Accident Detection and Alert System

Road accidents constitute one of the leading causes of death and injuries in the global scene, and hence the requirement for developing efficient and real-time accident detection systems is great. This paper considers a vehicle accident detection system using computer vision techniques powered by OpenCV. Such a system utilises live video feeds that are acquired through surveillance cameras for monitoring vehicular movement with regard to accident detection due to sudden changes in velocity, direction, or collision events. It introduces features, including motion tracking, object detection with pre-trained deep learning models, and real-time algorithms for anomaly detection. Frame-by-frame analysis is also introduced where rapid deceleration and sudden stops can be identified in the system before a collision is detected. Also, optical flow and contour detection are utilized to identify accident-prone behaviour of traffic patterns. When an accident is established, the system will notify the emergency services; hence, response times will be reduced and lives will be saved. The results of experiments carried out in simulated scenarios prove the accuracy of the proposed system in accident detection with minimal false positives. With this research, a scalable solution comes into the forefront that can be introduced to infrastructures of smart cities to help improve the safety of traffic systems and implement proactive mechanisms for response to emergencies.