Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters. We propose a new appearance model combining information from multiple images to obtain highly discriminative human signature, called Mean Riemannian Covariance Grid (MRCG). The method is evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. 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.
To obtain highly discriminative human signature, Bak et al. REF propose Mean Riemannian Covariance Grid (MRCG) to combine information from multiple images.
Multiple-shot human re-identification by Mean Riemannian Covariance Grid
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Computer Science