Gaining environmental awareness through lateral head scanning (yaw rotations) is important for driving safety, especially when approaching intersections. Therefore, head scanning movements could be an important behavioral metric for driving safety research and driving risk mitigation systems. Tracking head scanning movements with a single in-car camera is preferred hardware-wise, but it is very challenging to track the head over almost a [Formula: see text] range. In this paper, we investigate two state-of-the-art methods, a multi-loss deep residual learning method with 50 layers (multi-loss ResNet-50) and an ORB feature-based simultaneous localization and mapping method (ORB-SLAM). While deep learning methods have been extensively studied for head pose detection, this is the first study in which SLAM has been employed to innovatively track head scanning over a very wide range. Our laboratory experimental results showed that ORB-SLAM was more accurate than multi-loss ResNet-50, which often failed when many facial features were not in the view. On the contrary, ORB-SLAM was able to continue tracking as it does not rely on particular facial features. Testing with real driving videos demonstrated the feasibility of using ORB-SLAM for tracking large lateral head scans in naturalistic video data.

中文翻译：

---

迈向驾驶中头部扫描运动的大范围跟踪

通过横向头部扫描（偏航旋转）获得环境意识对于驾驶安全很重要，尤其是在接近十字路口时。因此，头部扫描动作可能是驾驶安全研究和驾驶风险缓解系统的重要行为指标。使用单个车载摄像头跟踪头部扫描运动在硬件方面是首选，但在几乎 [公式：见文本] 范围内跟踪头部是非常具有挑战性的。在本文中，我们研究了两种最先进的方法，一种具有 50 层的多损失深度残差学习方法（multi-loss ResNet-50）和一种基于 ORB 特征的同时定位和映射方法（ORB-SLAM ）。虽然深度学习方法已被广泛研究用于头部姿势检测，这是第一项使用 SLAM 来创新性地在非常广泛的范围内跟踪磁头扫描的研究。我们的实验室实验结果表明，ORB-SLAM 比 multi-loss ResNet-50 更准确，后者在许多面部特征不在视图中时经常失败。相反，ORB-SLAM 能够继续跟踪，因为它不依赖于特定的面部特征。使用真实驾驶视频进行的测试证明了使用 ORB-SLAM 跟踪自然视频数据中的大型横向头部扫描的可行性。