  Person re-identification (re-ID) has gained more and more attention due to
its widespread applications in intelligent video surveillance. Unfortunately,
the mainstream deep learning methods still need a large quantity of labeled
data to train models, and annotating data is an expensive work in real-world
scenarios. In addition, due to domain gaps between different datasets, the
performance is dramatically decreased when re-ID models pre-trained on
label-rich datasets (source domain) are directly applied to other unlabeled
datasets (target domain). In this paper, we attempt to remedy these problems
from two aspects, namely data and methodology. Firstly, we develop a data
collector to automatically generate synthetic re-ID samples in a computer game,
and construct a data labeler to simultaneously annotate them, which free humans
from heavy data collections and annotations. Based on them, we build two
synthetic person re-ID datasets with different scales, "GSPR" and "mini-GSPR"
datasets. Secondly, we propose a synthesis-based multi-domain collaborative
refinement (SMCR) network, which contains a synthetic pretraining module and
two collaborative-refinement modules to implement sufficient learning for the
valuable knowledge from multiple domains. Extensive experiments show that our
proposed framework obtains significant performance improvements over the
state-of-the-art methods on multiple unsupervised domain adaptation tasks of
person re-ID.
