CNN-Based Vehicle Rules Violation Detection System

Abstract

One of the most well-known components of the cutting-edge ai and machine learning sector that tracks numerous functional activities and their influence on the growth of modern smart cities worldwide is the video based intelligent transportation system v-its these days the high accident rate in a crowded metropolis is a major concern it is now crucial to address all of the causes of the significant increase in traffic accidents and the percentage of fatalities that are frequently attributed to breaking traffic laws. An enhanced V-ITS system for identifying and classifying cars, drivers, and their license plates while travelling on highways has been developed in order to overcome those difficulties. The technique is reliable for reducing the rate of violations because the upgraded V-ITS system can perform the aforementioned tasks with precision from real-time traffic footage. This real-time detection approach quickly recognizes any breaches of yellow or red traffic signals a pretrained Convolutional Neural Network (CNN) featuring a three-layer to improve the feasibility of the vita system the structure was developed subsequently a model founded on a Deep Belief Network (DBN) was designed and developed. The open cv yolov3 and other python tools as well as free internet video data sets are mostly used to complete the project the outcome of the real-time experiment of the v-its systems can applied to reduce vehicle violations ensuring of safety of both pedestrians and passengers