This paper presents an appearance-based model to address the human reidentication problem. Human re-identication 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 obtained in one camera is usually dierent from the ones obtained in another camera. In order to re-identify people a human signature should handle dierence 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 specic individual. We investigate the signicance 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-identication performance 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.
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