  Vehicle re-identification (re-ID) aims to discover and match the target
vehicles from a gallery image set taken by different cameras on a wide range of
road networks. It is crucial for lots of applications such as security
surveillance and traffic management. The remarkably similar appearances of
distinct vehicles and the significant changes of viewpoints and illumination
conditions take grand challenges to vehicle re-ID. Conventional solutions focus
on designing global visual appearances without sufficient consideration of
vehicles' spatiotamporal relationships in different images. In this paper, we
propose a novel discriminative feature representation with spatiotemporal clues
(DFR-ST) for vehicle re-ID. It is capable of building robust features in the
embedding space by involving appearance and spatio-temporal information. Based
on this multi-modal information, the proposed DFR-ST constructs an appearance
model for a multi-grained visual representation by a two-stream architecture
and a spatio-temporal metric to provide complementary information. Experimental
results on two public datasets demonstrate DFR-ST outperforms the
state-of-the-art methods, which validate the effectiveness of the proposed
method.
