DFR-ST: Discriminative feature representation with spatio-temporal cues for vehicle re-identification

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 in viewpoints and illumination conditions pose grand challenges to vehicle re-ID. Conventional solutions focus on designing global visual appearances without sufficient consideration of vehicles' spatio-temporal relationships in different images. This paper proposes a discriminative feature representation with spatio-temporal 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. 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 based on this multi-modal information. Experimental results on four public datasets demonstrate DFR-ST outperforms the state-of-the-art methods, which validates the effectiveness of the proposed method.