Title: A brief survey for person re-identification based on deep learning

Authors: Li Liu; Xi Li; Xuemei Lei

Addresses: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, Haidian, China; Shunde Graduate School, University of Science and Technology Beijing, Foshan, Guangdong, China ' School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, Haidian, China ' Office of Information Technology, University of Science and Technology Beijing, Beijing, Haidian, China

Abstract: Person Re-Identification (Re-ID) has been paid more attention due to its wide application in intelligent surveillance systems. Finding the same person from other non-overlapping cameras when a specific image of the pedestrian is given, which is a challenging problem for the reason of viewpoint variation, clothes changing, low resolution, etc. In this paper, we motivate reviewing for deep learning-based methods of Person Re-ID. We present a detailed survey of the state-of-the-art in terms of the description and analysis of supervised-based and unsupervised-based networks and their performance evaluation in the commonly used data sets. Finally, we analyse the challenging problems and discuss future works in this area.

Keywords: person re-identification; deep learning; literature survey; evaluation metric.

DOI: 10.1504/IJCAT.2022.126880

International Journal of Computer Applications in Technology, 2022 Vol.69 No.2, pp.101 - 111

Received: 30 Jun 2021
Accepted: 22 Aug 2021
Published online: 11 Nov 2022 *

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