Centroid Distance Shape Recognition for Real Time Low Complexity Traffic Sign Recognition

  • Hamidreza Emami Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.
  • Ramin Shaghaghi Kandowan Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.
  • Seyyed Abolfazl Hosseini Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.
Keywords: Traffic Sign Recognition, Advanced Driver Assistance Systems, Centroid Distance, Histogram of Oriented Gradients, HOG, Support Vector Machine, SVM, Shape Recognition, Low-Complexity

Abstract

This paper represents advantages of using Centroid distance function for shape detection in real time traffic sign recognition compared with extracting histogram of oriented gradients (HOG) features and using support vector machine (SVM) classifier. Simulation results of using centroid distance show similar accuracy in compare with HOG SVM while have very low complexity and cost and running with higher speed.

References

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Published
2020-12-01
How to Cite
Emami, H., Shaghaghi Kandowan, R., & Hosseini, S. A. (2020). Centroid Distance Shape Recognition for Real Time Low Complexity Traffic Sign Recognition. Majlesi Journal of Telecommunication Devices, 9(4), 157-160. Retrieved from http://journals.iaumajlesi.ac.ir/td/index/index.php/td/article/view/641
Section
Articles