  Vehicle re-identification (V-reID) has become significantly popular in the
community due to its applications and research significance. In particular, the
V-reID is an important problem that still faces numerous open challenges. This
paper reviews different V-reID methods including sensor based methods, hybrid
methods, and vision based methods which are further categorized into
hand-crafted feature based methods and deep feature based methods. The vision
based methods make the V-reID problem particularly interesting, and our review
systematically addresses and evaluates these methods for the first time. We
conduct experiments on four comprehensive benchmark datasets and compare the
performances of recent hand-crafted feature based methods and deep feature
based methods. We present the detail analysis of these methods in terms of mean
average precision (mAP) and cumulative matching curve (CMC). These analyses
provide objective insight into the strengths and weaknesses of these methods.
We also provide the details of different V-reID datasets and critically discuss
the challenges and future trends of V-reID methods.
