Publication detail

On Image Segmentation Techniques for Driver Inattention Systems

HORÁK, K. HONZÍK, P. KUČERA, P.

Original Title

On Image Segmentation Techniques for Driver Inattention Systems

English Title

On Image Segmentation Techniques for Driver Inattention Systems

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

driver fatigue, eyes detection, inattention, image processing, segmentation, tracking

RIV year

2011

Released

17.06.2011

Publisher

Institute of Automation and Computer Science

Location

Brno, Czech Republic

ISBN

978-80-214-4120-0

Book

The Proceedings of the 17th International Conference on Soft Computing

Pages from

1

Pages to

6

Pages count

5

Documents

BibTex


@inproceedings{BUT74375,
  author="Karel {Horák} and Petr {Honzík} and Pavel {Kučera}",
  title="On Image Segmentation Techniques for Driver Inattention Systems",
  annote="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.",
  address="Institute of Automation and Computer Science",
  booktitle="The Proceedings of the 17th International Conference on Soft Computing",
  chapter="74375",
  howpublished="print",
  institution="Institute of Automation and Computer Science",
  year="2011",
  month="june",
  pages="1--6",
  publisher="Institute of Automation and Computer Science",
  type="conference paper"
}