计算机工程与应用2024，Vol.60Issue(4)：133-141,9.DOI:10.3778/j.issn.1002-8331.2209-0120
结合数据增强与特征融合的跨模态行人重识别
Cross-Modality Person Re-identification Combined with Data Augmentation and Feature Fusion
摘要
Abstract
The difficulty of visible-infrared person re-identification problem lies in the large modal difference between images.Most existing methods alleviate the modal difference by generating fake images through generative adversarial net-works or extracting modal shared features on the original image.However,training a generative adversarial network con-sumes a lot of computational resources and generates fake images that are prone to introduce noise,and extracting modal shared features can also result in the loss of important differentiated features.To address these problems,a new cross-modality person re-identification network is proposed.Firstly,automatic data augmentation is used to improve model robust-ness.Then,instance regularization is used in the network to reduce modal differences.Finally,the pedestrian features of different scales extracted by each layer of the network are organically fused,and the fused features contain more differenti-ated features related to pedestrian identity.The proposed method achieves Rank-1/mAP of 69.47%/65.05%in the all-search mode of the SYSU-MM01,and Rank-1/mAP of 85.73%/77.77%in the visible to infrared modes of the RegDB,respectively.The experimental results have a significant improvement effect.关键词
跨模态/行人重识别/自动数据增强/特征融合Key words
cross-modality/person re-identification/automatic data augmentation/feature fusion分类
信息技术与安全科学引用本文复制引用
宋雨,王帮海,曹钢钢..结合数据增强与特征融合的跨模态行人重识别[J].计算机工程与应用,2024,60(4):133-141,9.基金项目
国家自然科学基金面上项目(62072119). （62072119）
