Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Authors

  • Xianyan Kuang Jiangxi University of Science and Technology
  • Wenbin Fu Jiangxi University of Science and Technology
  • Liu Yang Jiangxi University of Science and Technology

DOI:

https://doi.org/10.3991/ijoe.v14i03.7925

Keywords:

traffic sign detection and recognition, maximally stable extremal regions (MSER), random forests, geometry moment invariants, image enhancement

Abstract


Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set.

Author Biographies

Xianyan Kuang, Jiangxi University of Science and Technology

School of Electrical Engineering and Automation

Wenbin Fu, Jiangxi University of Science and Technology

School of Electrical Engineering and Automation

Liu Yang, Jiangxi University of Science and Technology

School of Electrical Engineering and Automation

Downloads

Published

2018-03-30

How to Cite

Kuang, X., Fu, W., & Yang, L. (2018). Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests. International Journal of Online and Biomedical Engineering (iJOE), 14(03), pp. 34–51. https://doi.org/10.3991/ijoe.v14i03.7925

Issue

Section

Papers