Traffic Sign Detection Based on Regions of Interest and HOG-MBLBP Features

LIU Chengyun; CHANG Faliang; CHEN Zhenxue

doi:10.11999/JEIT150918

Volume 38 Issue 5

May 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(5): 1092-1098

LIU Chengyun, CHANG Faliang, CHEN Zhenxue. Traffic Sign Detection Based on Regions of Interest and HOG-MBLBP Features[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1092-1098. doi: 10.11999/JEIT150918

Citation:

LIU Chengyun, CHANG Faliang, CHEN Zhenxue. Traffic Sign Detection Based on Regions of Interest and HOG-MBLBP Features[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1092-1098. doi: 10.11999/JEIT150918

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Traffic Sign Detection Based on Regions of Interest and HOG-MBLBP Features

doi: 10.11999/JEIT150918

Funds:

The National Natural Science Foundation of China (61273277, 61203261), Shandong Province Natural Science Foundation (ZR2011FM032, ZR2012FQ003), Specialized Research Fund for the Doctoral Program of Higher Education (20130131110038)

Received Date: 2015-08-05
Rev Recd Date: 2015-12-25
Publish Date: 2016-05-19

Abstract

Abstract

The imbalance between sample categories in traffic sign detection often results in the weakening of classification detection performance. To overcome this problem, a traffic sign detection method is proposed based on regions of interest and Histogram of Oriented Gradient and Multi-radius Block Local Binary Pattern (HOG-MBLBP) features. First, the color enhancement technology is used to segment and extract the regions of interest of the traffic signs captured in the natural background. Then HOG-MBLBP fusion features are extracted from traffic signs sample library. Moreover, genetic algorithm is used to optimize the parameters of Support Vector Machine (SVM) through cross-validation so as to train and promote SVM classifier performance. Finally, extracted HOG-MBLBP features of interest region images are put into the trained SVM multi-classifiers for further accurate detection and localization. By this method, the paper achieves the purpose of excluding false positives area. The experiments are carried out on the self-built Chinese traffic sign sample library, experimental results show that the proposed method can achieve 99.2% of classification accuracy, and the confusion matrix results also show the superiority of the proposed method.

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References(29)

References

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