Abstract
Unsupervised person re-identification (re-ID) aims to learn identity information from a source domain (e.g. one surveillance system) and apply it to a target domain (e.g. a different surveillance system). This is challenging due to occlusion, viewpoint, and illumination variations between the different domains (i.e. systems). In this paper, we propose a neural network architecture, known as Synthetic Model Bank (SMB), to address illumination variation in unsupervised person re-ID. The basic idea of SMB is to use synthetic data for training different re-ID models for different illumination conditions. From our experiments, the proposed SMB outperforms other synthetic augmentation methods on several re-ID benchmarks.