Hundreds of fatal accidents occur each year due to wrong-way driving (WWD). Although several methods have been developed to detect WWD using existing closed-circuit television (CCTV) data, they all require manual recalibration whenever a camera rotates, and are thus not scalable across statewide CCTV networks. This paper, therefore, proposes an end-to-end deep-learning-based model that considers camera orientation as a variable, detecting camera rotation automatically and learning new decision criteria accordingly using a neural network model. We show that our proposed solution can detect WWD with a precision of 0.99 and a recall of 0.97. Due to its cheap computational cost and high error tolerance, our solution is easily scalable for statewide surveillance on a real-time basis to help decision-makers reduce fatalities due to WWD.

中文翻译：

---

基于计算机视觉的深度学习模型，使用平移-倾斜-变焦交通摄像头检测错误方向的驾驶

由于错误方向驾驶 (WWD)，每年都会发生数百起致命事故。尽管已经开发出多种方法来使用现有的闭路电视 (CCTV) 数据来检测 WWD，但它们都需要在摄像机旋转时进行手动重新校准，因此无法在全州范围内的闭路电视网络中扩展。因此，本文提出了一种端到端的基于深度学习的模型，该模型将相机方向视为一个变量，自动检测相机旋转并使用神经网络模型相应地学习新的决策标准。我们表明，我们提出的解决方案可以以 0.99 的精度和 0.97 的召回率检测 WWD。由于其低廉的计算成本和高容错性，我们的解决方案可以轻松扩展到全州范围内的实时监控，以帮助决策者减少 WWD 造成的死亡人数。