A novel public dataset for multimodal ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
A novel public dataset for multimodal multiview and multispectral driver distraction analysis: 3MDAD
Auteur(s) :
Jegham, Imen [Auteur]
Ben Khalifa, Anouar [Auteur correspondant]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Alouani, Lihsen [Auteur]
INSA Institut National des Sciences Appliquées Hauts-de-France [INSA Hauts-De-France]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Mahjoub, Mohamed Ali [Auteur]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Ben Khalifa, Anouar [Auteur correspondant]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Alouani, Lihsen [Auteur]
INSA Institut National des Sciences Appliquées Hauts-de-France [INSA Hauts-De-France]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Mahjoub, Mohamed Ali [Auteur]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Titre de la revue :
Signal Processing: Image Communication
Pagination :
115960, 13 pages
Éditeur :
Elsevier
Date de publication :
2020-10
ISSN :
0923-5965
Mot(s)-clé(s) en anglais :
Safe driving
Intelligent transportation system
Driver distraction
Multiview
Multimodal
Multispectral
Public dataset
Intelligent transportation system
Driver distraction
Multiview
Multimodal
Multispectral
Public dataset
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
Résumé en anglais : [en]
Driver distraction and fatigue have become one of the leading causes of severe traffic accidents. Hence, driver inattention monitoring systems are crucial. Even with the growing development of advanced driver assistance ...
Lire la suite >Driver distraction and fatigue have become one of the leading causes of severe traffic accidents. Hence, driver inattention monitoring systems are crucial. Even with the growing development of advanced driver assistance systems and the introduction of third-level autonomous vehicles, this task is still trending and complex due to challenges such as the illumination change and the dynamic background. To reliably compare and validate driver inattention monitoring methods, a limited number of public datasets are available. In this paper, we put forward a public, well-structured and complete dataset, named Multiview, Multimodal and Multispectral Driver Action Dataset (3MDAD). The dataset is mainly composed of two sets: the first one recorded in daytime and the second one at nighttime. Each set consists of two synchronized data modalities, both from frontal and side views. More than 60 drivers are asked to execute 16 in-vehicle actions under a wide range of naturalistic driving settings. In contrast to other public datasets, 3MDAD presents multiple modalities, spectrums and views under different time and weather conditions. To highlight the utility of our dataset, we independently analyze the driver action recognition results adapted to each modality and those obtained of several combinations of modalities.Lire moins >
Lire la suite >Driver distraction and fatigue have become one of the leading causes of severe traffic accidents. Hence, driver inattention monitoring systems are crucial. Even with the growing development of advanced driver assistance systems and the introduction of third-level autonomous vehicles, this task is still trending and complex due to challenges such as the illumination change and the dynamic background. To reliably compare and validate driver inattention monitoring methods, a limited number of public datasets are available. In this paper, we put forward a public, well-structured and complete dataset, named Multiview, Multimodal and Multispectral Driver Action Dataset (3MDAD). The dataset is mainly composed of two sets: the first one recorded in daytime and the second one at nighttime. Each set consists of two synchronized data modalities, both from frontal and side views. More than 60 drivers are asked to execute 16 in-vehicle actions under a wide range of naturalistic driving settings. In contrast to other public datasets, 3MDAD presents multiple modalities, spectrums and views under different time and weather conditions. To highlight the utility of our dataset, we independently analyze the driver action recognition results adapted to each modality and those obtained of several combinations of modalities.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Source :