  Vehicle Re-ID has recently attracted enthusiastic attention due to its
potential applications in smart city and urban surveillance. However, it
suffers from large intra-class variation caused by view variations and
illumination changes, and inter-class similarity especially for different
identities with the similar appearance. To handle these issues, in this paper,
we propose a novel deep network architecture, which guided by meaningful
attributes including camera views, vehicle types and colors for vehicle Re-ID.
In particular, our network is end-to-end trained and contains three subnetworks
of deep features embedded by the corresponding attributes (i.e., camera view,
vehicle type and vehicle color). Moreover, to overcome the shortcomings of
limited vehicle images of different views, we design a view-specified
generative adversarial network to generate the multi-view vehicle images. For
network training, we annotate the view labels on the VeRi-776 dataset. Note
that one can directly adopt the pre-trained view (as well as type and color)
subnetwork on the other datasets with only ID information, which demonstrates
the generalization of our model. Extensive experiments on the benchmark
datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the
promising performance and yields to a new state-of-the-art for vehicle Re-ID.

Computer Vision and Pattern Recognition