Superpixel based semantic segmentation for assistance in varying terrain driving conditions

Ionut Gheorghe, Weidong Li, Thomas Popham, Keith J. Burnham

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Vehicle drivability and maneuverability can be improved by increasing the environment awareness via sensory inputs. In particular, off-road capable vehicles possess subsystems which are configurable to the driving conditions. In this work, a vision solution is explored as a precursor to autonomous toggling between different operating modes. The emphasis is on selecting an appropriate response to transitions from one terrain type to another. Given a forward facing camera, images are partitioned into pixel subsets known as superpixels in order to be classified. The quality of this semantic segmentation is considered for classes such as {grass, tree, sky, tarmac, dirt, gravel, shrubs}. Colour and texture are combined together to form visual cues and address this image recognition problem with good segmentation results
Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing: Proceedings of the Twenty-Third International Conference on Systems Engineering
EditorsHenry Selvaraj, Dawid Zydek, Grzegorz Chmaj
PublisherSpringer Verlag
ISBN (Print)978-3-319-08421-3
Publication statusPublished - 2015
Event23rd International Conference on Systems Engineering - Las Vegas, United States
Duration: 19 Aug 201421 Aug 2014
Conference number: 23


Conference23rd International Conference on Systems Engineering
Abbreviated titleICSEng2014
Country/TerritoryUnited States
CityLas Vegas

Bibliographical note

This paper is not available on the repository.


  • colour
  • machine learning
  • semantic segmentation
  • superpixels
  • Terrain classification
  • texture


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