  Existing vehicle re-identification methods mainly rely on the single query,
which has limited information for vehicle representation and thus significantly
hinders the performance of vehicle Re-ID in complicated surveillance networks.
In this paper, we propose a more realistic and easily accessible task, called
multi-query vehicle Re-ID, which leverages multiple queries to overcome
viewpoint limitation of single one. Based on this task, we make three major
contributions. First, we design a novel viewpoint-conditioned network (VCNet),
which adaptively combines the complementary information from different vehicle
viewpoints, for multi-query vehicle Re-ID. Moreover, to deal with the problem
of missing vehicle viewpoints, we propose a cross-view feature recovery module
which recovers the features of the missing viewpoints by learnt the correlation
between the features of available and missing viewpoints. Second, we create a
unified benchmark dataset, taken by 6142 cameras from a real-life
transportation surveillance system, with comprehensive viewpoints and large
number of crossed scenes of each vehicle for multi-query vehicle Re-ID
evaluation. Finally, we design a new evaluation metric, called mean cross-scene
precision (mCSP), which measures the ability of cross-scene recognition by
suppressing the positive samples with similar viewpoints from same camera.
Comprehensive experiments validate the superiority of the proposed method
against other methods, as well as the effectiveness of the designed metric in
the evaluation of multi-query vehicle Re-ID.
