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Authors: Mbasa Molo 1 ; 2 ; Emanuele Carlini 2 ; Luca Ciampi 2 ; Claudio Gennaro 2 and Lucia Vadicamo 2

Affiliations: 1 Department of Computer Science, University of Pisa, Pisa, Italy ; 2 Institute of Information Science and Technologies (ISTI), Italian National Research Council (CNR), Pisa, Italy

Keyword(s): Knowledge Distillation, Computer Vision, Object Recognition, Deep Learning, Edge Computing.

Abstract: The surge of the Internet of Things has sparked a multitude of deep learning-based computer vision applications that extract relevant information from the deluge of data coming from Edge devices, such as smart cameras. Nevertheless, this promising approach introduces new obstacles, including the constraints posed by the limited computational resources on these devices and the challenges associated with the generalization capabilities of the AI-based models against novel scenarios never seen during the supervised training, a situation frequently encountered in this context. This work proposes an efficient approach for detecting vehicles in parking lot scenarios monitored by multiple smart cameras that train their underlying AI-based models by exploiting knowledge distillation. Specifically, we consider an architectural scheme comprising a powerful and large detector used as a teacher and several shallow models acting as students, more appropriate for computational-bounded devices and designed to run onboard the smart cameras. The teacher is pre-trained over general-context data and behaves like an oracle, transferring its knowledge to the smaller nodes; on the other hand, the students learn to localize cars in new specific scenarios without using further labeled data, relying solely on the distilled loss coming from the oracle. Preliminary results show that student models trained only with distillation loss increase their performances, sometimes even outperforming the results achieved by the same models supervised with the ground truth. (More)

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Paper citation in several formats:
Molo, M.; Carlini, E.; Ciampi, L.; Gennaro, C. and Vadicamo, L. (2024). Teacher-Student Models for AI Vision at the Edge: A Car Parking Case Study. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 508-515. DOI: 10.5220/0012376900003660

@conference{visapp24,
author={Mbasa Molo. and Emanuele Carlini. and Luca Ciampi. and Claudio Gennaro. and Lucia Vadicamo.},
title={Teacher-Student Models for AI Vision at the Edge: A Car Parking Case Study},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={508-515},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012376900003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Teacher-Student Models for AI Vision at the Edge: A Car Parking Case Study
SN - 978-989-758-679-8
IS - 2184-4321
AU - Molo, M.
AU - Carlini, E.
AU - Ciampi, L.
AU - Gennaro, C.
AU - Vadicamo, L.
PY - 2024
SP - 508
EP - 515
DO - 10.5220/0012376900003660
PB - SciTePress