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- W2373055845 abstract "Traffic sign detection is important in intelligent transport system.In this paper,an efficient novel approach is proposed to achieve automatic traffic sign detection.The detection method combines color segmentation with learning based multi-features of traffic sign guided search.The rough location stage could obtain possible region of traffic sign using color segmentation based on YIQ space.The exact location stage searches traffic sign in these traffic sign possible regions based on multi-features fusion,we use histogram of oriented gradient(HOG) and local binary pattern(LBP) to classify by support vector machine(SVM).Experimental results show that,the proposed approach can achieve robustness to illumination,scale,viewpoint change and even partial occlusion.The average detection rate and the false positive rate of our approach are better than the method based on one feature." @default.
- W2373055845 created "2016-06-24" @default.
- W2373055845 creator A5059040206 @default.
- W2373055845 date "2011-01-01" @default.
- W2373055845 modified "2023-09-25" @default.
- W2373055845 title "Traffic Sign Detection Based on Color Segmentation and Multi-features Fusion" @default.
- W2373055845 hasPublicationYear "2011" @default.
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