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Authors: Mohamed Dhia Elhak Besbes 1 ; Yousri Kessentini 2 and Hedi Tabia 1

Affiliations: 1 IBISC, Univ. Evry, Université Paris-Saclay, 91025, Evry, France ; 2 Digital Research Center of Sfax, Sfax, Tunisia

Keyword(s): Fine-grained Vehicle Recognition, Convolutional Neural Network, Multi-stream Fusion.

Abstract: Vehicle recognition generally aims to classify vehicles based on make, model and year of manufacture. It is a particularly hard problem due to the large number of classes and small inter-class variations. To handle this problem recent state of the art methods use Convolutional Neural Network (CNN). These methods have however several limitations since they extract unstructured vehicle features used for the recognition task. In this paper, we propose more structured feature extraction method by leveraging robust multi-stream deep networks architecture. We employ a novel dynamic combination technique to aggregate different vehicle part features with the entire image. This allows combining global representation with local features. Our system which has been evaluated on publicly available datasets is able to learn highly discriminant representation and achieves state-of-the-art result.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Besbes, M.; Kessentini, Y. and Tabia, H. (2020). Multi-stream Deep Networks for Vehicle Make and Model Recognition. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 413-419. DOI: 10.5220/0008975404130419

@conference{visapp20,
author={Mohamed Dhia Elhak Besbes. and Yousri Kessentini. and Hedi Tabia.},
title={Multi-stream Deep Networks for Vehicle Make and Model Recognition},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={413-419},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008975404130419},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Multi-stream Deep Networks for Vehicle Make and Model Recognition
SN - 978-989-758-402-2
IS - 2184-4321
AU - Besbes, M.
AU - Kessentini, Y.
AU - Tabia, H.
PY - 2020
SP - 413
EP - 419
DO - 10.5220/0008975404130419
PB - SciTePress

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