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

محورهای موضوعی : Majlesi Journal of Telecommunication Devices

Hamidreza Emami ¹ (Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.)
Ramin Shaghaghi Kandowan ² (Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.)
Seyyed Abolfazl Hosseini ³ (Yadegar-e-Imam Khomeini(RAH)Shahre Rey Branch, Islamic Azad University, Tehran, Iran.)

تاریخ دریافت : 1399/08/17 تاریخ پذیرش : 1399/09/02 تاریخ انتشار : 1399/09/11

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

چکیده مقاله :

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.

منابع و مأخذ:

[1] Fleyeh, H. “Color detection and segmentation for road and traffic signs”, IEEE Conference on Cybernetics and Intelligent Systems, Singapore, pp. 808–813, 2004.

[2] Fleyeh, H. (2006) “Shadow and highlight invariant colour segmentation algorithm for traffic signs”. IEEE Conference on Cybernetics and Intelligent Systems, Bangkok, Thailand, pp. 108–114, 2006.

[3] Prachi Dewan, Rekha Vig, Neeraj Shukla, B. K. Das, “An overview of traffic signs Rrecognition methods”, International Journal of Computer Applications (0975 – 8887), Vol. 168, No. 11, June 2017.

[4] Dalal, N. & Triggs, B. “Histograms of oriented gradients for human detection”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society Washington, DC, pp. 886–893, 2005.

[5] Fleyeh Hasan, Roch Janina, “Benchmark evaluation of HOG descriptors as features for classifications of traffic signs”, International Journal for Traffic and Transport Engineering, 2013/12/30.

[6] Robert P. Loce, Raja Bala, Mohan Trivedi, “Computer Vision and Imaging in Intelligent Transportation Systems”, John Wiley & Sons, pp. 361, 2017.

[7] Sang-Seol Lee, Eunchong Lee, Youngbae Hwang and Sung-Joon Jang, “Low-complexity hardware architecture of traffic sign recognition with IHSL color space for advanced driver assistance systems” IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 2016.

[8] Dalarna University traffic sign dataset available online at: http://users.du.se/~hfl/traffic_signs/.

[9] Dogancan Temel, Tariq Alshawi, Min-Hung Chen, and Ghassan AlRegib, “Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions” in arXiv:1902.06857, 2019.