This document describes a proposed traffic violation detection system using image processing and a neural network model. The system uses CCTV footage from roads to detect vehicles and identify three types of violations: running a red light, parking in a no parking zone, and driving in the wrong direction. It processes frames from video feeds to detect vehicles using background subtraction, thresholding, and contour detection. A neural network classifies detected objects as cars, motorbikes, or non-vehicles. The system analyzes vehicle positions over time to identify violations. A database stores vehicle and violation data. A graphical user interface allows monitoring footage and violations. The system aims to help enforce traffic safety more efficiently than human patrols alone.