  Vision is the richest and most cost-effective technology for Driver
Monitoring Systems (DMS), especially after the recent success of Deep Learning
(DL) methods. The lack of sufficiently large and comprehensive datasets is
currently a bottleneck for the progress of DMS development, crucial for the
transition of automated driving from SAE Level-2 to SAE Level-3. In this paper,
we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which
includes real and simulated driving scenarios: distraction, gaze allocation,
drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth
and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A
comparison with existing similar datasets is included, which shows the DMD is
more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated
by extracting a subset of it, the dBehaviourMD dataset, containing 13
distraction activities, prepared to be used in DL training processes.
Furthermore, we propose a robust and real-time driver behaviour recognition
system targeting a real-world application that can run on cost-efficient
CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated
with different types of fusion strategies, which all reach enhanced accuracy
still providing real-time response.
