Recognition of traffic sign based on Bag-of-Words and Artificial Neural Network

Kh Tohidul Islam, Ram Gopal Raj, Ghulam Mujtaba

Research output: Contribution to journalArticleResearchpeer-review

32 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number138
Number of pages21
JournalSymmetry
Volume9
Issue number8
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Artificial intelligence
  • Feature extraction
  • Image classification
  • Intelligent systems
  • Pattern recognition
  • Traffic sign detection and recognition

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