Hard Spatial Attention Framework for Driver Action Recognition at Nighttime

Karam Abdullah, Karam Abdullah, Imen Jegham, Imen Jegham, Mohamed Mahjoub, Anouar Ben Khalifa, Anouar Ben Khalifa

2023

Abstract

Driver monitoring has become a key challenge in both computer vision and intelligent transportation system research fields due to its high potential to save pedestrians, drivers, and passengers’ lives. In fact, a variety of issues related to driver action classification in real-world driving settings are present and make classification a challenging task. Recently, driver in-vehicle action relying on deep neural networks has made significant progress. Though promising classification results have been achieved in the daytime, the performance in the nighttime remains far from satisfactory. In addition, deep learning techniques treat the whole input data with the same importance which is confusing. In this work, a nighttime driver action classification network called hard spatial attention is proposed. Our approach effectively captures the relevant dynamic spatial information of the cluttered driving scenes under low illumination for an efficient driver action classification. Experiments are performed on the unique public realistic driver action dataset recorded at nighttime 3MDAD dataset. Our approach outperforms state-of-the-art methods’ classification accuracies on both side and front views.

Paper Citation

in Harvard Style

Abdullah K., Jegham I., Mahjoub M. and Ben Khalifa A. (2023). Hard Spatial Attention Framework for Driver Action Recognition at Nighttime. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 964-971. DOI: 10.5220/0011846100003393

in Bibtex Style

@conference{icaart23,
author={Karam Abdullah and Imen Jegham and Mohamed Mahjoub and Anouar Ben Khalifa},
title={Hard Spatial Attention Framework for Driver Action Recognition at Nighttime},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={964-971},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011846100003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Hard Spatial Attention Framework for Driver Action Recognition at Nighttime
SN - 978-989-758-623-1
AU - Abdullah K.
AU - Jegham I.
AU - Mahjoub M.
AU - Ben Khalifa A.
PY - 2023
SP - 964
EP - 971
DO - 10.5220/0011846100003393