  Compared with existing vehicle re-identification (ReID) tasks conducted with
datasets collected by fixed surveillance cameras, vehicle ReID for unmanned
aerial vehicle (UAV) is still under-explored and could be more challenging.
Vehicles with the same color and type show extremely similar appearance from
the UAV's perspective so that mining fine-grained characteristics becomes
necessary. Recent works tend to extract distinguishing information by regional
features and component features. The former requires input images to be aligned
and the latter entails detailed annotations, both of which are difficult to
meet in UAV application. In order to extract efficient fine-grained features
and avoid tedious annotating work, this letter develops an unsupervised
self-aligned network consisting of three branches. The network introduced a
self-alignment module to convert the input images with variable orientations to
a uniform orientation, which is implemented under the constraint of triple loss
function designed with spatial features. On this basis, spatial features,
obtained by vertical and horizontal segmentation methods, and global features
are integrated to improve the representation ability in embedded space.
Extensive experiments are conducted on UAV-VeID dataset, and our method
achieves the best performance compared with recent ReID works.
