A Triplet-Learnt Coarse-to-Fine Reranking for Vehicle Re-identification

Efkleidis Katsaros

doi:10.5220/0008974005180525

A Triplet-Learnt Coarse-to-Fine Reranking for Vehicle Re-identification

Abstract

Vehicle re-identification refers to the task of matching the same query vehicle across non-overlapping cameras and diverse viewpoints. Research interest on the field emerged with intelligent transportation systems and the necessity for public security maintenance. Compared to person, vehicle re-identification is more intricate, facing the challenges of lower intra-class and higher inter-class similarities. Motivated by deep metric learning advances, we propose a novel, triplet-learnt coarse-to-fine reranking scheme (C2F-TriRe) to address vehicle re-identification. Coarse vehicle features conduct the baseline ranking. Thereafter, a fully connected network maps features to viewpoints. Simultaneously, windshields are detected and respective fine features are extracted to capture custom vehicle characteristics. Conditional to the viewpoint, coarse and fine features are combined to yield a robust reranking. The proposed scheme achieves state-of-the-art performance on the VehicleID dataset and outperforms our baselines by a large margin.

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Efkleidis Katsaros

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Category:: Other publications
Type:: Other publications
Title of issue:: Proc. Int. Conf. Pattern Recognition Applications and Methods ICPRAM strony 518 - 525
Publication year:: 2020
DOI:: Digital Object Identifier (open in new tab) 10.5220/0008974005180525
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