consists of metrics, each one learned to match a specific pair of poses. Automatically estimated poses determine the proper metric, thus improving matching. We show that metrics learned using a single camera improve the matching across the whole camera network, providing a scalable solution. We validated our approach on a publicly available dataset demonstrating increase in the re-identification performance.

Paper Details

Date Published: 16 March 2015
PDF: 6 pages
Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 93990H (16 March 2015); doi: 10.1117/12.2083862
Show Author Affiliations
Slawomir Bak, INRIA Sophia Antipolis - Méditerranée (France)
Filipe Martins , INRIA Sophia Antipolis - Méditerranée (France)
Francois Bremond, INRIA Sophia Antipolis - Méditerranée (France)


Published in SPIE Proceedings Vol. 9399:
Image Processing: Algorithms and Systems XIII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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