Stixel Based Scene Understanding for Autonomous Vehicles

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dc.contributor.authorWieszok, Z
dc.contributor.authorAouf, Nabil
dc.contributor.authorKechagias-Stamatis, Odysseas
dc.contributor.authorChermak, Lounis
dc.date.accessioned2017-08-07T11:08:48Z
dc.date.available2017-08-07T11:08:48Z
dc.date.issued2017-08-03
dc.description.abstractWe propose a stereo vision based obstacle detection and scene segmentation algorithm appropriate for autonomous vehicles. Our algorithm is based on an innovative extension of the Stixel world, which neglects computing a disparity map. Ground plane and stixel distance estimation is improved by exploiting an online learned color model. Furthermore, the stixel height estimation is leveraged by an innovative joined membership scheme based on color and disparity information. Stixels are then used as an input for the semantic scene segmentation providing scene understanding, which can be further used as a comprehensive middle level representation for high-level object detectors.en_UK
dc.identifier.citationZ. Wieszok, N. Aouf, O. Kechagias-Stamatis and L. Chermak, (2017) Stixel based scene understanding for autonomous vehicles, IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Calabria, Italy, 2017, pp. 43-48en_UK
dc.identifier.urihttps://doi.org/10.1109/ICNSC.2017.8000065
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/12279
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectStereo visionen_UK
dc.subjectSensorsen_UK
dc.titleStixel Based Scene Understanding for Autonomous Vehiclesen_UK
dc.typeConference paperen_UK

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