Title :

Deep learning-based hard spatial attention for driver in-vehicle action monitoring

Author(s) :

Jegham, Imen [Auteur]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Alouani, Lihsen [Auteur]
Queen's University [Belfast] [QUB]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Ben Khalifa, Anouar [Auteur]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Mahjoub, Mohamed Ali [Auteur]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]

Journal title :

Expert systems with applications

Pages :

119629

Publisher :

Elsevier

Publication date :

2023-06

ISSN :

0957-4174

English keyword(s) :

Driver
Action
Recognition
In-vehicle action monitoring
Hard attention
Deep learning
Hybrid network

HAL domain(s) :

Physique [physics]
Sciences de l'ingénieur [physics]

English abstract : [en]

Distracted driving is one of the main causes of deaths and injuries in the world. Monitoring driver behaviors through Driver Action Recognition (DAR) contributes significantly to building safer transportation systems. ...
Show more >Distracted driving is one of the main causes of deaths and injuries in the world. Monitoring driver behaviors through Driver Action Recognition (DAR) contributes significantly to building safer transportation systems. However, in naturalistic driving settings, this task is complex and challenging because of numerous difficulties, such as high illumination variation and cluttered and dynamic background. In this paper, we introduce a novel hard attention network that highlights the most pertinent driving-scene information while filtering out irrelevant data. Specifically, only local discriminative salient regions are exploited through a hard attention mechanism. The experimental results indicate that our approach significantly enhances DAR performance. We evaluated our network on three diverse state-of-the-art datasets recorded in real-world conditions: it achieves up to 95.83% in terms of safe driving recognition and up to 99.07% in terms of distraction detection. The proposed approach outperforms the soft attention-based DAR not only in detection and recognition performance but also in computation complexity by 38.71% less runtime. For reproducible research, the code is available at https://github.com/JEGHAMI/HSAShow less >

Language :

Anglais

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL