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Boosted human re-identification using riemannian manifolds (2011)
| Venue: | Image and Vision Computing |
| Citations: | 19 - 4 self |
BibTeX
@ARTICLE{Corvée11boostedhuman,
author = {Etienne Corvée and Francois Brémond and Monique Thonnat},
title = {Boosted human re-identification using riemannian manifolds},
journal = {Image and Vision Computing},
year = {2011}
}
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Abstract
This paper presents an appearance-based model to address the human re-identification problem. Human re-identification is an important and still unsolved task in computer vision. In many systems there is a requirement to identify individuals or determine whether a given individual has already appeared somewhere in a network of cameras. The human appearance ob-tained in one camera is usually different from the ones obtained in another camera. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters. The paper focuses on a new appearance model based on Mean Riemannian Covariance (MRC) patches extracted from tracks of a particular individual. A new similarity measure using Riemannian manifold theory is also proposed to distinguish sets of patches belonging to a specific individual. We investigate the signifi-cance of the MRC patches based on their reliability extracted during tracking and their discriminative power obtained by a boosting scheme. The methods are evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. The re-identification per-formance is presented using the cumulative matching characteristic (CMC) curve. We demonstrate that the proposed approach outperforms state of the art methods. Finally, the results of our approach are shown on two other more pertinent datasets.
Keyphrases
human re-identification riemannian manifold camera parameter mrc patch cumulative matching characteristic human re-identification problem distinguish set riemannian manifold theory art method computer vision boosting scheme i-lids datasets mean riemannian covariance particular individual discriminative power unsolved task pertinent datasets human appearance new similarity measure human signature new appearance model appearance-based model many system benchmark video sequence re-identification per-formance
