Visual re-identification across large, distributed camera networks

• Formalization of object re-identification problem in a distributed environment
• Re-identification treated as an open-world problem
• Novelty detection and forgetting included in the scheme
• A set of performance measures, geared towards open-world, distributed surveillance
• Experiments on a many-camera (36) surveillance dataset and publicly available source code

We propose a holistic approach to the problem of re-identification in an environment of distributed smart cameras. We model the re-identification process in a distributed camera network as a distributed multi-class classifier, composed of spatially distributed binary classifiers. We treat the problem of re-identification as an open-world problem, and address novelty detection and forgetting. As there are many tradeoffs in design and operation of such a system, we propose a set of evaluation measures to be used in addition to the recognition performance. The proposed concept is illustrated and evaluated on a new many-camera surveillance dataset and SAIVT-SoftBio dataset.

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