  Vehicle Re-identification (re-id) over surveillance camera network with
non-overlapping field of view is an exciting and challenging task in
intelligent transportation systems (ITS). Due to its versatile applicability in
metropolitan cities, it gained significant attention. Vehicle re-id matches
targeted vehicle over non-overlapping views in multiple camera network.
However, it becomes more difficult due to inter-class similarity, intra-class
variability, viewpoint changes, and spatio-temporal uncertainty. In order to
draw a detailed picture of vehicle re-id research, this paper gives a
comprehensive description of the various vehicle re-id technologies,
applicability, datasets, and a brief comparison of different methodologies. Our
paper specifically focuses on vision-based vehicle re-id approaches, including
vehicle appearance, license plate, and spatio-temporal characteristics. In
addition, we explore the main challenges as well as a variety of applications
in different domains. Lastly, a detailed comparison of current state-of-the-art
methods performances over VeRi-776 and VehicleID datasets is summarized with
future directions. We aim to facilitate future research by reviewing the work
being done on vehicle re-id till to date.

Computer Vision and Pattern Recognition;Artificial Intelligence;Multimedia