摘要
红外与可见光跨模态行人重识别对提升智能视频监控系统的全天作战能力具有重要作用。现有跨模态行人重识别方法通常专注于特征对齐,未重视多模态度量对齐,导致重识别对模态变化不够鲁棒。为此,提出一种基于度量正则化的红外与可见光跨模态行人重识别算法。首先,设计度量正则化损失函数,用于约束不同模态检索模式下匹配行为的差异,提升算法的鲁棒性。其次,考虑到实际监控场景中红外图像的数量少于可见光图像的数量,利用模态数据比例修正交叉熵损失函数,减少模态数据不平衡对模型训练的不利影响。实验结果证明了所提算法的优越性,例如在RegDB数据集由可见光检索红外图像的首位识别率达到89.52%。
Infrared and visible cross-modal person re-identification plays an important role in improving the all-day combat capability of intelligent video surveillance systems.Existing methods usually focus on the alignment of cross-modal features,and neglect the metric alignment among multiple modalities,resulting in re-identification lacking robustness to modal changes.For that,this paper proposes a metric regularized infrared and visible cross-modal person re-identification.First,this paper designs a metric regularized loss function to constrain the difference among matching behaviors under different modal retrieval modes to improve the robustness.Second,considering that the number of infrared images is less than that of visible images in actual surveillance scenes,this paper applies the modal data proportion to modify the cross-entropy function to reduce the adverse effect of the imba-lance between different modalities.Experimental results show the superiority of the proposed method,e.g.,using visible images to retrieval infrared images,the rank-1 identification rate reaches 89.52%on the RegDB dataset.
作者
吴含笑
赵倩倩
朱建清
曾焕强
杜吉祥
廖昀
WU Hanxiao;ZHAO Qianqian;ZHU Jianqing;ZENG Huanqiang;DU Jixiang;LIAO Yun(College of Information Science and Engineering,Huaqiao University,Xiamen,Fujian 361021,China;College of Engineering,Huaqiao University,Quanzhou,Fujian 362011,China;College of Computer Science and Technology,Huaqiao University,Xiamen,Fujian 361021,China;Xiamen Yealink Network Technology Co.,LTD,Xiamen,Fujian 361015,China)
出处
《计算机科学》
CSCD
北大核心
2023年第S01期344-351,共8页
Computer Science
基金
福建省杰出青年科学基金(2022J06023)
国家自然科学基金(61976098)。
关键词
度量正则化损失函数
度量对齐
跨模态
行人重识别
智能视频监控系统
Metric regularized loss function
Metric alignment
Cross-modal
Person re-identification
Intelligent video surveillance systems
