Key technology for intelligent video surveillance: a review of person re-identification

Abstract

Person re-identification (person ReID) aims to solve the association and matching of target person across cameras and scenes, which is a key link of intelligent video surveillance systems and plays a significant role in maintaining social public order. In order to understand the development status of person ReID technology and accelerate the implementation of person ReID research and applications, this paper provides statistics on the number of applications, funding intensity and geographic distribution of NSFC in this field, as well as a comprehensive review of person ReID research published in top international conferences and journals in the past decade. The paper starts with a standard person ReID algorithm process and details three key techniques: representation learning, metric learning, and re-ranking. Then, the main challenges faced in practical open scenarios are listed, and seven open person ReID tasks are outlined accordingly: occlusion, unsupervised, semi-supervised, cross-modal, end-to-end search, adversarial robustness and fast retrieval. In addition, representative datasets of standard person ReID and open person ReID are collated and some representative algorithms are compared. Finally, this paper provides an outlook on the future trends of person ReID.