Person re-identification (re-ID) aims to match person images under different cameras with disjoint views. Although supervised re-ID has achieved great progress, unsupervised cross-domain re-ID remains a challenging work due to domain bias. In this work, we divide cross-domain re-ID task into two phases: domain-invariant features learning and domain-specific features learning. Our contributions are twofold. (i) To achieve domain-invariant features learning, a novel model called Pedestrian General Similarity (PGS) is proposed, which can eliminate two main factors that cause domain bias: image style and background. Compared with the existing re-ID models, PGS has better generalization ability. (ii) A novel pseudo label assignment method named Mutual Nearest Neighbors Pseudo Labeling (MNNPL) is proposed, which calculates pseudo labels based on the similarity between samples in the target domain, and the resulting pseudo labels are used to guide domain-specific feature learning. Extensive experiments are conducted on several large scale datasets, the results show that our method outperforms most published unsupervised cross-domain methods by a large margin.

人员重新识别（re-ID）的目的是将视角不相交的不同摄像机下的人员图像进行匹配。尽管有监督的re-ID已经取得了很大的进步，但是由于域的偏见，无监督的跨域re-ID仍然是一项具有挑战性的工作。在这项工作中，我们将跨域re-ID任务分为两个阶段：领域不变特征学习和领域特定特征学习。我们的贡献是双重的。（i）为了实现领域不变特征学习，提出了一种称为行人通用相似度（PGS）的新颖模型，该模型可以消除引起领域偏差的两个主要因素：图像样式和背景。与现有的re-ID模型相比，PGS具有更好的泛化能力。（ii）提出了一种新的伪标签分配方法，称为相互最近邻居伪标签（MNNPL），它根据目标域中样本之间的相似性来计算伪标记，然后将所得伪标记用于指导特定于域的特征学习。在几个大型数据集上进行了广泛的实验，结果表明我们的方法在很大程度上优于大多数已发布的无监督跨域方法。