Detail publikace

Effectiveness of Approaches for Rail Candidates Detection and Verification of the SVM

Originální název

Effectiveness of Approaches for Rail Candidates Detection and Verification of the SVM

Anglický název

Effectiveness of Approaches for Rail Candidates Detection and Verification of the SVM

Jazyk

en

Originální abstrakt

Rail candidates detection is the primary task in railway recognition systems based on recognition in images taken from the camera mounted on the board of the locomotive. In order to reduce the classifier complexity, effective and responsible rail candidates generation plays an important role without placing big decision responsibility on a  further classifier stage. There are two basic options. Due to the rich complex environment along the track, pixel-per-pixel methods are often omitted. The second option involving a thorough investigation around a  pixel is preferred. In this paper, we present comparison between two different approaches to rail candidates detection, each representing one of the basic groups, furthermore consequences in rail hypotheses generation. We introduce the finding that using the SVM is more efficient than the method based on pixel-per-pixel.

Anglický abstrakt

Rail candidates detection is the primary task in railway recognition systems based on recognition in images taken from the camera mounted on the board of the locomotive. In order to reduce the classifier complexity, effective and responsible rail candidates generation plays an important role without placing big decision responsibility on a  further classifier stage. There are two basic options. Due to the rich complex environment along the track, pixel-per-pixel methods are often omitted. The second option involving a thorough investigation around a  pixel is preferred. In this paper, we present comparison between two different approaches to rail candidates detection, each representing one of the basic groups, furthermore consequences in rail hypotheses generation. We introduce the finding that using the SVM is more efficient than the method based on pixel-per-pixel.

BibTex


@inproceedings{BUT130980,
  author="Marek {Musil}",
  title="Effectiveness of Approaches for Rail Candidates Detection and Verification of the SVM",
  annote="Rail candidates detection is the primary task in railway recognition systems
based on recognition in images taken from the camera mounted on the board of the
locomotive. In order to reduce the classifier complexity, effective and
responsible rail candidates generation plays an important role without placing
big decision responsibility on a  further classifier stage. There are two basic
options. Due to the rich complex environment along the track, pixel-per-pixel
methods are often omitted. The second option involving a thorough investigation
around a  pixel is preferred. In this paper, we present comparison between two
different approaches to rail candidates detection, each representing one of the
basic groups, furthermore consequences in rail hypotheses generation. We
introduce the finding that using the SVM is more efficient than the method based
on pixel-per-pixel.",
  address="University of Žilina",
  booktitle="ICTIC - Proceedings in Conference of Informatics and Management Sciences",
  chapter="130980",
  doi="10.18638/ictic.2016.5.1",
  edition="Volume 5 Issue 1",
  howpublished="electronic, physical medium",
  institution="University of Žilina",
  year="2016",
  month="april",
  pages="152--156",
  publisher="University of Žilina",
  type="conference paper"
}