Development of Traffic Violation Tracker using Machine Learning

Abstract: In the new evolving world, traffic rule violations have become a central issue for the majority of developing countries. The number of vehicles is increasing rapidly as well as the number of traffic rule violations is 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 traffic rule violations accurately and cost-effectively[6]. The proposed model includes an automated system which uses a camera-based on a PC to capture video. [4]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 the 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.