摘要
行人重识别旨在建立目标行人在多个无交叉覆盖监控区域间的身份联系,在智慧城市、司法侦查和监控安全等领域具有重要应用价值。传统行人重识别方法针对短时间跨度场景,依赖行人外观特征的稳定不变性,旨在克服光照差异、视角变化和物体遮挡等挑战。与之不同,换装行人重识别针对长时间跨度场景,除受限于上述挑战还面临换装带来的外观变化问题,是近几年的一个研究难点和热点。围绕换装行人重识别,本文从数据集和解决方法两个方面综述国内外研究进展,探讨面临的挑战和难点。首先,梳理并比较了当前针对换装行人重识别的数据集,从采集方式、行人及样本数量等方面分析其挑战性和面临的局限性。然后,在简单回顾换装行人重识别发展历史的基础上,将其归纳为基于非视觉传感器的方法和基于视觉相机的方法两类。针对基于非视觉传感器的方法,介绍了深度传感器、射频信号等在换装行人重识别中的应用。针对基于视觉相机的方法,详细阐述了基于显式特征设计与提取的方法、基于特征解耦的方法和基于隐式数据驱动自适应学习的方法。在此基础上,探讨了当前换装行人重识别面临的问题并展望未来的发展趋势,旨在为相关研究提供参考。
Person re-identification(Re-ID)aims to build identity correspondence of the target pedestrian among multiple non-overlap monitoring areas,which has significant application value in the fields such as smart city,criminal investigation and forensics,and surveillance security.Conventional Re-ID methods are often focused on short-term scenarios,which aim to tackle some challenges in related to illumination difference,view-angle change and occlusion.In these methods,the target pedestrian of interest(TPoI)is assumed as unchangeable dressing status while he re-appears under the surveillance circustmances.Such methods are restricted by the homology of appearance across different cameras,such as the same color and texture of pedestrians’clothes.In contrast,cloth-changing person Re-ID aims at long-term scenarios,which determines that the TPoI re-appears after a long-time gap likes one week or more.In addition to the above challenges in classical person Re-ID,cloth-changing person Re-ID also suffers the difficulty of appearance difference caused by clothes changing.This makes it a research difficulty in recent years.Considering cloth-changing person Re-ID,this paper discusses its challenges and difficulties,and provides an indepth review on recent progress in terms of the analysis of datasets and methods.Based on the analysis,some potential research trends and solutions are proposed.First,we summary and compare the existing cloth-changing person Re-ID datasets in relevant to 1)RGBD-based pattern analysis and computer vision(PAVIS),BIWI,and IAS-Lab,2)radio frequency-based radio frequency re-identification dataset-campus(RRDCampus)and RRD-Home,3)RGB image-based Celeb-ReID,person Re-ID under moderate clothing change(PRCC),long-term cloth-changing(LTCC),and DeepChange and 4)video-based train station dataset(TSD),Motion-ReID and CVID-ReID(cloth-varing video Re-ID),which can be oriented to their difficulties and limitations on the aspects of collecting methods,number of identities and images.Additionally,some popular person Re-
作者
张鹏
张晓林
包永堂
贲晛烨
单彩峰
Zhang Peng;Zhang Xiaolin;Bao Yongtang;Ben Xianye;Shan Caifeng(College of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao 266590,China;SenseTime Group Inc.,Shenzhen 518067,China;School of Information Science and Engineering,Shandong University,Qingdao 266237,China;College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China)
出处
《中国图象图形学报》
CSCD
北大核心
2023年第5期1242-1264,共23页
Journal of Image and Graphics
基金
国家自然科学基金项目(62202280)
山东省自然科学基金青年项目(ZR2021QF017)
山东高等学校青年创新团队人才引育计划。
关键词
视频监控
换装行人重识别
深度学习
特征学习与表示
生物特征
特征解耦
数据驱动学习
video surveillance
cloth-changing person re-identification
deep learning
feature learning and representation
biometric
feature decoupling
data-driven learning
