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.

翻译：人工(与手有关)活动是驾驶时发生碰撞风险的一个重要来源。 因此,分析手势和手活动是了解驾驶员是否准备好控制车辆的一个有用组成部分。 通过照相机进行的视觉遥感提供了观察手部的被动手段,但其效力因摄像头的位置而异。我们引入了一个算法框架,即SMART手(SMART Hands),用于精确的手分类,并使用机器学习来提供一组摄像视图。我们用一个4个摄像头来说明这一框架的有效性,在各种地点达到98%的分类精确度,并为驾驶员两只手持有物件。我们的结论是,这个多照相机框架可以扩大到额外的任务,如观看和分析,并在驾驶员和乘客安全方面进一步应用。