Hard Spatio-Multi Temporal Attention Framework for Driver Monitoring at Nighttime

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.