Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Reidentification Using Spatiotemporal Appearance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning to Locate Informative Features for Visual Identification
International Journal of Computer Vision
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
People re-identification by spectral classification of silhouettes
Signal Processing
Multiple-Shot Person Re-identification by HPE Signature
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Domain transfer for person re-identification
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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This paper proposes a novel approach for pedestrian re-identification. Previous re-identification methods use one of 3 approaches: invariant features; designing metrics that aim to bring instances of shared identities close to one another and instances of different identities far from one another; or learning a transformation from the appearance in one domain to the other. Our implicit approach models camera transfer by a binary relation R={(x,y)|x and y describe the same person seen from cameras A and B respectively}. This solution implies that the camera transfer function is a multi-valued mapping and not a single-valued transformation, and does not assume the existence of a metric with desirable properties. We present an algorithm that follows this approach and achieves new state-of-the-art performance.