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dc.contributor.authorCáceres Hernández, Danilo
dc.contributor.authorSeo, Dongwook
dc.contributor.authorHyun Jo, Kang
dc.date.accessioned2018-06-29T22:03:13Z
dc.date.available2018-06-29T22:03:13Z
dc.date.issued2016-07-06
dc.identifierhttps://ieeexplore.ieee.org/abstract/document/7529668/
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/5095
dc.descriptionIn the field of intelligent vehicle systems (IVS), color and edge of lane markings are important features for vision-based applications. This paper proposes a method to detect lane marking based on a fusion approach which combine color and edge lane marking information. Firstly, by knowing the vehicle speed the road surface region of interest is extracted using the typical stopping distance. Secondly, a lane marking clustering method is introduced. This is done by combining the edge and color information of the lane marking. Finally, a fitting model is implemented. A line fitting model is used to extract the lane marking parameters. However for those regions in which lane can not described as a line, the algorithm computed the curve parameters using Lagrange interpolating polynomial.en_US
dc.description.abstractIn the field of intelligent vehicle systems (IVS), color and edge of lane markings are important features for vision-based applications. This paper proposes a method to detect lane marking based on a fusion approach which combine color and edge lane marking information. Firstly, by knowing the vehicle speed the road surface region of interest is extracted using the typical stopping distance. Secondly, a lane marking clustering method is introduced. This is done by combining the edge and color information of the lane marking. Finally, a fitting model is implemented. A line fitting model is used to extract the lane marking parameters. However for those regions in which lane can not described as a line, the algorithm computed the curve parameters using Lagrange interpolating polynomial.en_US
dc.language.isoenen_US
dc.subjectImage color analysisen_US
dc.subjectFeature extractionen_US
dc.subjectRoadsen_US
dc.subjectImage edge detectionen_US
dc.subjectCamerasen_US
dc.subjectVehiclesen_US
dc.subjectImage segmentationen_US
dc.titleRobust lane marking detection based on multi-feature fusionen_US
dc.typeArticleen_US


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