Vision based lane detection and departure warning system

Priya V. Date, V. Gaikwad
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引用次数: 6

Abstract

The increasing number of road accidents is a serious issue in front of modern society. Driver inattention, fatigue and drowsiness are the major causes of road accidents. Numerous methods have been proposed for lane detection and tracking. The main objective of this paper is comparison of three widely used lane detection techniques depending upon their similarities and differences, advantages and disadvantages. Hough transform, Road model and Fuzzy logic based techniques. Hough transform is most effective technique for detection of straight line with the advantage of having reduced logic area and memory utilization and high reliability. Model based techniques uses mathematical model for lane boundary fitting and requires very few parameters for lane representation because of this these systems are more robust against occlusion and missing data than feature based techniques. Fuzzy logic is very useful for drawing precise conclusions from imprecise input data and for solving problems which are difficult or impossible to model using exact mathematical model but can be easily solved by experience of human operator.
基于视觉的车道检测与偏离预警系统
日益增多的交通事故是摆在现代社会面前的一个严重问题。司机注意力不集中、疲劳和困倦是造成交通事故的主要原因。人们提出了许多车道检测和跟踪的方法。本文的主要目的是比较三种广泛使用的车道检测技术,根据它们的异同,优缺点。基于霍夫变换、路模型和模糊逻辑的技术。霍夫变换是最有效的直线检测技术,具有逻辑面积小、存储空间小、可靠性高等优点。基于模型的技术使用数学模型进行车道边界拟合,并且需要很少的参数来表示车道,因此这些系统比基于特征的技术对遮挡和缺失数据更健壮。模糊逻辑对于从不精确的输入数据中得出精确的结论,以及解决用精确的数学模型难以或不可能建模但可以通过操作员的经验轻松解决的问题是非常有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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