(Safe) SMART Hands: Hand Activity Analysis and Distraction Alerts Using a Multi-Camera Framework
This addresses driver safety by reducing crash risk from hand-related distractions, but it is incremental as it builds on existing multi-camera and machine learning approaches.
The paper tackled the problem of analyzing hand activity to assess driver readiness by introducing the SMART Hands framework, which achieved 98% classification accuracy for both hands in a 4-camera setup.
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.