Forbidden Traffic Signs Detection and Recognition Based on Sparse Representation
- DOI
- 10.2991/meic-14.2014.174How to use a DOI?
- Keywords
- Detection;HSI;recognition;forbidden;traffic signs;sparse representation
- Abstract
This paper presents an automatic traffic-sign detection and recognition algorithm based on sparse representation. Our system consists of several steps. In the first stage, we detect potential traffic signs using the most remarkable feature-color-extracted by HSI model, which is immune to lighting changes. In the second stage, the preprocessing stage, image binaryzation and image cutting help the system to extract candidate traffic-sign regions. Then OMP algorithm is performed to calculate the sparse coefficients of candidate traffic-sign regions on dictionary D. The dictionary D is constructed by training images with different rotation. Finally, the sparse coefficients are used to classify. The algorithm proposed offers high performance and better accuracy especially in variable lighting conditions and rotation. Because of its real time and accuracy, this algorithm can be used in real world application.
- Copyright
- © 2014, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Sheng Guo AU - Jianhua Li AU - Shuping Zhao PY - 2014/11 DA - 2014/11 TI - Forbidden Traffic Signs Detection and Recognition Based on Sparse Representation BT - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering PB - Atlantis Press SP - 779 EP - 783 SN - 2352-5401 UR - https://doi.org/10.2991/meic-14.2014.174 DO - 10.2991/meic-14.2014.174 ID - Guo2014/11 ER -