Set based discriminative ranking for recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Identity inference: generalizing person re-identification scenarios
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Symmetry-driven accumulation of local features for human characterization and re-identification
Computer Vision and Image Understanding
Covariance descriptor multiple object tracking and re-identification with colorspace evaluation
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
On-the-fly feature importance mining for person re-identification
Pattern Recognition
Features accumulation on a multiple view oriented model for people re-identification
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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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.