Multiple-shot person re-identification by chromatic and epitomic analyses
Pattern Recognition Letters
Boosted human re-identification using Riemannian manifolds
Image and Vision Computing
Appearance-based people recognition by local dissimilarity representations
Proceedings of the on Multimedia and security
Fast person re-identification based on dissimilarity representations
Pattern Recognition Letters
Part-based spatio-temporal model for multi-person re-identification
Pattern Recognition Letters
Towards skeleton biometric identification using the microsoft kinect sensor
Proceedings of the 28th Annual ACM Symposium on Applied Computing
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
On-the-fly feature importance mining for person re-identification
Pattern Recognition
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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In many surveillance systems there is a requirement todetermine whether a given person of interest has alreadybeen observed over a network of cameras. This is the personre-identification problem. The human appearance obtainedin one camera is usually different from the ones obtained inanother camera. In order to re-identify people the humansignature should handle difference in illumination, pose andcamera parameters. We propose a new appearance modelbased on spatial covariance regions extracted from humanbody parts. The new spatial pyramid scheme is applied tocapture the correlation between human body parts in orderto obtain a discriminative human signature. The humanbody parts are automatically detected using Histograms ofOriented Gradients (HOG). The method is evaluated usingbenchmark video sequences from i-LIDS Multiple-CameraTracking Scenario data set. The re-identification performanceis presented using the cumulative matching characteristic(CMC) curve. Finally, we show that the proposedapproach outperforms state of the art methods.