Referenced Items are not available for this document.
Traffic-sign recognition (TSR) is an essential component of a driver assistance system (DAS), providing drivers with safety and precaution information. In this paper, we evaluate the performance of k-d trees, random forests, and support vector machines (SVMs) for traffic-sign classification using different-sized histogram-of-oriented-gradient (HOG) descriptors and distance transforms (DTs). We also use the Fisher's criterion and random forests for the feature selection to reduce the memory requirements and enhance the performance. We use the German Traffic Sign Recognition Benchmark (GTSRB) data set containing 43 classes and more than 50 000 images.
Published in:
Intelligent Transportation Systems, IEEE Transactions on
(Volume:13
,
Issue:
4
)
Date of Publication: Dec. 2012
- Page(s):
- 1507 - 1514
- ISSN :
- 1524-9050
- INSPEC Accession Number:
- 13148955
- Digital Object Identifier :
- 10.1109/TITS.2012.2225618
- Product Type:
- Journals & Magazines
- Date of Publication :
- 15 November 2012
- Date of Current Version :
- 27 November 2012
- Issue Date :
- Dec. 2012
- Sponsored by :
- IEEE Intelligent Transportation Systems Society
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
