面向复杂场景的行人重识别综述

doi:10.11896/jsjkx.211200207

Abstract

Abstract: Person re-identification(Re-ID) aims to study the matching of specific persons among multiple disjoint cameras.To the best of our knowledge,it’s the first work that uses the types of challenges that the Re-ID technology needs to overcome in complex scenes as the classification basis,and classifies the Re-ID articles published during 2010-2021 into seven categories:person posture issues,occlusion issues,lighting issues,viewpoint issues,background issues,resolution issues and other open issues.This classification method is convenient for researchers to start from actual needs and find corresponding solutions according to the problems.Firstly,it reviews the research background,significance and research status of Re-ID,summarizes the current mainstream Re-ID framework,counts the papers published in the three top conferences of computer vision,i.e.CVPR,ICCV and ECCV,and counts the Re-ID related projects in the national fund projects since 2013.Secondly,with regard to the seven types of challenges faced in complex scenarios,the existing literatures are classified and analyzed in detail from the two aspects:the cause of the problems and the solutions.The mainstream methods for dealing with various challenges are summarized and listed again.Afterwards,we summarize the Re-ID methods with high generalization and list the difficulties of the current Re-ID research.Finally,the future development trend of Re-ID is discussed.

Key words: Person re-identification, Deep learning, Feature extraction, Metric learning, Computer vision

CLC Number:

TP391.41

ZHANG Min, YU Zeng, HAN Yun-xing, LI Tian-rui. Overview of Person Re-identification for Complex Scenes[J].Computer Science, 2022, 49(10): 138-150.

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