Authors:
Karam Abdullah
1
;
2
;
Imen Jegham
3
;
Mohamed Mahjoub
3
and
Anouar Ben Khalifa
3
;
4
Affiliations:
1
University of Mosul, Collage of Education for Pure Science, Computer Science Department, Mosul, Iraq
;
2
Université De Sousse, ISITCOM, LATIS-Laboratory of Advanced Technology and Intelligent Systems, 4011, Sousse, Tunisia
;
3
Université De Sousse, Ecole Nationale d’Ingénieurs De Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, 4023, Sousse, Tunisia
;
4
Université De Jendouba, Institut National Des Technologies et Des Sciences Du Kef, 7100, Le Kef, Tunisia
Keyword(s):
Driver Monitoring, Nighttime, Spatio-multi-temporal Attention, Hard Attention, Deep Learning, Hybrid Network.
Abstract:
Driver distraction and inattention is recently reported to be the major factor in traffic crashes even with the appearance of various advanced driver assistance systems. In fact, driver monitoring is a challenging vision-based task due to the high number of issues present including the dynamic and cluttered background and high in-vehicle actions similarities. This task becomes more and more complex at nighttime because of the low illumination. In this paper, to efficiently recognize driver actions at nighttime, we unprecedentedly propose a hard spatio-multi-temporal attention network that exclusively focuses on dynamic spatial information of the driving scene and more specifically driver motion, then using a batch split unit only relevant temporal information is considered in the classification. Experiments prove that our proposed approach achieves high recognition accuracy compared to state-of-the art-methods on the unique realistic available dataset 3MDAD.