  Existing vehicle re-identification (re-id) evaluation benchmarks consider
strongly artificial test scenarios by assuming the availability of high quality
images and fine-grained appearance at an almost constant image scale,
reminiscent to images required for Automatic Number Plate Recognition, e.g.
VeRi-776. Such assumptions are often invalid in realistic vehicle re-id
scenarios where arbitrarily changing image resolutions (scales) are the norm.
This makes the existing vehicle re-id benchmarks limited for testing the true
performance of a re-id method. In this work, we introduce a more realistic and
challenging vehicle re-id benchmark, called Vehicle Re-Identification in
Context (VRIC). In contrast to existing datasets, VRIC is uniquely
characterised by vehicle images subject to more realistic and unconstrained
variations in resolution (scale), motion blur, illumination, occlusion, and
viewpoint. It contains 60,430 images of 5,622 vehicle identities captured by 60
different cameras at heterogeneous road traffic scenes in both day-time and
night-time.
