  Vehicle re-identification (Re-ID) has become a popular research topic owing
to its practicability in intelligent transportation systems. Vehicle Re-ID
suffers the numerous challenges caused by drastic variation in illumination,
occlusions, background, resolutions, viewing angles, and so on. To address it,
this paper formulates a multi-order deep cross-distance learning
(\textbf{DCDLearn}) model for vehicle re-identification, where an efficient
one-view CycleGAN model is developed to alleviate exhaustive and enumerative
cross-camera matching problem in previous works and smooth the domain
discrepancy of cross cameras. Specially, we treat the transferred images and
the reconstructed images generated by one-view CycleGAN as multi-order
augmented data for deep cross-distance learning, where the cross distances of
multi-order image set with distinct identities are learned by optimizing an
objective function with multi-order augmented triplet loss and center loss to
achieve the camera-invariance and identity-consistency. Extensive experiments
on three vehicle Re-ID datasets demonstrate that the proposed method achieves
significant improvement over the state-of-the-arts, especially for the small
scale dataset.
