Automatic Tracking of Traffic Violations Using Machine Learning
In the modern emerging world, breaches of traffic laws have become a key concern for the majority of developing countries. The number of vehicles is steadily increasing, as well as the number of traffic law violations is exponentially growing. Managing violations of the traffic rules has always been a boring and compromising job. While the traffic management process has become automated, due to the complexity of interface formats, different sizes, rotations and non-uniform lighting conditions during image acquisition, this is a very challenging issue. The principal objective of this project is to control the traffic rule violations accurately and cost effectively. The proposed model implements an automated system that uses Raspberry PI-based IR sensors and camera to capture images. The project introduces Automatic Number Plate Recognition (ANPR) techniques and other image processing techniques for plate location and character recognition, allowing numeral plate identification simpler and easier to recognize. The SMS based module is used to inform vehicle owners of their traffic rule violation after identifying the vehicle number from the number plate. To monitor the report status an additional SMS is sent to the Regional Transport Office (RTO).