Authors:
Matteo Taiana
;
Dario Figueira
;
Athira Nambiar
;
Jacinto Nascimento
and
Alexandre Bernardino
Affiliation:
IST, Portugal
Keyword(s):
Re-Identification, Pedestrian Detection, Camera Networks, Video Surveillance.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
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.