Abstract
In this paper we explain the proposed method of traffic sign detection and classification for driver assistant system (DAS). Color detection framework using RGB method is utilized in this study, whereas an artificial neural network (ANN) has been used as classifiers for classification. There are at least 100 types of Malaysian Traffic Signs have been employed in this research. Most of the images are taken at various places throughout the urban and suburban areas involved with scale, illumination and rotational changes as well as occlusion images. The experimental results are shown that the proposed framework achieved at least 80 % successful detection with 21 false positive images. On the other hand, the ANN gives strong rates where at least most of the signs can be classify with more than 85 % success.
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© 2014 Springer Science+Business Media Singapore
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Ali, N.M., Sobran, N.M.M., Ghazaly, M.M., Shukor, S.A., Ibrahim, A.F.T. (2014). Traffic Sign Detection and Classification for Driver Assistant System. In: Mat Sakim, H., Mustaffa, M. (eds) The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications. Lecture Notes in Electrical Engineering, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-4585-42-2_32
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DOI: https://doi.org/10.1007/978-981-4585-42-2_32
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