基于局部语义特征不变性的跨域行人重识别

行人重识别是刑侦案件中重要的侦查手段，而跨域是行人重识别的主要挑战之一，也是制约其实际应用的瓶颈问题。在带标签的源域和无标签的目标域学习跨域行人局部语义不变性特征模型。首先，在源域上通过只含有行人标识无部件标签的监督学习方式学习行人的各部件特征，并在源域和目标域上采用无监督学习方式对齐行人部件。然后，基于对齐后的行人全局与局部特征，引入特征模板池存储对齐后的目标域全局和局部特征，并设计了跨域不变性损失函数进行特征不变性约束，提高行人重识别的跨域适应能力。最后，在Market-1501、DukeMTMC-reID和MSMT17数据集之间开展了跨域行人重识别验证实验，实验结果表明，所提方法在跨域行人重识别上取得了显著的性能提升。 

Cross-domain person re-identification based on partial semantic feature invariance

Cross-domain is one of the main challenges of person re-identification which is an important investigative method in criminal investigation cases, and that restricts the practical application of re-identification. In this paper, cross-domain invariance feature model based on pedestrian partial semantics is learned from the labeled source domain and the unlabeled target domain. First, features of person parts are learned by supervised learning with only pedestrian signs without labels of parts, and pedestrian parts are aligned by unsupervised learning in the source and target domains. Second, based on the aligned global and local features, feature template pooling is introduced to store the aligned global and local partial features of the target domain, and cross-domain invariance loss function is designed for feature invariance constraints to improve the cross-domain adaptability of person re-identification. Finally, verification experiments of cross-domain person re-identification are conducted on the Market-1501, DukeMTMC-reID and MSMT17 datasets. The experiment results show that the proposed method achieves significant performance improvements in cross-domain person re-identification.