  Vehicle Re-identification (Re-ID) has been broadly studied in the last
decade; however, the different camera view angle leading to confused
discrimination in the feature subspace for the vehicles of various poses, is
still challenging for the Vehicle Re-ID models in the real world. To promote
the Vehicle Re-ID models, this paper proposes to synthesize a large number of
vehicle images in the target pose, whose idea is to project the vehicles of
diverse poses into the unified target pose so as to enhance feature
discrimination. Considering that the paired data of the same vehicles in
different traffic surveillance cameras might be not available in the real
world, we propose the first Pair-flexible Pose Guided Image Synthesis method
for Vehicle Re-ID, named as VehicleGAN in this paper, which works for both
supervised and unsupervised settings without the knowledge of geometric 3D
models. Because of the feature distribution difference between real and
synthetic data, simply training a traditional metric learning based Re-ID model
with data-level fusion (i.e., data augmentation) is not satisfactory, therefore
we propose a new Joint Metric Learning (JML) via effective feature-level fusion
from both real and synthetic data. Intensive experimental results on the public
VeRi-776 and VehicleID datasets prove the accuracy and effectiveness of our
proposed VehicleGAN and JML.
