Real-Time Video Road Sign Detection And Tracking Using Image Processing And Autonomous Car

Abdul Azis, Fadilah and Ponaseran, P.S. Giritharan and Md Sani, Zamani and Mohd Aras, Mohd Shahrieel and Othman, Muhammad Nur (2019) Real-Time Video Road Sign Detection And Tracking Using Image Processing And Autonomous Car. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8 (12S2). pp. 495-500. ISSN 2278-3075

Text L109410812S219.PDF Download (508kB)

Abstract

Detection and monitoring of real-time road signs are becoming today's study in the autonomous car industry. The number of car users in Malaysia risen every year as well as the rate of car crashes. Different types, shapes, and colour of road signs lead the driver to neglect them, and this attitude contributing to a high rate of accidents. The purpose of this paper is to implement image processing using the real-time video Road Sign Detection and Tracking (RSDT) with an autonomous car. The detection of road signs is carried out by using Video and Image Processing technique control in Python by applying deep learning process to detect an object in a video’s motion. The extracted features from the video frame will continue to template matching on recognition processes which are based on the database. The experiment for the fixed distance shows an accuracy of 99.9943% while the experiment with the various distance showed the inversely proportional relation between distances and accuracies. This system was also able to detect and recognize five types of road signs using a convolutional neural network. Lastly, the experimental results proved the system capability to detect and recognize the road sign accurately.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network, Image processing, Real-time, Road signs, Video Road Sign Detection, Image Processing, Autonomous Car
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	30 Jul 2020 15:14
Last Modified:	30 Jul 2020 15:14
URI:	http://eprints.utem.edu.my/id/eprint/24253
Statistic Details:	View Download Statistic

Actions (login required)

View Item