Vehicle re-identification is the task of identifying the same vehicle across multiple cameras.
In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras.
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
One of the most challenging problems is to learn an efficient representation for a vehicle from its multi-viewpoint images.
We describe in this paper a Two-Stream Siamese Neural Network for vehicle re-identification.
Results show that T2TP outperforms I2TP for MCD and RSCR.
It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforms the evaluated baselines and state-of-the-art vehicle ReID approaches.