Open AccessTraffic sign detection based on Haar and adaBoost classifier
Author(s) -
Wenhao Wang,
Hui Gao,
Xiaobing Chen,
Zhenyang Yu,
Mingxing Jiang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012091
Subject(s) - haar like features , adaboost , haar , artificial intelligence , pattern recognition (psychology) , computer science , classifier (uml) , traffic sign , traffic sign recognition , cascade , intelligent transportation system , computer vision , feature extraction , face detection , sign (mathematics) , engineering , mathematics , facial recognition system , mathematical analysis , civil engineering , chemical engineering , wavelet
Intelligent transportation system is a hot issue in the field of computer vision. Traffic sign detection is an important part of intelligent transportation system. A traffic sign detection algorithm based on Haar feature and AdaBoost algorithm is proposed in this paper. Firstly, Haar features are extracted from the positive and negative samples of the training set, and then these features are used to train the AdaBoost cascade classifier. Finally, the Haar features are extracted from the test image, and the possible traffic signs in the image are detected, the experimental results show that the method can achieve good detection effect, and provide a new idea for the detection of traffic signs.