Automatic Road Traffic Sign Detection and Recognition using MATLAB |
Author(s): |
V. Venmathi , SNS COLLEGE OF ENGINEERING; Y. Divya, SNS COLLEGE OF ENGINEERING; K. R. Hema, SNS COLLEGE OF ENGINEERING; S. Hemalatha, SNS COLLEGE OF ENGINEERING |
Keywords: |
Roadsign recognition (RSR), Automatic detection, Classification, Segmentation, Cropped images |
Abstract |
This paper presents a method for detection and recognition of traffic signs. We proposed a new recognition approach of traffic signs, which has the feature of introducing automatic detection through image processing. We designed and built a prototype system by implementation of C and Open CV library. The experimental results show that our approach could have good prospects for automatic detection and recognition of traffic signs. As when human observe a traffic sign, a two-stage procedure is performed by first locating the sign according to its unique shape and color, and second paying attention to content inside the sign .In the detection module segments, the input image in a YCBCR colour space and then it detects road signs by using the image processing method. The experiment is to detect the road signs under varying lighting, rotation and translation conditions. Traffic road sign detection and recognition is important to transport system with a camera while driving in the road. This paper presents and overview the, traffic road sign detection and recognition we developed and implemented the procedure to extract the road sign natural complex image. The main objective of this paper is to design and construct a system which can automatically detect the direction of the road sign. This paper is based upon a major approach to detect the traffic sign .In this paper, we will demonstrate the basic idea of how to detect the traffic signs and extract it. This system will play an important role for the detection purpose of specific domains like schools, traffic sign, universities, hospitals, offices etc. |
Other Details |
Paper ID: IJSRDV5I10755 Published in: Volume : 5, Issue : 1 Publication Date: 01/04/2017 Page(s): 1633-1635 |
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