We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Recognition and Classification of Traffic Signs using Machine Learning Techniques

by Sandeep Rai, Kaminee Pachlasiya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 169 - Number 10
Year of Publication: 2017
Authors: Sandeep Rai, Kaminee Pachlasiya
10.5120/ijca2017914889

Sandeep Rai, Kaminee Pachlasiya . Recognition and Classification of Traffic Signs using Machine Learning Techniques. International Journal of Computer Applications. 169, 10 ( Jul 2017), 12-18. DOI=10.5120/ijca2017914889

@article{ 10.5120/ijca2017914889,
author = { Sandeep Rai, Kaminee Pachlasiya },
title = { Recognition and Classification of Traffic Signs using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 169 },
number = { 10 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume169/number10/28019-2017914889/ },
doi = { 10.5120/ijca2017914889 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:17:01.790993+05:30
%A Sandeep Rai
%A Kaminee Pachlasiya
%T Recognition and Classification of Traffic Signs using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 169
%N 10
%P 12-18
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The computerized recognition and classification of traffic signs is a challenging problem, with several important request areas, including advanced drivers assistance systems, autonomous vehicles and street surveying. While much research is present on both automated diagnosis and popularity of symbol-based traffic indicators there is much less research concentrated specifically on the reputation of wording on traffic information indications. This may be partial because of the difficulty of the duty brought on by problems, such as brightness and shadows, blurring, occlusion, and signal deterioration. Our method of this issue by detecting many text-based traffic indication prospects using basic condition and color information. The proposed system includes two main periods: Recognition and Classification. The Acceptance stage exploits the understanding of the composition of the Traffic indication, i.e., the condition and size of the sign in the frame, to look for the locations in the landscape that it will seek out traffic text indications.

References
  1. B. Riveiro, L. Díaz-Vilariño, B. Conde-Carnero, M. Soilán and P. Arias, "Automatic Segmentation and Shape-Based Classification of Retro-Reflective Traffic Signs from Mobile LiDAR Data," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 1, pp. 295-303, Jan. 2016.
  2. Y. Yang, Hengliang Luo, Huarong Xu and F. Wu, "Towards real-time traffic sign detection and classification," 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, 2014, pp. 87-92.
  3. A. Welzel, P. Reisdorf and G. Wanielik, "Improving Urban Vehicle Localization with Traffic Sign Recognition," 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, 2015, pp. 2728-2732.
  4. T. Chen and S. Lu, "Accurate and Efficient Traffic Sign Detection Using Discriminative AdaBoost and Support Vector Regression," in IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 4006-4015, June 2016..
  5. Jianmin Duan and M. Viktor, "Real-time road edges detection and road signs recognition," Control, Automation, and Information Sciences (ICCAIS), 2015 International Conference on, Changshu, 2015, pp. 107-112..
  6. Yanjun Fan and Weigong Zhang, "Traffic sign detection and classification for Advanced Driver Assistant Systems," Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on, Zhangjiajie, 2015, pp. 1335-1339..
  7. J. M. Patel and N. C. Gamit, "A review on feature extraction techniques in Content-Based Image Retrieval," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, 2016, pp. 2259-2263..
  8. K. Jayanthi and M. Karthikeyan, "Efficient fuzzy color and texture feature extraction technique for content-based image retrieval system," Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on, Coimbatore, 2014, pp. 1-5.
  9. Diwaker and M. Dutta, "Assessment of feature extraction techniques for hyperspectral image classification," Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in, Ghaziabad, 2015, pp. 499-502..
  10. S. Mahajan and D. Patil, "Image retrieval using contribution-based clustering algorithm with different feature extraction techniques," IT in Business, Industry and Government (CSIBIG), 2014 Conference on, Indore, 2014, pp. 1-7.
  11. S. Khalid, T. Khalil and S. Nasreen, "A survey of feature selection and feature extraction techniques in machine learning," Science and Information Conference (SAI), 2014, London, 2014, pp. 372-378.
  12. G. M. Hadjidemetriou, S. E. Christodoulou and P. A. Vela, "Automated detection of pavement patches utilizing support vector machine classification," 2016 18th Mediterranean Electrotechnical Conference (MELECON), Lemesos, 2016, pp. 1-5..
  13. Xiaowu Sun, Lizhen Liu, Hanshi Wang, Wei Song and Jingli Lu, "Image classification via support vector machine," 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), Harbin, China, 2015, pp. 485-489.
  14. Z. Malik and I. Siddiqi, "Detection and Recognition of Traffic Signs from Road Scene Images," 2014 12th International Conference on Frontiers of Information Technology, Islamabad, 2014, pp. 330-335.
  15. J. Greenhalgh and M. Mirmehdi, "Real-Time Detection and Recognition of Road Traffic Signs," in IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1498-1506, Dec. 2012.
  16. I. Sebanja and D. B. Megherbi, "Automatic detection and recognition of traffic road signs for intelligent autonomous unmanned vehicles for urban surveillance and rescue," 2010 IEEE International Conference on Technologies for Homeland Security (HST), Waltham, MA, 2010, pp. 132-138.
  17. S. Jung, U. Lee, J. Jung and D. H. Shim, "Real-time Traffic Sign Recognition system with deep convolutional neural network," 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Xi'an, 2016, pp. 31-34.
  18. Y. Wu and Z. Chen, "A detection method of road traffic sign based on inverse perspective transform," 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, 2016, pp. 293-296.
  19. A. J. Kale and R. C. Mahajan, "A road sign detection and the recognition for Driver Assistance Systems," 2015 International Conference on Energy Systems and Applications, Pune, 2015, pp. 69-74.
  20. Q. Hu, S. Paisitkriangkrai, C. Shen, A. van den Hengel and F. Porikli, "Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework," in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 4, pp. 1002-1014, April 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Recognition and Classification Machine Learning Image Processing Indian Traffic Signs.