  Driver inattention assessment has become a very active field in intelligent
transportation systems. Based on active sensor Kinect and computer vision
tools, we have built an efficient module for detecting driver distraction and
recognizing the type of distraction. Based on color and depth map data from the
Kinect, our system is composed of four sub-modules. We call them eye behavior
(detecting gaze and blinking), arm position (is the right arm up, down, right
of forward), head orientation, and facial expressions. Each module produces
relevant information for assessing driver inattention. They are merged together
later on using two different classification strategies: AdaBoost classifier and
Hidden Markov Model. Evaluation is done using a driving simulator and 8 drivers
of different gender, age and nationality for a total of more than 8 hours of
recording. Qualitative and quantitative results show strong and accurate
detection and recognition capacity (85% accuracy for the type of distraction
and 90% for distraction detection). Moreover, each module is obtained
independently and could be used for other types of inference, such as fatigue
detection, and could be implemented for real cars systems.
