Unsupervised Person Re-identification Using Reliable and Soft Labels

Abstract

In this paper, we propose unsupervised person re-identification (ReID) using reliable and soft labels. We provide unsupervised person ReID to consider unknown pedestrian queries. We update ResNet model for person ReID based on reliable and soft labels. First, we perform unsupervised clustering on person images under different cameras, and select samples based similarity between images and cluster centers. Then, we conduct re-clustering for the selected samples and assign labels to them, i.e. reliable labels. We get probability of the unselected samples, i.e. soft labels. Finally, we update ResNet model for person ReID using reliable and soft labels. Experiments on Market-1501 and DukeMTMC-ReID demonstrate that the proposed method outperforms state-of-the-arts for unsupervised person ReID in terms of the cosine distance and accuracy.