An Automated System for Accident Detection Using OpenCV

Abstract

Road accidents are one of the leading causes of fatalities and severe injuries worldwide, creating an urgent need for quick and accurate detection methods to reduce emergency response time. Traditional accident reporting systems rely heavily on human intervention, such as eyewitnessaccounts or delayed manual reports, which often lead to a lag in response time. It presents an automated accident detection system that uses OpenCV, a powerful computer vision library, to monitor and analyze real-time video feeds from traffic surveillance cameras, identifying accidents themomenttheyoccur. Utilizingimageprocessing techniques like edge detection, object tracking, and motion analysis, the system analyzes vehicular movement on the road. Sudden changes, such as abrupt stops or irregular vehicle behavior, are detected in real-time to identify potential accidents. OpenCV’s capabilities allow the system to adapt to various environmental conditions, including differing lighting, weather, and traffic densities.

Introduction

Road accidents cause millions of injuries and deaths annually, particularly in low- and middle-income countries. Despite improvements in infrastructure and vehicle safety, accident rates remain high, partly due to delays in detection and emergency response. Current systems rely on manual reporting or in-vehicle sensors, which have limitations such as delayed notifications, limited coverage, and high false positives.

This study proposes an automated, real-time accident detection system using OpenCV, an open-source computer vision library, to process live video feeds from traffic surveillance cameras. The system aims to detect accidents by analyzing anomalies in vehicle movement, improving response times and road safety without requiring specialized vehicle hardware.

A literature survey reviews various approaches employing deep learning models (CNNs, RNNs), machine learning algorithms, and video processing techniques for accident detection, highlighting advances and challenges such as environmental factors and scalability.

The system focuses on detecting common accident types—rear-end, T-bone, and front-impact collisions—using two main data sources: elevated traffic cameras providing comprehensive road views and dashcam videos offering close-up perspectives.

The proposed modular design includes data collection, preprocessing (frame extraction, background subtraction, data augmentation), model development (training CNNs and RNNs), and real-time detection with automated alerts to emergency services. Hardware and software requirements are specified, emphasizing scalability, cost-effectiveness, and adaptability to diverse conditions.

This automated approach addresses the shortcomings of existing systems by offering continuous monitoring, reducing false positives, and enabling timely emergency response to enhance public road safety.

Conclusion

The Automated System for Accident Detection Using OpenCV represents a significant advancement in road safety and emergency response capabilities. By leveraging real-time video analysis and advanced computer vision techniques, this system effectively identifies accidents as they occur, ensuring prompt notification of emergency services. Theintegration of machine learning models enhances the system\'s accuracy, allowing it to distinguish between normal traffic behaviors and potential accident scenarios.

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Copyright

Copyright © 2025 Nishanth Melvin L, Pradeep Kumar V, Pratheesh A, Dinesh Kumar, Dr. J. Jayaprakash, Mrs. S. Chinchu Nair. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.