TRAFFIC SIGN DETECTION AND RECOGNITION: REVIEW AND ANALYSIS

Authors

  • Nursabillilah Mohd Ali Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Mohd Safirin Karis Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Amar Faiz Zainal Abidin Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Bahzifadhli Bakri Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Ezreen Farina Shair Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Nur Rafiqah Abdul Razif Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia

DOI:

https://doi.org/10.11113/jt.v77.6559

Keywords:

Traffic sign, detection and recognition, HSV, RGB, Bhattacharyya Coefficient

Abstract

Over the years, traffic sign detection and recognition systems gives extra value to driver assistance when driving, leading to more user-friendly driving experience and much improved safety for passengers. As part of Advanced Driving Systems (ADAS) one can be benefitted by using this system especially with driving incapacities by alerting and aid them about the existence of traffic signs to minimized unwanted circumstances during driving such as fatigue, poor sight and adverse weather conditions. Though a various number of traffic sign detection systems have been revised in literature; the need of design with a robust algorithm still remains open for further research. This paper purposes to design a system capable of performing traffic sign detection while considering variations of challenges such as color illumination, computational difficulty and functional constraints existed. Traffic sign detection is divided into three main parts namely; Pre-processing, Color segmentation and Thresholding. The color segmentation method is vital as it presents a detailed investigation of vision based color spaces in this case RGB, HSV and CMYK considering varying illumination conditions under different environments. This paper further highlights possible improvements to the proposed approaches for traffic sign detection.

References

G. Siogkas, E. Dermatas. 2006. 1st Initial, Detection, Tracking and Classification of Traffic Signs in Adverse Conditions. IEEE MELECON 2006. 537-540.

Mohd Ali, N., Mohd Sobran, N., Md Ghazaly, M., Ab Shukor, SA. and Tuani, AF. 2014. Traffic Sign Detection and Classification for Driver Assistant System (DAS). 277-283

M. Benallal and J. Meunier, 2003. Real Time Color Segmentation of Traffic Signs. IEEE CCECE 2003 Canadian Conference on Electrical and Computer Engineering.

Mohd Ali, N., Mohd Sobran, N., Md Ghazaly, M., Ab Shukor, SA. and Tuani, AF. 2014. Individual Processing Speed Analysis for Traffic Sign Detection and Recognition. Smart Instrmentation, Measurement and Applications (ICSIMA). 2013, Kuala Lumpur, Malaysia.

Mohd Ali, N., Karis, MS. and Safei, J. 2014. Hidden Nodes of Neural Network: Useful Application in Traffic Sign Recognition. Smart Instrmentation, Measurement and Applications (ICSIMA), 2014, Kuala Lumpur, Malaysia.

Mohd Ali, N., Md Rahid, NK. and Mohd Mustafah, Y. 2013. Performance Comparison between RGB and HSV Color Segmentations for Traffic Sign Detection. Applied Mechanics and Materials. 393: 550-555.

Zakir, U., Leonce, NJ., and Edirisinghe, EA. 2010. Traffic Sign Segmentation based on Color Spaces: A Comparative Study. In the Proceedings of the 11th International Conference on Computer Graphics and Imaging, Innabruck, Austria, 2010.

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Published

2015-12-01

How to Cite

Mohd Ali, N., Karis, M. S., Zainal Abidin, A. F., Bakri, B., Shair, E. F., & Abdul Razif, N. R. (2015). TRAFFIC SIGN DETECTION AND RECOGNITION: REVIEW AND ANALYSIS. Jurnal Teknologi, 77(20). https://doi.org/10.11113/jt.v77.6559