Skip to Main content Skip to Navigation
New interface
Journal articles

Boosted human re-identification using Riemannian manifolds

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 over a network of cameras. The human appearance obtained 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 significance of MRC patches based on their reliability extracted during tracking and their discriminative power obtained by a boosting scheme. Our method is evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. Re-identification performance is presented using a 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 further and more pertinent datasets.
Document type :
Journal articles
Complete list of metadata

Cited literature [1 references]  Display  Hide  Download
Contributor : Slawomir Bak Connect in order to contact the contributor
Submitted on : Monday, November 28, 2011 - 12:17:56 PM
Last modification on : Friday, February 4, 2022 - 3:22:32 AM
Long-term archiving on: : Friday, November 16, 2012 - 12:15:15 PM


Files produced by the author(s)




Slawomir Bak, Etienne Corvee, Francois Bremond, Monique Thonnat. Boosted human re-identification using Riemannian manifolds. Image and Vision Computing, 2011, ⟨10.1016/j.imavis.2011.08.008⟩. ⟨hal-00645588⟩



Record views


Files downloads