Open AccessA REVIEW STUDY OF TRAFFIC SIGNAL VIOLATION DETECTION USING ARTIFICIAL INTELLIGENCE
Author(s) -
Dantene Davis,
Amit Kumar Singh,
Amarjeeth Singh,
Fahad Ahmad
Publication year - 2021
Publication title -
epra international journal of research and development
Language(s) - English
Resource type - Journals
ISSN - 2455-7838
DOI - 10.36713/epra7139
Subject(s) - computer science , artificial intelligence , computer vision , raspberry pi , canny edge detector , process (computing) , task (project management) , enhanced data rates for gsm evolution , optical character recognition , artificial neural network , real time computing , image processing , edge detection , pattern recognition (psychology) , image (mathematics) , computer security , engineering , systems engineering , operating system , internet of things
In the new evolving world, traffic rule violations have become a central issue for majority of the developing countries. The numbers of vehicles are increasing rapidly as well as the numbers of traffic rule violations are increasing exponentially. Managing traffic rule violations has always been a tedious and compromising task. Even though the process of traffic management has become automated, it’s a very challenging problem, due to the diversity of plate formats, different scales, rotations and non-uniform illumination conditions during image acquisition. The principal objective of this project is to control the traffic rule violations accurately and cost effectively. The proposed model includes an automated system which uses IR sensors and camera based on Raspberry PI to capture video. The project presents Automatic Number Plate Recognition (ANPR) techniques and other image manipulation techniques for plate localization and character recognition which makes it faster and easier to identify the number plates. After recognizing the vehicle number from number plate, the SMS based module is used to notify the vehicle owners about their traffic rule violation. An additional SMS is sent to Regional Transport Office (RTO) for tracking the report status.KEYWORDS- Automatic Number Plate Recognition (ANPR), Artificial Neural Network, Image acquisition, CNN, Tesseract OCR, Canny Edge Detection.