摘要
目标重识别(ReID)技术旨在匹配不同区域摄像头在不同时间拍摄到的同一目标,其核心是通过目标间的细粒度差异实现不同目标的有效区分。因此,目标重识别技术被广泛应用于安防布控、刑侦监控等领域并发挥了重要作用。传统的目标重识别技术通常适用于光照条件良好情况下的可见光模态数据,但在处理黑夜低光照条件下的目标重识别任务时,其性能通常受到严重限制。红外摄像机因其卓越的夜视性能,通常被应用于在低光照条件下采集目标红外图像。因此,跨模态目标重识别技术旨在通过可见光图像匹配红外图像,实现全天候不间断的目标重识别。近年来,跨模态目标重识别技术取得了很大进展,然而,对于现有模型的归纳总结及深入分析仍然欠缺。为此,对跨模态目标重识别领域的相关研究和新颖方法进行了深入调研和总结,讨论了现有方法在实际场景中面临的挑战,并从模型分类和模型评价两个方面对现有方法进行归纳与分析。首先,围绕跨模态目标重识别问题的研究难点,将跨模态目标重识别分为生成式方法和非生成式方法两大类;然后,对当前跨模态重识别领域中广泛使用的评测数据集以及相关评价指标进行了综述与总结;最后,讨论了跨模态重识别领域仍然存在的挑战并对未来发展趋势进行了展望。
Object re-identification(ReID)technology aims to match the same object captured by cameras across different areas at different time.The key is to distinguish different objects through fine-grained differences between different individuals,which is widely used in security control,criminal investigation and monitoring,etc.Traditional ReID technology is usually suitable for visible cameras with good lighting conditions,but its performance is severely limited under low-light conditions.The infrared camera is often used to collect infrared images of objects under low light conditions due to its outstanding night vision performance.Therefore,cross-modality object re-identification technology focuses on achieving uninterrupted object ReID across day and night from visible images to infrared images(VI-ReID),and vice versa.In recent years,VI-ReID technology has made significant progress.However,a comprehensive summary and in-depth analysis of existing models are still lacking.To this end,this paper conducts an in-depth investigation and summary of relevant research and novel methods in the field of VI-ReID.It discusses the challenges faced by existing methods in actual scenarios,and categorizes them from two aspects:model classification and model evaluation.First,focusing on the research challenges,VI-ReID is categorized into generative methods and non-generative methods.Se-condly,the evaluation datasets and evaluation metrics are reviewed and summarized.Finally,the remaining challenges in VI-ReID are discussed and the future development trends are prospected.
作者
崔振宇
周嘉欢
彭宇新
CUI Zhenyu;ZHOU Jiahuan;PENG Yuxin(Wangxuan Institute of Computer Technology,Peking University,Beijing 100871,China;National Key Laboratory for Multimedia Information Processing,Peking University,Beijing 100871,China)
出处
《计算机科学》
CSCD
北大核心
2024年第1期13-25,共13页
Computer Science
基金
国家自然科学基金(61925201,62132001)。
关键词
计算机视觉
目标重识别
跨模态
细粒度特征
表征学习
Computer vision
Object re-identification
Cross-modality
Fine-grained feature
Representation learning
