U. Zakir
A novel road traffic sign detection and recognition approach by introducing CCM and LESH
Zakir, U.; Usman, A.; Hussain, A.
Abstract
A real time road sign detection and recognition system can provide an additional level of driver assistance leading to an improved safety to passengers, road users and other vehicles. Such Advanced Driver Assistance Systems (ADAS) can be used to alert a driver about the presence of a road sign by reducing the risky situation during distraction, fatigue and in the presence of poor driving conditions. This paper is divided into two parts: Detection and Recognition. The detection part includes a novel Combined Colour Model (CCM) for the accurate and robust road sign colour segmentation from video stream. It is complemented by a novel approach to road sign recognition which is based on Local Energy based Shape Histogram (LESH). Experimental results and a detailed analysis to prove the effectiveness of the proposed vision system are provided. An accuracy rate of above 97.5% is recorded.
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | 19th International Conference, ICONIP 2012 |
Start Date | Nov 12, 2012 |
End Date | Nov 15, 2012 |
Publication Date | 2012 |
Deposit Date | Oct 11, 2019 |
Publisher | Springer |
Pages | 629-636 |
Series Title | Lecture Notes in Computer Science |
Series Number | 7665 |
Series ISSN | 0302-9743 |
Book Title | Neural Information Processing |
ISBN | 978-3-642-34486-2 |
DOI | https://doi.org/10.1007/978-3-642-34487-9_76 |
Public URL | http://researchrepository.napier.ac.uk/Output/1793180 |
You might also like
Applications of Deep Learning and Reinforcement Learning to Biological Data
(2018)
Journal Article
Guided Policy Search for Sequential Multitask Learning
(2018)
Journal Article
Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization
(2018)
Journal Article
Cross-modality interactive attention network for multispectral pedestrian detection
(2018)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search