H-net: Unsupervised domain adaptation person re-identification network based on hierarchy

Due to the high cost of manual labeling for supervised person re-identification (re-ID), unsupervised domain adaptation (UDA) person re-ID has been attracting the attention of many scholars. In this research, target domain datasets and source domain datasets are two indispensable datasets, and although there are many different pictures of the same person in the target domain, these pictures are precious to the network in different degrees. However, the existing UDA person re-ID algorithms does not treat different samples in the target domain differently, they just treat positive samples as indistinguishable samples. Not only that, although the triplet loss has been re-identified by unsupervised person re-ID, the noise of the hardest sample hasn't been carried out well. In this paper, a novel and robust network model named unsupervised domain adaptation hierarchical person re-identification network (H-Net) is proposed, which not only effectively reduces the impact of inaccurate identification of the hardest sample but also treats different positive samples differently by hierarchical feature collection. Numerous experimental results on Market-1501 and DukeMTMC-reID demonstrate that the proposed H-Net outperforms the existing methods and can significantly improve the accuracy of person re-ID.