Real-Time Video Analysis for Intelligent Traffic Management, Public Safety, and Security: A Deep Learning Approach

Abstract

This paper proposes a novel centralized traffic management system designed to enhance road safety and streamline law enforcement. The system leverages a network of intelligent sensors and cameras to achieve real-time accident detection, traffic violation identification, and facial recognition. Data collected from these sensors is fed into a centralized hub for processing and analysis. Upon identifying an incident, the system promptly dispatches notifications to patrolling units, enabling faster response times for accidents and violations. Additionally, facial recognition capabilities allow for real-time identification of individuals, potentially aiding in apprehending criminals or locating missing persons. This paper will discuss the system architecture, core functionalities, and potential benefits for improved traffic management and public safety. We will also address the challenges associated with data privacy and security in such a centralized system.