论文标题
驾驶员监控系统的强大安全带检测和使用识别
Robust Seatbelt Detection and Usage Recognition for Driver Monitoring Systems
论文作者
论文摘要
驾驶时适当戴安全带可以使严重的坠机相关伤害或死亡减少大约一半。但是，当前的安全带提醒系统有多个缺点，例如，“安全带警告塞子”很容易被愚弄，并且无法识别出不正确的用法，例如坐在弯曲的安全带前或手臂下的安全带。一般安全带的使用识别有许多挑战，仅举几个挑战，缺乏红外（IR）摄像机中的颜色信息，这是由宽阔的视野（FOV）鱼眼镜头引起的强烈失真，皮带及其背景之间的低对比度，手或头发引起的遮挡，以及成像模糊。在本文中，我们介绍了一种新颖的一般安全带检测和使用识别框架，以解决上述挑战。我们的方法由三个组成部分组成：局部预测指标，一个全局汇编程序和形状建模过程。我们的方法可以应用于驾驶员监控系统（DMS）中的驱动程序或乘员监视系统（OMS）中的各种相机方式的驾驶员。提供了DMS和OMS的实验结果，以证明所提出方法的准确性和鲁棒性。
Wearing a seatbelt appropriately while driving can reduce serious crash-related injuries or deaths by about half. However, current seatbelt reminder system has multiple shortcomings, such as can be easily fooled by a "Seatbelt Warning Stopper", and cannot recognize incorrect usages for example seating in front of a buckled seatbelt or wearing a seatbelt under the arm. General seatbelt usage recognition has many challenges, to name a few, lacking of color information in Infrared (IR) cameras, strong distortion caused by wide Field of View (FoV) fisheye lens, low contrast between belt and its background, occlusions caused by hands or hair, and imaging blurry. In this paper, we introduce a novel general seatbelt detection and usage recognition framework to resolve the above challenges. Our method consists of three components: a local predictor, a global assembler, and a shape modeling process. Our approach can be applied to the driver in the Driver Monitoring System (DMS) or general passengers in the Occupant Monitoring System (OMS) for various camera modalities. Experiment results on both DMS and OMS are provided to demonstrate the accuracy and robustness of the proposed approach.
