车道标线与道路边界识别的集成方法

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008) Pub Date : 2008-01-23 DOI:10.1109/WKDD.2008.119

Weina Lu, Yucai Zheng, Yuquan Ma, Tao Liu

{"title":"车道标线与道路边界识别的集成方法","authors":"Weina Lu, Yucai Zheng, Yuquan Ma, Tao Liu","doi":"10.1109/WKDD.2008.119","DOIUrl":null,"url":null,"abstract":"An integrated vision method was proposed for intelligent vehicles to synchronously recognize the lane marking and road boundary in direct or curve road environment. Firstly, with region connectivity analyzing, the method extracted the brightness features of lane markings on input images by self-adaptive threshold segmenting. Not only the gradient magnitude but also the gradient direction features of the road boundary were extracted by the Sobel operator method. Secondly, the 2-D models of road shape were acquired and the features above were matched to them by least-squares fit. With the circular calling of detecting and tracking program block, the whole process showed a fast and exact capability. The experiments have been conducted with the videos captured from real road scenes, and the results proved that it is a real time and robust method to recognize the road for the vision-based navigation of intelligent vehicles.","PeriodicalId":101656,"journal":{"name":"First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"An Integrated Approach to Recognition of Lane Marking and Road Boundary\",\"authors\":\"Weina Lu, Yucai Zheng, Yuquan Ma, Tao Liu\",\"doi\":\"10.1109/WKDD.2008.119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An integrated vision method was proposed for intelligent vehicles to synchronously recognize the lane marking and road boundary in direct or curve road environment. Firstly, with region connectivity analyzing, the method extracted the brightness features of lane markings on input images by self-adaptive threshold segmenting. Not only the gradient magnitude but also the gradient direction features of the road boundary were extracted by the Sobel operator method. Secondly, the 2-D models of road shape were acquired and the features above were matched to them by least-squares fit. With the circular calling of detecting and tracking program block, the whole process showed a fast and exact capability. The experiments have been conducted with the videos captured from real road scenes, and the results proved that it is a real time and robust method to recognize the road for the vision-based navigation of intelligent vehicles.\",\"PeriodicalId\":101656,\"journal\":{\"name\":\"First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WKDD.2008.119\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WKDD.2008.119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 10

摘要

提出了一种智能车辆在直弯道路环境下同步识别车道标线和道路边界的集成视觉方法。该方法首先通过区域连通性分析，通过自适应阈值分割提取输入图像上车道标记的亮度特征;采用Sobel算子提取道路边界的梯度大小和梯度方向特征。其次，获取道路形状的二维模型，并通过最小二乘拟合对上述特征进行匹配;通过循环调用检测和跟踪程序块，整个过程显示出快速准确的能力。实验结果表明，该方法是一种实时、鲁棒的道路识别方法，适用于基于视觉的智能车辆导航。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

An Integrated Approach to Recognition of Lane Marking and Road Boundary

An integrated vision method was proposed for intelligent vehicles to synchronously recognize the lane marking and road boundary in direct or curve road environment. Firstly, with region connectivity analyzing, the method extracted the brightness features of lane markings on input images by self-adaptive threshold segmenting. Not only the gradient magnitude but also the gradient direction features of the road boundary were extracted by the Sobel operator method. Secondly, the 2-D models of road shape were acquired and the features above were matched to them by least-squares fit. With the circular calling of detecting and tracking program block, the whole process showed a fast and exact capability. The experiments have been conducted with the videos captured from real road scenes, and the results proved that it is a real time and robust method to recognize the road for the vision-based navigation of intelligent vehicles.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)

自引率

0.00%

发文量