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：手动（与手相关）活动是驾驶过程中导致碰撞风险的重要因素。因此，对手部位置及手部活动占用的分析，是理解驾驶员接管车辆控制权准备状态的有效组成部分。通过摄像头进行视觉感知是一种被动观察手部的方法，但其有效性因摄像头位置而异。我们提出了一种名为SMART Hands的算法框架，该框架利用机器学习集成多摄像头视角实现对手部的精准分类。我们通过四摄像头设置验证了该框架的有效性，在驾驶员双手的不同位置和手持物体场景下达到了98%的分类准确率。我们得出结论：该多摄像头框架可扩展至视线追踪和姿态分析等附加任务，并在驾驶员与乘客安全领域具有更广泛的应用前景。