Traffic Signs Recognition and Classification based on Deep Feature Learning
Yan Lai, Nanxin Wang, Yusi Yang, Lan Lin
2018
Abstract
Traffic signs recognition and classification play an important role in the unmanned automatic driving. Various methods were proposed in the past years to deal with this problem, yet the performance of these algorithms still needs to be improved to meet the requirements in real applications. In this paper, a novel traffic signs recognition and classification method is presented based on Convolutional Neural Network and Support Vector Machine (CNN-SVM). In this method, the YCbCr color space is introduced in CNN to divide the color channels for feature extraction. A SVM classifier is used for classification based on the extracted features. The experiments are conducted on a real world data set with images and videos captured from ordinary car driving. The experimental results show that compared with the state-of-the-art methods, our method achieves the best performance on traffic signs recognition and classification, with a highest 98.6% accuracy rate.
DownloadPaper Citation
in Harvard Style
Lai Y., Wang N., Yang Y. and Lin L. (2018). Traffic Signs Recognition and Classification based on Deep Feature Learning.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 622-629. DOI: 10.5220/0006718806220629
in Bibtex Style
@conference{icpram18,
author={Yan Lai and Nanxin Wang and Yusi Yang and Lan Lin},
title={Traffic Signs Recognition and Classification based on Deep Feature
Learning},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={622-629},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006718806220629},
isbn={978-989-758-276-9},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Traffic Signs Recognition and Classification based on Deep Feature
Learning
SN - 978-989-758-276-9
AU - Lai Y.
AU - Wang N.
AU - Yang Y.
AU - Lin L.
PY - 2018
SP - 622
EP - 629
DO - 10.5220/0006718806220629