Comprehensive Performance Analysis Of Road Detection Algorithms Using The Common Urban Kitti-road Benchmark

dc.creatorVitor G.B.
dc.creatorVictorino A.C.
dc.creatorFerreira J.V.
dc.date2014
dc.date2015-06-25T18:00:34Z
dc.date2015-11-26T14:56:21Z
dc.date2015-06-25T18:00:34Z
dc.date2015-11-26T14:56:21Z
dc.date.accessioned2018-03-28T22:08:23Z
dc.date.available2018-03-28T22:08:23Z
dc.identifier9781479936380
dc.identifierIeee Intelligent Vehicles Symposium, Proceedings. Institute Of Electrical And Electronics Engineers Inc., v. , n. , p. 19 - 24, 2014.
dc.identifier
dc.identifier10.1109/IVS.2014.6856616
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84905383045&partnerID=40&md5=8efebe5cb04bca7d1a09cee1e88467d1
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/87417
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87417
dc.identifier2-s2.0-84905383045
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1255455
dc.descriptionThe navigation of an autonomous vehicle is a highly complex task and the dynamic environment is used as a source for reasoning. Road detection is a major issue in autonomous systems and advanced driving assistance systems applied for inner-city. Uncertainty may arise in environments with unmarked or weakly marked roads or poor lightning conditions. Moreover, when a common benchmark is not used, it is hard to decide which approach performs better on the road detection problem. This paper introduces a comprehensive performance analysis of two road recognition approaches using the urban Kitti-road benchmark. The first approach makes the extraction of a feature set based on statistical measures of 2D and 3D information from each superpixel. An Artificial Neural Network is used to detect the road pattern. The second approach extracts the feature set based on a multi-normalized histogram of Textons and Disptons for each superpixel. This feature set is used as a source for a Joint Boosting algorithm to model the road pattern. The proposed work presents a detailed evaluation highliting the pros and cons of each approach. © 2014 IEEE.
dc.description
dc.description
dc.description19
dc.description24
dc.descriptionIEEE Intelligent Transportation Systems Society (ITSS)
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dc.languageen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationIEEE Intelligent Vehicles Symposium, Proceedings
dc.rightsfechado
dc.sourceScopus
dc.titleComprehensive Performance Analysis Of Road Detection Algorithms Using The Common Urban Kitti-road Benchmark
dc.typeActas de congresos


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