  Manual (hand-related) activity is a significant source of crash risk while
driving. Accordingly, analysis of hand position and hand activity occupation is
a useful component to understanding a driver's readiness to take control of a
vehicle. Visual sensing through cameras provides a passive means of observing
the hands, but its effectiveness varies depending on camera location. We
introduce an algorithmic framework, SMART Hands, for accurate hand
classification with an ensemble of camera views using machine learning. We
illustrate the effectiveness of this framework in a 4-camera setup, reaching
98% classification accuracy on a variety of locations and held objects for both
of the driver's hands. We conclude that this multi-camera framework can be
extended to additional tasks such as gaze and pose analysis, with further
applications in driver and passenger safety.
