AI-Powered Real-Time Accident Detection and Emergency Response System with Vehicle Forensic Analysis

Abstract

Road accidents remain one of the leading causes of injury and death worldwide, and delays in detecting and reporting them significantly increase the risk of severe outcomes. Conventional CCTV-based surveillance depends heavily on human operators, making continuous, error-free monitoring of multiple video feeds impractical. This paper presents an AI-Powered Real-Time Accident Detection and Emergency Response System with Vehicle Forensic Analysis that continuously monitors live CCTV streams using YOLOv11 for vehicle detection and DeepSORT for multi-object tracking to identify collisions automatically. Upon detecting an accident, the system assesses its severity, stores the event in a relational database, and instantly dispatches alerts through SMS, email, voice call, and a monitoring dashboard. A dedicated forensic module then reconstructs the incident by extracting collision frames, recognizing number plates through Optical Character Recognition (OCR), estimating vehicle speed, and retrieving owner records, culminating in an automatically generated digital forensic PDF report for use by police, insurers, and legal authorities. Experimental evaluation across varied traffic and lighting conditions confirms reliable accident detection, fast alert dispatch, and consistent forensic report generation, demonstrating the system's potential to shorten emergency response times and streamline post-accident investigation