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Description
  • Visual systems for automatic monitoring of driver vigilance usually have to address two main problems. First of all, they have to acquire and process image sequence so that fatigue features can be simply extracted. Secondly, visual systems have to analyse a set of acquired features and subsequently recognize dangerous behaviour such as driver inattention or sleepiness. This paper is focused particularly on segmentation methods used for reliable eyes tracking, because of eyes features are probably most significant features for determining of a driver fatigue. Fundamentals segmentation methods as simple colour segmentation or Hough transform as well as more complex methods as Haar-like features or symmetries detection are introduced in the paper. Several of the most frequently used fatigue features are listed and described at the end of the paper. All the presented methods were tested and verified on both laboratory and real sets of images.
  • Visual systems for automatic monitoring of driver vigilance usually have to address two main problems. First of all, they have to acquire and process image sequence so that fatigue features can be simply extracted. Secondly, visual systems have to analyse a set of acquired features and subsequently recognize dangerous behaviour such as driver inattention or sleepiness. This paper is focused particularly on segmentation methods used for reliable eyes tracking, because of eyes features are probably most significant features for determining of a driver fatigue. Fundamentals segmentation methods as simple colour segmentation or Hough transform as well as more complex methods as Haar-like features or symmetries detection are introduced in the paper. Several of the most frequently used fatigue features are listed and described at the end of the paper. All the presented methods were tested and verified on both laboratory and real sets of images. (en)
Title
  • On Image Segmentation Techniques for Driver Inattention Systems
  • On Image Segmentation Techniques for Driver Inattention Systems (en)
skos:prefLabel
  • On Image Segmentation Techniques for Driver Inattention Systems
  • On Image Segmentation Techniques for Driver Inattention Systems (en)
skos:notation
  • RIV/00216305:26220/11:PU94504!RIV12-MSM-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/09/1897), Z(MSM0021630529)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 218049
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/11:PU94504
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • driver fatigue, eyes detection, inattention, image processing, segmentation, tracking (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [EA31CF8DB79D]
http://linked.open...v/mistoKonaniAkce
  • Brno University of Technology
http://linked.open...i/riv/mistoVydani
  • Brno, Czech Republic
http://linked.open...i/riv/nazevZdroje
  • The Proceedings of the 17th International Conference on Soft Computing
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Horák, Karel
  • Kučera, Pavel
  • Honzík, Petr
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Institute of Automation and Computer Science
https://schema.org/isbn
  • 978-80-214-4120-0
http://localhost/t...ganizacniJednotka
  • 26220
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