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Yadav, Shubham
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Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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Indian Traffic Signboard Recognition and Driver Alert System Using Machine Learning Yadav, Shubham; Patwa, Anuj; Rane, Saiprasad; Narvekar, Chhaya
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (877.436 KB) | DOI: 10.24071/ijasst.v1i1.1843

Abstract

Sign board recognition and driver alert system which has a number of important application areas that include advance driver assistance systems, road surveying and autonomous vehicles. This system uses image processing technique to isolate relevant data which is captured from the real time streaming video. The proposed method is broadly divided in five part data collection, data processing, data classification, training and testing. System uses variety of image processing techniques to enhance the image quality and to remove non-informational pixel, and detecting edges. Feature extracter are used to find the features of image. Machine learning algorithm Support Vector Machine(SVM) is used to classify the images based on their features. If features of sign that are captured from the video matches with the trained traffic signs then it will generate the voice signal to alert the driver. In India there are different traffic sign board and they are classified into three categories: Regulatory sign, Cautionary sign, informational sign. These Indian signs have four different shapes and eight different colors. The proposed system is trained for ten different types of sign . In each category more than a thousand sample images are used to train the network.                           
Co-Authors Narvekar, Chhaya Patwa, Anuj Rane, Saiprasad