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

Islam, K.T. and Raj, R.G. and Mujtaba, G. (2017) Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network. Symmetry, 9 (8). p. 138. ISSN 2073-8994, DOI

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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.

Item Type: Article
Uncontrolled Keywords: artificial intelligence; intelligent systems; pattern recognition; image classification; feature extraction; traffic sign detection and recognition
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 06 Sep 2018 04:30
Last Modified: 06 Sep 2018 04:30

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