Automatic road marking recognition for intelligent vehicle systems application. (May 2017)
- Record Type:
- Journal Article
- Title:
- Automatic road marking recognition for intelligent vehicle systems application. (May 2017)
- Main Title:
- Automatic road marking recognition for intelligent vehicle systems application
- Authors:
- Gang, Longhui
Zhang, Mingheng
Zhang, Lianfeng
Hu, Juanjuan - Abstract:
- Automatic road markings recognition is important for the research of intelligent vehicle which is used for both automotive navigation and advanced driver assistance system. In most previous researches, the markings such as lane have been used for localizing and serving the vehicle along the road. However, in fact, the markings such as guide arrows and warnings are necessary for automotive navigation. Therefore, this article presents an automatic road markings recognition method based on support vector machine to reduce the impact of external environment such as viewpoint, brightness, and background. In which, the input vector comprises four improved Hu moments and two affine invariant moments obtained from reconstructed image. The presented method has been tested with experiment images, and the results show that the accuracy of recognition can be reached over 97% and time consumption per frame is 0.26 s. It is clear that the proposed method has strong potential effectiveness to be applied for intelligent vehicle systems application.
- Is Part Of:
- Advances in mechanical engineering. Volume 9:Number 5(2017:May)
- Journal:
- Advances in mechanical engineering
- Issue:
- Volume 9:Number 5(2017:May)
- Issue Display:
- Volume 9, Issue 5 (2017)
- Year:
- 2017
- Volume:
- 9
- Issue:
- 5
- Issue Sort Value:
- 2017-0009-0005-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-05
- Subjects:
- Intelligent vehicle -- advanced driver assistance system -- scene reconstruction -- support vector machine -- recognition
Mechanical engineering -- Periodicals
621.05 - Journal URLs:
- http://ade.sagepub.com/content/current ↗
http://www.hindawi.com/journals/ame ↗
http://www.uk.sagepub.com ↗ - DOI:
- 10.1177/1687814017706267 ↗
- Languages:
- English
- ISSNs:
- 1687-8132
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 13860.xml