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Article

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

1
Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
2
Department of Information Systems, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
3
Department of Computer Science, Sukkur Institute of Business Administration, Sukkur 56200, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: Yunsick Sung
Symmetry 2017, 9(8), 138; https://doi.org/10.3390/sym9080138
Received: 25 April 2017 / Revised: 14 July 2017 / Accepted: 27 July 2017 / Published: 30 July 2017
(This article belongs to the Special Issue Advanced in Artificial Intelligence and Cloud Computing)
The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the well-maintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system. View Full-Text
Keywords: artificial intelligence; intelligent systems; pattern recognition; image classification; feature extraction; traffic sign detection and recognition artificial intelligence; intelligent systems; pattern recognition; image classification; feature extraction; traffic sign detection and recognition
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MDPI and ACS Style

Islam, K.T.; Raj, R.G.; Mujtaba, G. Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network. Symmetry 2017, 9, 138. https://doi.org/10.3390/sym9080138

AMA Style

Islam KT, Raj RG, Mujtaba G. Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network. Symmetry. 2017; 9(8):138. https://doi.org/10.3390/sym9080138

Chicago/Turabian Style

Islam, Kh Tohidul; Raj, Ram Gopal; Mujtaba, Ghulam. 2017. "Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network" Symmetry 9, no. 8: 138. https://doi.org/10.3390/sym9080138

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