  With the development of smart cities, urban surveillance video analysis will
play a further significant role in intelligent transportation systems.
Identifying the same target vehicle in large datasets from non-overlapping
cameras should be highlighted, which has grown into a hot topic in promoting
intelligent transportation systems. However, vehicle re-identification (re-ID)
technology is a challenging task since vehicles of the same design or
manufacturer show similar appearance. To fill these gaps, we tackle this
challenge by proposing Triplet Center Loss based Part-aware Model (TCPM) that
leverages the discriminative features in part details of vehicles to refine the
accuracy of vehicle re-identification. TCPM base on part discovery is that
partitions the vehicle from horizontal and vertical directions to strengthen
the details of the vehicle and reinforce the internal consistency of the parts.
In addition, to eliminate intra-class differences in local regions of the
vehicle, we propose external memory modules to emphasize the consistency of
each part to learn the discriminating features, which forms a global dictionary
over all categories in dataset. In TCPM, triplet-center loss is introduced to
ensure each part of vehicle features extracted has intra-class consistency and
inter-class separability. Experimental results show that our proposed TCPM has
an enormous preference over the existing state-of-the-art methods on benchmark
datasets VehicleID and VeRi-776.
