  Vehicle re-identification (Re-ID) is urgently demanded to alleviate
thepressure caused by the increasingly onerous task of urban traffic
management. Multiple challenges hamper the applications of vision-based vehicle
Re-ID methods: (1) The appearances of different vehicles of the same
brand/model are often similar; However, (2) the appearances of the same vehicle
differ significantly from different viewpoints. Previous methods mainly use
manually annotated multi-attribute datasets to assist the network in getting
detailed cues and in inferencing multi-view to improve the vehicle Re-ID
performance. However, finely labeled vehicle datasets are usually unattainable
in real application scenarios. Hence, we propose a Discriminative-Region
Attention and Orthogonal-View Generation (DRA-OVG) model, which only requires
identity (ID) labels to conquer the multiple challenges of vehicle Re-ID.The
proposed DRA model can automatically extract the discriminative region
features, which can distinguish similar vehicles. And the OVG model can
generate multi-view features based on the input view features to reduce the
impact of viewpoint mismatches. Finally, the distance between vehicle
appearances is presented by the discriminative region features and multi-view
features together. Therefore, the significance of pairwise distance measure
between vehicles is enhanced in acomplete feature space. Extensive experiments
substantiate the effectiveness of each proposed ingredient, and experimental
results indicate that our approach achieves remarkable improvements over the
state- of-the-art vehicle Re-ID methods on VehicleID and VeRi-776 datasets.
