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

• Main Authors: Abdul Azis, Fadilah, Ponaseran, P.S. Giritharan, Md Sani, Zamani, Mohd Aras, Mohd Shahrieel, Othman, Muhammad Nur
• Format: Article
• Language: English
• Published: Blue Eyes Intelligence Engineering & Sciences Publication 2019
• Online Access: http://eprints.utem.edu.my/id/eprint/24253/2/L109410812S219.PDF; http://eprints.utem.edu.my/id/eprint/24253/; http://www.ijitee.org/wp-content/uploads/papers/v8i12S2/L109410812S219.pdf
• Institution: Universiti Teknikal Malaysia Melaka

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.