一种域增强和域自适应的换衣行人重识别范式

张培煦; 胡冠宇; 杨新宇

doi:10.19665/j.issn1001-2400.20221106

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一种域增强和域自适应的换衣行人重识别范式

信息与通信工程 & 计算机科学与技术 | 更新时间：2023-11-30

- 一种域增强和域自适应的换衣行人重识别范式
- 暂无标题
- 西安电子科技大学学报 2023年50卷第5期页码：87-94
- 作者机构：
  
  西安交通大学计算机科学与技术学院,陕西西安 710049
- 作者简介：
  
  [ "张培煦(1998—),男,西安交通大学科研助理,E-mail:[email protected];" ]
  [ "胡冠宇(1995—),男,西安交通大学博士研究生,E-mail:[email protected]" ]
  杨新宇(1973—),男,教授,E-mail:[email protected]
- 基金信息：
  
  国家自然科学基金(62072366)
- DOI：10.19665/j.issn1001-2400.20221106
  中图分类号：
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张培煦, 胡冠宇, 杨新宇. 一种域增强和域自适应的换衣行人重识别范式[J]. 西安电子科技大学学报, 2023,50(5):87-94.
张培煦, 胡冠宇, 杨新宇. 一种域增强和域自适应的换衣行人重识别范式[J]. 西安电子科技大学学报, 2023,50(5):87-94. DOI： 10.19665/j.issn1001-2400.20221106.

DOI：

摘要

为了解决服装变化对行人重识别模型识别人物身份准确率的影响,提出一个基于域增强和域自适应的换衣行人重识别范式,使模型在不同的域中学习通用鲁棒的身份表示特征。首先设计了一种服装语义感知的域数据增强方法,根据人体语义信息,在不改变目标人物身份的情况下,分别改变样本衣服裤子的颜色,生成同人同衣不同色的域数据,填补换衣数据域单一问题;其次设计了一个多正类域自适应损失函数,该函数根据不同域数据在模型训练中所做出贡献的不同,为多正类数据损失赋予不同权重,迫使模型专注于样本的通用身份特征的学习。实验证明,在不影响非换衣行人重识别准确度的情况下,该方法在PRCC和CCVID换衣数据集上的首位命中率和平均精度均值达到了约59.5%、60.0%和88.0%、84.5%。对比于其他方法,这种方法具有更高的准确率和更强的鲁棒性,显著提高了模型识别换衣行人的能力。

Abstract

In order to solve the influence of the clothing change on the model’s recognition accuracy of the personal identity,a clothes-changing person re-identification paradigm based on domain augmentation and adaptation is proposed,which enables the model to learn general robust identity representation features in different domains.First,a clothing semantic-aware domain data enhancement method is designed based on the semantic information of the human body,which changes the color of sample clothes without changing the identity of the target person to fill the lack of domain diversity in the data; second,a multi-positive class domain adaptive loss function is designed,which assigns differential weights to the multi-positive class data losses according to the different contributions made by different domain data in the model training,forcing the model to focus on the learning of generic identity features of the samples.Experiments demonstrate that the method achieves 59.5%,60.0%,and 88.0%,84.5% of Rank-1 and mAP on two clothing change datasets,PRCC and CCVID,without affecting the accuracy of non-clothing person re-identification.Compared with other methods,this method has a higher accuracy and stronger robustness and significantly improves the model’s ability to recognize persons.

关键词

人工智能计算机视觉行人重识别域自适应数据增强

Keywords

artificial intelligencecomputer visionperson re-identificationdomain adaptationdata augmentation

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