View-aware attribute-guided network for vehicle re-identification

Abstract

Vehicle re-identification is one of the essential applications for intelligent transportation systems and urban surveillance. However, enormous variation in inter-class and intra-class resemblance creates a challenge for methods to distinguish between the same vehicles with different views. Additionally, diversified illumination and complicated environments create significant hurdles for the existing methods. We present a multi-guided learning method in this paper that uses multi-attribute and view point information, while also enhancing the robustness of feature extraction. The multi-attribute sub-network learns discriminative features like, i.e. color and type of vehicle. Moreover, the view predictor network adds extra information to the feature embedding and To validate the effectiveness of our framework, experiments on two benchmark datasets VeRi-776 and VehicleID are conducted. Experimental results illustrate our framework achieved comparative performance. VAAG CAR A CAR BFig. 1 The figure shows two separate cars from distinct views from VeRi-776. Car A on the top row illustrates several views from various angles. Car B in the lower row has a diversity of viewpoints as well. These cars have very similar appearances, and it's difficult to tell them apart. Car A appears to be different than its other views and identical to Car B from various perspectives.

Vehicle re-identification method based on multi-attribute dense linking network combined with distance control module

“…MSCL (Yuefeng et al, 2022) achieves unsupervised vehicle re-identification through the integration of the Discrete Sample Separation module and Mixed Sample Contrastive Learning. VAAG (Tumrani et al, 2023) addresses the re-identification task by learning robust discriminative features encompassing camera views, vehicle types, and vehicle colors.…”