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29.03.2023 | Regular Paper

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

verfasst von: Saifullah Tumrani, Wazir Ali, Rajesh Kumar, Abdullah Aman Khan, Fayaz Ali Dharejo

Erschienen in: Multimedia Systems | Ausgabe 4/2023

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Abstract

Vehicle re-identification is one of the essential application of urban surveillance. Due to enormous variation in inter-class and intra-class resemblance creates a challenge for methods to distinguish between the same vehicles. Additionally, varying illumination and complex environments create significant hurdles for the existing methods to re-identify vehicles. 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.

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Titel: View-aware attribute-guided network for vehicle re-identification
verfasst von: Saifullah Tumrani
Wazir Ali
Rajesh Kumar
Abdullah Aman Khan
Fayaz Ali Dharejo
Publikationsdatum: 29.03.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Multimedia Systems / Ausgabe 4/2023
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-023-01077-y

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