Chu Jiangwei, Jin Lisheng, Guo Lie, Libibing, Wang Rongben
{"title":"Study on method of detecting preceding vehicle based on monocular camera","authors":"Chu Jiangwei, Jin Lisheng, Guo Lie, Libibing, Wang Rongben","doi":"10.1109/IVS.2004.1336478","DOIUrl":null,"url":null,"abstract":"This article describes systemically the method of detecting the preceding vehicle based on a monocular camera. The main content is as follows: first, a primary area of interest is found by the lane borderlines that are identified in a camera image, and a likelihood target vehicle is searched by the gray difference between the target vehicle and the background; second, an identifying area of interest is found again based on the area of a likelihood target vehicle, a target vehicle is affirmed by a symmetry character of the vehicle outline and a position of the vehicle symmetrical axis is ascertained; third, the object vehicle is tracked by Kalman forecast principle in the sequence images; fourth, a method of detecting distance in a frame of image is introduced. The calibration of the camera's interior parameters and the results of some experiments are given.","PeriodicalId":296386,"journal":{"name":"IEEE Intelligent Vehicles Symposium, 2004","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"43","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Intelligent Vehicles Symposium, 2004","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2004.1336478","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 43
Abstract
This article describes systemically the method of detecting the preceding vehicle based on a monocular camera. The main content is as follows: first, a primary area of interest is found by the lane borderlines that are identified in a camera image, and a likelihood target vehicle is searched by the gray difference between the target vehicle and the background; second, an identifying area of interest is found again based on the area of a likelihood target vehicle, a target vehicle is affirmed by a symmetry character of the vehicle outline and a position of the vehicle symmetrical axis is ascertained; third, the object vehicle is tracked by Kalman forecast principle in the sequence images; fourth, a method of detecting distance in a frame of image is introduced. The calibration of the camera's interior parameters and the results of some experiments are given.