跨模态异构行人再识别的研究进展

doi:10.16451/j.cnki.issn1003-6059.202012002

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摘要

同构行人再识别技术研究基于可见光图像的行人检索问题,但无法完全应对复杂多变真实场景,大量研究工作开始探索基于可见光图像与其它异构数据之间的行人检索问题,即跨模态异构行人再识别.该研究相比同构行人再识别,更具挑战性.文中首先简述跨模态异构行人再识别的概念及与一般行人再识别的区别,再针对文本与图像、图像与视频、跨分辨率图像、红外图像与可见光图像、深度图与可见光图像、素描与可见光图像这6类场景,归纳整理和分析跨模态异构行人再识别的代表性工作、常用数据集及一些算法的性能表现.最后,总结目前整体研究进展,展望未来发展趋势.

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	孙锐
	赵争晖
	杨梓
	高隽

关键词 ：行人再识别(ReID), 异构行人再识别(Hetero-ReID), 跨模态, 度量学习, 特征表示, 深度学习

Abstract：

Homogeneous person re-identification(ReID) based on RGB images is well researched.With the development of public security and pedestrian retrieval in complex real-world situations,the field of pedestrian matching based on multi-modal heterogeneous data sources,called cross-modality heterogeneous ReID,is explored.In this paper,heterogeneous ReID and the differences from homogeneous ReID are firstly summarized.Then,heterogeneous ReID is described under six types of scenarios,including text-to-image,images-to-video,cross-resolution,infrared-visible,RGB-depth and sketch-photo.The relevant popular datasets,representative algorithms and excellent performance of these algorithms on datasets are summarized and reorganized.Finally,the current research progress and the future research trends of cross-modality heterogeneous person re-identification are discussed.

Key words： Person Re-identification(ReID) Heterogeneous Person Re-identification(Hetero-ReID) Cross-Modality Metric Learning Feature Representation Deep Learning

收稿日期: 2020-10-13

基金资助:

国家自然科学基金面上项目(No.61471154,61876057)、安徽省重点研发计划(No.202004d07020012)资助

通讯作者: 孙锐,博士,教授,主要研究方向为计算机视觉、机器学习.E-mail:sunrui@hfut.edu.cn.

作者简介: 赵争晖,硕士研究生,主要研究方向为图像信息处理、计算机视觉.E-mail:904150289@qq.com;杨梓,硕士研究生,主要研究方向为图像信息处理、计算机视觉.E-mail:798830989@qq.com;高隽,博士,教授,主要研究方向为智能信息处理、机器视觉.E-mail:gaojun@hfut.edu.cn.

引用本文:

孙锐, 赵争晖, 杨梓, 高隽. 跨模态异构行人再识别的研究进展[J]. 模式识别与人工智能, 2020, 33(12): 1066-1082. SUN Rui, ZHAO Zhenghui, YANG Zi, GAO Jun. A Survey on Cross-Modality Heterogeneous Person Re-identification. , 2020, 33(12): 1066-1082.

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