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A Vision-Based Approach for Rail Extraction and its Application in a Camera Pan–Tilt Control System

机译:基于视觉的铁轨提取方法及其在摄像机俯仰控制系统中的应用

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摘要

Rail extraction, i.e., determining the position of the rails ahead of a train, is one of the basic tasks of vision-based driver support in railways. This paper introduces an approach that extracts rails by matching edge features to candidate rail patterns modeled as sequences of parabola segments. Patterns are precomputed in a semiautomatic offline stage for areas near the camera and generated on the fly for more distant regions. Our approach was designed to address the challenges posed by the open environment without requiring explicit knowledge about train speed or camera parameters/position and running fast enough for practical use without specialized hardware. Evaluation was performed on hours of video captured under real operation conditions, considering the requirements of a system in which a camera with zoom lens mounted on a pan–tilt unit captures images from the area ahead with increased resolution.
机译:铁路提取,即确定火车前方铁路的位置,是铁路中基于视觉的驾驶员支持的基本任务之一。本文介绍了一种通过将边缘特征与建模为抛物线段序列的候选轨道图案匹配来提取轨道的方法。模式会在半自动离线阶段针对相机附近的区域进行预先计算,并针对较远的区域即时生成。我们的方法旨在解决开放环境带来的挑战,而无需明确了解火车速度或摄像机参数/位置,并且无需特殊硬件即可运行得足够快以适合实际使用。考虑到系统的要求,评估系统是对在实际操作条件下捕获的视频小时进行的评估,在该系统中,安装在云台上的变焦镜头的摄像机可以以更高的分辨率捕获前方区域的图像。

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