Intelligent Traffic Violation Detection

Abstract

ITVD is a technique which uses Artificial Intelligence and deep learning concepts to detect the vehicle violating the traffic rules. We observe that in the past few years there has been a tremendous increase in the number of on road vehicles. The congested roads with pollution, thereby creating havoc which serves as a reason to violate the traffic rules. This in turn increases road accidents. ITVD is an algorithm which detects traffic violations such as jumping red signals, riding vehicles without helmets, driving without seat belts and vehicles stepping over the stop line during red signals. In many developing countries like India, traffic violations are monitored manually by the traffic department. Such systems make the law enforcement and traffic management difficult since it requires tracking of each vehicle without a miss. This necessitates an automated system which detects the traffic violations and abnormal events occurring on the roads. In this paper we propose the YOLOv3(You Only Look Once version3) algorithm to detect the traffic violations. This algorithm uses Convolutional Neural Networks (CNN) to detect an object and Darknet-53 as a feature extractor. The main advantage of using YOLOv3 is that it uses clustering analysis to cluster the input dataset to improve the prediction ability even with small vehicles.