Towards fully automated person re-identification

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 of partially occluded people and detections with bounding boxes misaligned to the people. These effects, when left untreated, may significantly jeopardise the performance of the re-identification system. To tackle this problem we propose modifications to classical person detection and re-identification algorithms, which enable the full system to deal with occlusions and false positives. We show the advantages of the proposed method on a fully labelled video data set acquired by 8 high-resolution cameras in a typical office scenario at working hours.