A Fully Autonomous Person Re-Identification System

Abstract

The problem of re-identification involves the association of the appearances of a person caught with one or more surveillance cameras. This task is especially challenging in very crowded areas, where possible occlusions of people can drastically reduce visibility. In this paper, we aim to obtain a fully automatic re-identification system containing a stage of detection of persons before the stage of re-identification. Both stages are based on a general-purpose DNN (Deep Neural Network) object detector - the YOLO (You Only Look Once) model. The primary purpose and novelty of the proposed method are to obtain an autonomous re-identification system, starting from a simple detection model. Thus, with minimal computational and hardware resources, the proposed method leads to comparable results with other existing methods, even when running in real-time on multiple security cameras.