Intelligent Traffic Violation Detection and Criminal Tracking

Abstract

Traffic congestion and increasing instances of traffic violations have become serious challenges for urban infrastructure and public safety. Traditional traffic management methods heavily rely on manual processes, which are often inefficient and unable to keep up with the growing number of vehicles. The need for automation and intelligent decision making in traffic enforcement is evident. This project, Intelligent Traffic Violation Detection and Criminal Tracking, aims to address these challenges by developing an AI-driven multilayered system. By integrating deep learning, computer vision, and cloud computing, the system will automate the detection of traffic violations and enable real-time tracking of criminal or wanted vehicles. Leveraging video surveillance and license plate recognition technologies, the system will provide accurate, scalable, and fast traffic violation verification while enhancing the capability to track offenders. Additionally, the project incorporates AWS cloud services to ensure efficient data processing and real-time alert delivery to law enforcement agencies. The proposed system will significantly reduce manual intervention, lower false positive rates, and improve the speed and accuracy of traffic law enforcement. Through this work, our goal is to contribute to safer roads, more efficient traffic management, and improved public trust in automated traffic enforcement technologies.