A Camera-Aware Three-Stage Method for Fully Unsupervised Person Re-identification

Most of existing unsupervised person re-identification methods focus on cross-domain adaptation. In order to further relieve the dependence on manual labels, we propose a camera-aware three-stage method for fully unsupervised person re-identification which only requires the unlabeled target dataset. We exploit camera labels and divide the learning process into three relatively easy sub-tasks: initialization by instance discrimination, intra-camera learning and inter-camera learning. The first stage regards each person image as an instance and tries to distinguish each image. The second stage performs intra-camera clustering while the last stage performs clustering and training on the whole dataset. These three stages share the backbone network. Finally, our method substantially boosts the performance stage by stage without any manual ID annotation. We conduct extensive experiments on three large-scale image-based datasets, including Market-1501, DukeMTMC-reID and MSMT17. The results demonstrate that our method achieves the state-of-the-art performance.