Abstract
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 language | English |
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Title of host publication | Advances in Intelligent Systems and Computing: Proceedings of the Twenty-Third International Conference on Systems Engineering |
Editors | Henry Selvaraj, Dawid Zydek, Grzegorz Chmaj |
Publisher | Springer Verlag |
Pages | 691-698 |
Volume | 1089 |
ISBN (Print) | 978-3-319-08421-3 |
DOIs | |
Publication status | Published - 2015 |
Event | 23rd International Conference on Systems Engineering - Las Vegas, United States Duration: 19 Aug 2014 → 21 Aug 2014 Conference number: 23 |
Conference
Conference | 23rd International Conference on Systems Engineering |
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Abbreviated title | ICSEng2014 |
Country/Territory | United States |
City | Las Vegas |
Period | 19/08/14 → 21/08/14 |
Bibliographical note
This paper is not available on the repository.Keywords
- colour
- machine learning
- semantic segmentation
- superpixels
- Terrain classification
- texture