Person re-identification (Re-ID) aims to match the image frames which contain
the same person in the surveillance videos. Most of the Re-ID algorithms
conduct supervised training in some small labeled datasets, so directly
deploying these trained models to the real-world large camera networks may lead
to a poor performance due to underfitting. The significant difference between
the source training dataset and the target testing dataset makes it challenging
to incrementally optimize the model. To address this challenge, we propose a
novel solution by transforming the unlabeled images in the target domain to fit
the original classifier by using our proposed similarity preserved generative
adversarial networks model, SimPGAN. Specifically, SimPGAN adopts the
generative adversarial networks with the cycle consistency constraint to
transform the unlabeled images in the target domain to the style of the source
domain. Meanwhile, SimPGAN uses the similarity consistency loss, which is
measured by a siamese deep convolutional neural network, to preserve the
similarity of the transformed images of the same person. Comprehensive
experiments based on multiple real surveillance datasets are conducted, and the
results show that our algorithm is better than the state-of-the-art
cross-dataset unsupervised person Re-ID algorithms.

本研究提出了使用基于相似性保持生成对抗网络模型 SimPGAN，将目标域中未标记图像转化为原始分类器的解决方案，通过在多个真实监控数据集上的全面实验，研究结果表明，该算法优于现有的基于跨数据集的无监督人员重新识别算法。