基于图像和特征联合约束的跨模态行人重识别  

Cross-modality Person Re-identification Based on Joint Constraints of Image and Feature

作　　者：张玉康 谭磊[1,2] 陈靓影 ZHANG Yu-Kang;TAN Lei;CHEN Jing-Ying(National Engineering Laboratory for Big Data for Education,Central China Normal University,Wuhan 430072;National Engineering Research Center for E-Learning,Central China Normal University,Wuhan 430072)

机构地区：[1]华中师范大学教育大数据国家工程实验室,武汉430072 [2]华中师范大学国家数字化学习工程技术研究中心,武汉430072

出　　处：《自动化学报》2021年第8期1943-1950,共8页Acta Automatica Sinica

基　　金：国家自然科学基金面上项目(61977027);湖北省科技创新重大专项(2019AAA044)资助。

摘　　要：近年来,基于可见光与近红外的行人重识别研究受到业界人士的广泛关注.现有方法主要是利用二者之间的相互转换以减小模态间的差异.但由于可见光图像和近红外图像之间的数据具有独立且分布不同的特点,导致其相互转换的图像与真实图像之间存在数据差异.因此,本文提出了一个基于图像层和特征层联合约束的可见光与近红外相互转换的中间模态,不仅实现了行人身份的一致性,而且减少了模态间转换的差异性.此外,考虑到跨模态行人重识别数据集的稀缺性,本文还构建了一个跨模态的行人重识别数据集,并通过大量的实验证明了文章所提方法的有效性,本文所提出的方法在经典公共数据集SYSU-MM01上比D2RL算法在Rank-1和mAP上分别高出4.2%和3.7%,该方法在本文构建的Parking-01数据集的近红外检索可见光模式下比ResNet-50算法在Rank-1和mAP上分别高出10.4%和10.4%.In recent years,the research of person re-identification based on visible and near-infrared has attracted widespread attention from the industry.The existing methods mainly use the mutual conversion between them to reduce the difference between their modalities.However,due to the problem of data independence and different distribution between visible image and near-infrared image,there is a large difference between the converted image and the real image,which leads to further improvement of this method.Therefore,this paper proposes a middle modality of conversion between visible and near-infrared modality.So visible and near-infrared can be seamlessly transferred,realizing the identity consistency of person and reducing the difference of conversion between modalities.In addition,considering the scarcity of cross modality person re-identification dataset,this paper also constructs a cross modality person re-identification dataset,and proves the effectiveness of the proposed method through a large number of experiments.In the All-Search Single-shot mode on the SYSU-MM01 dataset,the result of the proposed method is 4.2%and 3.7%higher than Rank1 and mAP using the D2RL algorithm,respectively.Compared with ResNet-50 algorithm,the result of the proposed method on the Parking-01 dataset constructed in this paper is 10.4%and 10.4%higher in Rank-1 and mAP respectively.

关 键 词：跨模态 行人重识别 中间模态 联合约束 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]