DESIGN OF TRAFFIC AMERCEMENT AUTOMATION USING COMPUTER VISION

Computer Science

Traffic rule violations, such as speeding, not wearing a proper protective helmet, and running red lights, are major contributors to the high number of road accidents in India. According to National Crime Records Bureau data, over 1.5 lakh people lose their lives in road accidents across the country yearly, an average of 426 daily or 18 every hour. To address this issue, we propose an automated system for collecting traffic fines using machine learning techniques. The system utilizes object detection to identify vehicles that have violated traffic rules, using video surveillance cameras. It then extracts the number plate and an encrypted QR code of the vehicle, which can be used to retrieve the vehicle owner's phone number and other details. These details are stored on a server, and the system sends three reminders for payment within a fixed time window. If the fine is not paid, the vehicle's registration with the RTO (Regional Transport Office) is automatically canceled and it is no longer allowed on the road. This system aims to improve the efficiency and accuracy of the traffic fine collection process, while also reducing errors and the possibility of bribery. The use of machine learning techniques and video surveillance cameras allows for continuous monitoring of traffic, reducing the need for large numbers of traffic police to manually monitor the roads. The extraction of the QR code and vehicle owner's details also makes it easier to track and verify transactions, ensuring that the correct fine is being paid by the right individual. The automatic cancellation of the vehicle's registration, if the fine is not paid, is a deterrent for individuals attempting to evade paying their fines. In summary, our proposed system utilizes advanced technology to automate the traffic fine collection process, improving efficiency and accuracy while reducing the possibility of bribery and errors.y.

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