  Most of researchers use the vehicle re-identification based on
classification. This always requires an update with the new vehicle models in
the market. In this paper, two types of vehicle re-identification will be
presented. First, the standard method, which needs an image from the search
vehicle. VRIC and VehicleID data set are suitable for training this module. It
will be explained in detail how to improve the performance of this method using
a trained network, which is designed for the classification. The second method
takes as input a representative image of the search vehicle with similar
make/model, released year and colour. It is very useful when an image from the
search vehicle is not available. It produces as output a shape and a colour
features. This could be used by the matching across a database to re-identify
vehicles, which look similar to the search vehicle. To get a robust module for
the re-identification, a fine-grained classification has been trained, which
its class consists of four elements: the make of a vehicle refers to the
vehicle's manufacturer, e.g. Mercedes-Benz, the model of a vehicle refers to
type of model within that manufacturer's portfolio, e.g. C Class, the year
refers to the iteration of the model, which may receive progressive alterations
and upgrades by its manufacturer and the perspective of the vehicle. Thus, all
four elements describe the vehicle at increasing degree of specificity. The aim
of the vehicle shape classification is to classify the combination of these
four elements. The colour classification has been separately trained. The
results of vehicle re-identification will be shown. Using a developed tool, the
re-identification of vehicles on video images and on controlled data set will
be demonstrated. This work was partially funded under the grant.
