New Hybrid Technique for traffic sign recognition
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A hybrid traffic sign recognition scheme combining of knowledge-based analysis and radial basis function neural classifier (RBFNN) is proposed in this paper. Initially, traffic signs are detected from the road scenes using color segmentation method. The extracted signs are then passed to the recognition system for classification. The proposed recognition technique composes of three stages: (i) color histogram classification, (ii) shape classification and, (iii) RBF neural classification. Based on the unique color and shape of traffic signs, they can be classified into smaller subclasses and can be easily recognized using RBFNN. Before feeding traffic sign into the RBFNN, traffic sign features are extracted by Principle Component Analysis (PCA) in order to reduce the dimensionality of the original images. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. The performance of the proposed hybrid system is evaluated and compared to the purely neural classifier. The experimental results demonstrate that the proposed method has better recognition rate.
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Lim, Hann; Seng, K.; Ang, L. (2010)This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural ...
Lau, M.; Lim, Hann; Gopalai, A. (2015)Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, ...
Lim, King Hann; Seng, Kah Phooi; Ang, Li-Minn (2012)Lyapunov theory-based radial basis function neural network (RBFNN) is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO) classification. Multidimensional input is ...