Vehicle re-identification with multiple discriminative features based on non-local-attention block

Vehicle re-identification (re-id) technology refers to a vehicle matching under a non-overlapping domain, that is, to confirm whether the vehicle target taken by cameras in different positions at different times is the same vehicle. Different identities of the same type of vehicles are one of the most challenging factors in the field of vehicle re-identification. The key to solve this difficulty is to make full use of the multiple discriminative features of vehicles. Therefore, this paper proposes a multiple discriminative features extraction network (MDFE-Net) that can enhance the distance dependence on the vehicle's multiple discriminative features by non-local attention, which in turn enhances the discriminative power of the network. Meanwhile, to more directly represent the retrieval capability of the model and enhance the rigor of model evaluation, we introduce a novel vehicle re-id model evaluation metric called mean positive sample occupancy (mPSO). Comprehensive experiments implemented on challenging vehicle evaluation datasets (including VeRi-776, VRIC, and VehicleID) show that our model robustly achieves state-of-the-art performances. Moreover, our novel metric mPSO further proves the powerful retrieval capability of the MDFE-Net.