A Survey on Visible-Infrared Cross-Modality Person Re-Identification

LI Zhi-yong; JIANG Wei; LIU Hao-jie

doi:10.12263/DZXB.20250800

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A Survey on Visible-Infrared Cross-Modality Person Re-Identification

SURVEYS AND REVIEWS | 更新时间：2026-04-24

- A Survey on Visible-Infrared Cross-Modality Person Re-Identification
- ACTA ELECTRONICA SINICA Vol. 53, Issue 12, Pages: 4811-4832(2025)
- 作者机构：
  
  1.浙江大学控制科学与工程学院，浙江杭州 310027
  2.浙江水利水电学院计算机科学与技术学院，浙江杭州 310018
- 作者简介：
- 基金信息：
  
  Zhejiang Province Natural Science Foundation of China(LZ24F030004)
- DOI：10.12263/DZXB.20250800
  CLC： TP391.4;TP18
- Received：13 September 2025，
  
  Accepted：05 December 2025，
  
  Published：25 December 2025
- 稿件说明：
移动端阅览
励志勇, 姜伟, 刘浩杰. 可见光-红外跨模态行人重识别研究综述[J]. 电子学报, 2025, 53(12): 4811-4832.

LI Zhi-yong, JIANG Wei, LIU Hao-jie. A Survey on Visible-Infrared Cross-Modality Person Re-Identification[J]. Acta Electronica Sinica, 2025, 53(12): 4811-4832.
励志勇, 姜伟, 刘浩杰. 可见光-红外跨模态行人重识别研究综述[J]. 电子学报, 2025, 53(12): 4811-4832. DOI：10.12263/DZXB.20250800

LI Zhi-yong, JIANG Wei, LIU Hao-jie. A Survey on Visible-Infrared Cross-Modality Person Re-Identification[J]. Acta Electronica Sinica, 2025, 53(12): 4811-4832. DOI：10.12263/DZXB.20250800

摘要

行人重识别（Person Re-identification，ReID）作为智能视频监控系统的核心技术，其核心任务是在非重叠视域的摄像头网络中实现对特定目标行人的高效检索与匹配.然而，传统仅依赖可见光图像的方法在夜间或低照度等复杂光照条件下性能显著下降.为应对这一挑战，可见光-红外行人重识别（Visible-Infrared Person Re-identification，VI-ReID）应运而生，旨在实现可见光图像与红外图像之间的交叉检索.该任务不仅继承了单模态行人重识别中姿态变化、视角差异和遮挡等固有难题，更需克服由成像机理不同所导致的巨大跨模态鸿沟.本文对近年来基于深度学习的可见光-红外跨模态行人重识别方法进行了系统性梳理、归纳与评述，将现有主流方法划分为三大核心类别：（1）基于跨模态网络结构设计的方法，通过精心构造网络架构以提取模态不变的身份特征，具体包括双流特征提取网络、身份信息解耦模块、细粒度特征对齐，以及利用网络结构搜索等设计方法；（2）生成式学习方法，旨在通过模态转换或数据增强弥合模态间差距，涵盖单向或双向图像生成、构建统一中间模态，以及在特征层面进行生成与补偿等策略；（3）基于跨模态相似度学习的方法，聚焦于损失函数与度量学习的设计，通过拉近跨模态正样本对的距离并推开负样本对，主要包括基于样本或中心（代理）的对比学习，以及针对测试阶段优化的跨模态重排序算法.此外，考虑到实际应用中标注成本高昂且标签可能存在噪声或缺失，本文进一步深入探讨了非完全有监督学习范式下的研究进展，系统总结了噪声标签学习、半监督学习及无监督学习等方向所面临的独特挑战与代表性解决方案.为全面评估各类算法的性能，本文在SYSU-MM01、RegDB和LLCM公开数据集上，对不同监督范式下的代表性算法进行了统一的性能对比与分析.最后，本文立足于当前研究的技术瓶颈，对未来发展趋势进行了展望，指出构建更贴近真实场景的多样化数据集、缓解模态数据不平衡问题、推动模型轻量化部署、探索可持续或终身学习机制，以及拓展至视频级或多源异构信息融合的行人重识别等方向将是该领域极具潜力的研究热点，旨在为后续学者提供有价值的理论参考与技术指引.

Abstract

Person re-identification (ReID) is a core technology in intelligent video surveillance systems

with the fundamental objective of efficiently retrieving and matching a specific pedestrian across camera networks with non-overlapping fields of view. However

traditional approaches that rely solely on visible images suffer severe performance degradation under challenging illumination conditions such as nighttime or low-light environments. To address this limitation

visible-infrared person re-identification (VI-ReID) has emerged

aiming to enable cross-modal retrieval between visible and infrared images. This task not only inherits classic challenges from unimodal ReID—such as pose variations

viewpoint changes

and occlusions—but also faces a significant cross-modal discrepancy arising from the intrinsic differences in imaging mechanisms. This paper provides a systematic survey

comprehensive synthesis

and critical review of recent deep learning-based methods for VI-ReID. We categorize existing mainstream approaches into three major groups: (1) cross-modal network architecture design

which constructs specialized network structures to extract modality-invariant identity features

including dual-stream feature extraction networks

identity disentanglement modules

fine-grained feature alignment strategies

and architecture search-based designs; (2) generative learning methods

which seek to bridge the modality gap through modality translation or data augmentation

covering unidirectional or bidirectional image generation

construction of unified intermediate modalities

and feature-level generation and compensation techniques; (3) cross-modal similarity learning

which focuses on designing loss functions and metric learning schemes to pull together positive cross-modal pairs while pushing apart negative ones

primarily encompassing sample- or proxy-based contrastive learning and test-time optimized cross-modal re-ranking algorithms. Moreover

recognizing the high cost of annotation and the prevalence of noisy or incomplete labels in real-world applications

this survey further investigates advances under non-fully-supervised learning paradigms

systematically summarizing the unique challenges and representative solutions in noisy-label learning

semi-supervised learning

and unsupervised learning. To offer a holistic performance evaluation

we conduct unified comparisons and analyses of representative algorithms under different supervision settings on widely adopted public benchmarks—SYSU-MM01

RegDB

and LLCM. Finally

grounded in current technical bottlenecks

we outline promising future directions

including the development of more realistic and diverse datasets

mitigation of modality imbalance

lightweight model deployment

exploration of sustainable or lifelong learning mechanisms

and extension toward video-based or multi-source heterogeneous information-fused ReID. This survey aims to serve as a valuable theoretical reference and technical guide for future researchers in the field.

关键词

Keywords

references

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