  Vehicle re-identification (Re-ID) is an active task due to its importance in
large-scale intelligent monitoring in smart cities. Despite the rapid progress
in recent years, most existing methods handle vehicle Re-ID task in a
supervised manner, which is both time and labor-consuming and limits their
application to real-life scenarios. Recently, unsupervised person Re-ID methods
achieve impressive performance by exploring domain adaption or clustering-based
techniques. However, one cannot directly generalize these methods to vehicle
Re-ID since vehicle images present huge appearance variations in different
viewpoints. To handle this problem, we propose a novel viewpoint-aware
clustering algorithm for unsupervised vehicle Re-ID. In particular, we first
divide the entire feature space into different subspaces according to the
predicted viewpoints and then perform a progressive clustering to mine the
accurate relationship among samples. Comprehensive experiments against the
state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi and
VeRi-Wild validate the promising performance of the proposed method in both
with and without domain adaption scenarios while handling unsupervised vehicle
Re-ID.
