  Vehicle re-identification (reID) often requires recognize a target vehicle in
large datasets captured from multi-cameras. It plays an important role in the
automatic analysis of the increasing urban surveillance videos, which has
become a hot topic in recent years. However, the appearance of vehicle images
is easily affected by the environment that various illuminations, different
backgrounds and viewpoints, which leads to the large bias between different
cameras. To address this problem, this paper proposes a cross-camera adaptation
framework (CCA), which smooths the bias by exploiting the common space between
cameras for all samples. CCA first transfers images from multi-cameras into one
camera to reduce the impact of the illumination and resolution, which generates
the samples with the similar distribution. Then, to eliminate the influence of
background and focus on the valuable parts, we propose an attention alignment
network (AANet) to learn powerful features for vehicle reID. Specially, in
AANet, the spatial transfer network with attention module is introduced to
locate a series of the most discriminative regions with high-attention weights
and suppress the background. Moreover, comprehensive experimental results have
demonstrated that our proposed CCA can achieve excellent performances on
benchmark datasets VehicleID and VeRi-776.
