A Survey of Different Image Processing Methods for the Design and Development of an Efficient Traffic Sign Board Recognition System

PDF

Published: 2021-11-30

Page: 433-438


Abhinav V. Deshpande *

School of Electronics Engineering (SENSE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.

*Author to whom correspondence should be addressed.


Abstract

As far as the safety of a driver is concerned, more focus should be put on correct interpretation and information which is conveyed by a traffic sign, while driving a vehicle along the road. A sign board can be thought of as an emblem which disseminates important and meaningful information regarding the potential hazards prevailing among road users comprising roadways cladded with snowfall, construction worksites or repairing of roads taking place and telling the people to follow an alternative route. It alerts the person who is passing through the road about the maximum possible extremity that his vehicle is trying to achieve indicating slowing down the speed of vehicle since chances of having collision cannot be ruled out. The traffic sign images were acquired from the image database and were subjected to some pre-processing techniques such as conversion of original into gray scale images, filtering the images with the help of Averaging Filter, Wiener Filter, carrying out the operation of Unsharp Mask Filtering which consists of performing the conventional method of Unsharp Mask Filtering as well as controlling the amount of sharpness with the help of certain parameters. In the future, we will concentrate on detecting, recognizing as well as classifying a particular sign board.

Keywords: Averaging filter, wiener filter, unsharp mask filtering, color image, pixel values, light intensity


How to Cite

Deshpande, A. V. (2021). A Survey of Different Image Processing Methods for the Design and Development of an Efficient Traffic Sign Board Recognition System. Asian Basic and Applied Research Journal, 3(1), 433–438. Retrieved from https://globalpresshub.com/index.php/ABAARJ/article/view/1361

Downloads

Download data is not yet available.

References

Huaping Liu, Yulong Liu, Fuchun Sun, Traffic sign recognition using group sparse coding”, Elsevier- Information Sciences. 2014;266:75-89.

Zhan-Li Sun, Han Wang, Wai-Shing Lau, Gerald Seet, Danwei Wang, Application of BW-ELM model on traffic sign recognition, Elsevier- Neurocomputing. 2014;128:153-159.

Fatin Zaklouta, Bogdan Stanciulescu, Real-time traffic sign recognition in three stages”, Elsevier- Robotics and Autonomous Systems. 2012;62:16-24.

Shuihua Wang, Hangrong Pan, Chenyang Zhang, Yingli Tian, “RGB-D image-based detection of stairs, pedestrian crosswalks and traffic Signs”, Elsevier- Journal of Visual Communication and Image Retrieval. 2014;25:263-272.

Jonathan J. Kay, Peter T. Savolainen, Timothy J. Gates, Tapan K. Datta, “Driver behavior during bicycle passing maneuvers in response to a Share the Road sign treatment”, Elsevier- Accident Analysis and Prevention. 2014;70:92-99.

Jesmin Khan, Sharif Bhuiyan, Reza Adhami, “Hierarchical clustering of EMD based interest points for road sign detection”, Elsevier-Optics & Laser Technology. 2014;57:271-283.

Zong-Yao Chen, Wei-Chao Lin, Shih-Wen Ke, Chih-Fong Tsai, “Evolutionary feature and instance selection for traffic sign recognition”, Elsevier- Computers in Industry. 2015;74:201-211.

Samuele Salti, Alioscia Petrelli, Federico Tombari, Nicola Fioraio, Luigi Di Stefano, “Traffic sign detection via interest region extraction”, Elsevier- Pattern Recognition. 2014;48:1039-1049.

Lillo-Castellano JM, Mora-Jiménez I, Figuera-Pozuelo C, Rojo-Álvarez JL. Traffic sign segmentation and classification using statistical learning methods”, Elsevier- Neurocomputing. 2015;153:286-299.

Haojie Li, Fuming Sun, Lijuan Liu, Ling Wang. A novel traffic sign detection method via color segmentation and robust shape matching. Elsevier- Neurocomputing. 2015;169:77-88.