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The vast quantity of visual data generated by the rapid expansion of large scale distributed multicamera networks, makes automated person detection and reidentification (RE-ID) essential components of modern surveillance systems. However, the integration of automated person detection and RE-ID algorithms is not without problems, and the errors arising in this integration must be measured (e.g., detection...
In this work we propose an architecture for fully automated person re-identification in camera networks. Most works on re-identification operate with manually cropped images both for the gallery (training) and the probe (test) set. However, in a fully automated system, re-identification algorithms must work in series with person detection algorithms, whose output may contain false positives, detections...
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