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Authors: Paola Natalia Cañas ; Juan Diego Ortega ; Marcos Nieto and Oihana Otaegui

Affiliation: Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Spain

Keyword(s): Driver Monitoring Systems, Deep Learning, Action Recognition, Distraction Detection, Distracted Driver, ADAS, Autonomous Driving.

Abstract: The recently presented Driver Monitoring Dataset (DMD) extends research lines for Driver Monitoring Systems. We intend to explore this dataset and apply commonly used methods for action recognition to this specific context, from image-based to video-based analysis. Specially, we aim to detect driver distraction by applying action recognition techniques to classify a list of distraction-related activities. This is now possible thanks to the DMD, that offers recordings of distracted drivers in video format. A comparison between different state-of-the-art models for image and video classification is reviewed. Also, we discuss the feasibility of implementing image-based or video-based models in a real-context driver monitoring system. Preliminary results are presented in this article as a point of reference to future work on the DMD.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Cañas, P.; Ortega, J.; Nieto, M. and Otaegui, O. (2021). Detection of Distraction-related Actions on DMD: An Image and a Video-based Approach Comparison. 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 458-465. DOI: 10.5220/0010244504580465

@conference{visapp21,
author={Paola Natalia Cañas. and Juan Diego Ortega. and Marcos Nieto. and Oihana Otaegui.},
title={Detection of Distraction-related Actions on DMD: An Image and a Video-based Approach Comparison},
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={458-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010244504580465},
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 - Detection of Distraction-related Actions on DMD: An Image and a Video-based Approach Comparison
SN - 978-989-758-488-6
IS - 2184-4321
AU - Cañas, P.
AU - Ortega, J.
AU - Nieto, M.
AU - Otaegui, O.
PY - 2021
SP - 458
EP - 465
DO - 10.5220/0010244504580465
PB - SciTePress

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