This paper is published in Volume-4, Issue-3, 2018
Area
Image Processing
Author
Ramesh K. Admane, Devendra Patle
Org/Univ
Sri Satya Sai University of Technology and Medical Sciences, Sehore, Madhya Pradesh, India
Pub. Date
26 May, 2018
Paper ID
V4I3-1567
Publisher
Keywords
Traffic sign, Image processing, Classification, Support vector machine, Speeded-up robust features.

Citationsacebook

IEEE
Ramesh K. Admane, Devendra Patle. Traffic sign detection from video using image processing, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Ramesh K. Admane, Devendra Patle (2018). Traffic sign detection from video using image processing. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Ramesh K. Admane, Devendra Patle. "Traffic sign detection from video using image processing." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

Since past decades, more people in India are demanding own vehicles. Population in India increases day by day and so transportation methods. Road accidents are increased especially on highways. Many peoples get injured and lost their life because they do not see the road sign boards and also not aware of the meaning of road sign boards. Proposed automatic road sign detection system enhances intelligent transportation system by providing information about road sign to the driver and keeps drivers view on road, which will minimize accidents on the road and saves the life of people. A proposed system identifies the road sign using image processing techniques. A system collects the video of road signs with the help of a camera which is mounted on moving vehicle. Image videos are converted into frames of the image at the rate of 25frames/sec. Image pre-processing techniques such as converting the RGB image to greyscale, resizing of the image are used. Images are enhanced using median filtering technique. To extract features of image speeded-up robust features (SURF) technology is used. The standard road sign images are stored in the database and for classification of features, linear support vector machine (SVM) is used. Results are tested on 10 videos of road signs and it shows that proposed method correctly detects normal, blur and partial view of road sign videos.