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Authors: Alessandro Simoni 1 ; Andrea D’Eusanio 2 ; Stefano Pini 1 ; Guido Borghi 3 and Roberto Vezzani 2 ; 1

Affiliations: 1 Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, 41125 Modena, Italy ; 2 Artificial Intelligence Research and Innovation Center, University of Modena and Reggio Emilia, 41125 Modena, Italy ; 3 Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy

Keyword(s): Car Model Classification, Vehicle Keypoint Localization, Multi-task Learning.

Abstract: In this paper, we present a novel multi-task framework which aims to improve the performance of car model classification leveraging visual features and pose information extracted from single RGB images. In particular, we merge the visual features obtained through an image classification network and the features computed by a model able to predict the pose in terms of 2D car keypoints. We show how this approach considerably improves the performance on the model classification task testing our framework on a subset of the Pascal3D+ dataset containing the car classes. Finally, we conduct an ablation study to demonstrate the performance improvement obtained with respect to a single visual classifier network.

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Paper citation in several formats:
Simoni, A.; D’Eusanio, A.; Pini, S.; Borghi, G. and Vezzani, R. (2021). Improving Car Model Classification through Vehicle Keypoint Localization. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 354-361. DOI: 10.5220/0010207803540361

@conference{visapp21,
author={Alessandro Simoni. and Andrea D’Eusanio. and Stefano Pini. and Guido Borghi. and Roberto Vezzani.},
title={Improving Car Model Classification through Vehicle Keypoint Localization},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={354-361},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010207803540361},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Improving Car Model Classification through Vehicle Keypoint Localization
SN - 978-989-758-488-6
IS - 2184-4321
AU - Simoni, A.
AU - D’Eusanio, A.
AU - Pini, S.
AU - Borghi, G.
AU - Vezzani, R.
PY - 2021
SP - 354
EP - 361
DO - 10.5220/0010207803540361
PB - SciTePress

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