Due to the limited sensing ability with the single-view camera and the real-time requirement for multi-view scenarios or deep learning-based methods in complex scenes, the output of lane detection is not applicable for the actual lane departure warning system. To tackle this challenge, the authors propose a fast and robust approach for lane detection based on well-designed multi-camera fusion, integrating vanishing point estimation, and specified feature fitting strategies. To meet real-time demand, several simple but effective image processing means are introduced and improved. Concretely, on account of statistical information, the authors’ method carries out an improved region of interest selection to speed up the detection. Afterwards, they used the B-spline fitting lane line on the strength of the RANdom SAmple consensus algorithm for the front view image detection and improved the Hough algorithm for the two rear-view images correspondingly. Using coordinate conversion and self-designed fusion strategy, they get the robust lane information based on symmetrical lane detection from the left/right sides of both front and side views. Experimental results in newly introduced multi-camera scenarios show that their multi-camera fusion framework contributes to significant improvement in accuracy and robustness in comparison with traditional methods.

中文翻译：

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在复杂场景中使用多摄像机融合的车道检测快速而强大的方法

由于单视点相机的感应能力有限，并且在复杂场景中对多视点场景或基于深度学习的方法具有实时性要求，因此车道检测的输出不适用于实际的车道偏离警告系统。为了解决这一挑战，作者提出了一种快速而强大的车道检测方法，该方法基于精心设计的多摄像机融合，整合消失点估计和指定的特征拟合策略。为了满足实时需求，引入并改进了几种简单但有效的图像处理手段。具体地，由于统计信息，作者的方法进行了改进的感兴趣区域选择，以加快检测速度。之后，他们在RANdom SAmple共识算法的强度上使用B样条拟合车道线进行前视图像检测，并相应地改进了两个后视图像的Hough算法。他们使用坐标转换和自行设计的融合策略，从前视图和侧视图的左侧/右侧基于对称车道检测获得了可靠的车道信息。在新引入的多摄像机场景中的实验结果表明，与传统方法相比，它们的多摄像机融合框架极大地提高了准确性和鲁棒性。他们从前视图和侧视图的左侧/右侧基于对称车道检测获得了可靠的车道信息。在新引入的多摄像机场景中的实验结果表明，与传统方法相比，它们的多摄像机融合框架有助于显着提高准确性和鲁棒性。他们从前视图和侧视图的左侧/右侧基于对称车道检测获得鲁棒的车道信息。在新引入的多摄像机场景中的实验结果表明，与传统方法相比，它们的多摄像机融合框架有助于显着提高准确性和鲁棒性。