Traffic Signs Recognition and Classification based on Deep Feature Learning

Yan Lai, Nanxin Wang, Yusi Yang, Lan Lin

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.

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Paper 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