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

Subject Areas : Majlesi Journal of Telecommunication Devices

Hamidreza Emami ¹ , Ramin Shaghaghi Kandowan ² , Seyyed Abolfazl Hosseini ³

1 - Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.
2 - Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.
3 - Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.

Received: 2020-11-07 Accepted : 2020-11-22 Published : 2020-12-01

Keywords: Histogram of Oriented Gradients, Support vector machine, Traffic Sign Recognition, Low-Complexity, HOG, SVM, Shape Recognition, Advanced Driver Assistance Systems, Centroid Distance,

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.

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