Real-Time Accident Detection and Automated Ambulance Rescue System via Video Surveillance and Deep Learning

The escalating global frequency of road traffic accidents necessitates the development of swift and reliable emergency response systems. The time elapsed between a collision and the arrival of medical assistance is the most critical factor influencing accident-related fatalities, often referred to as the "golden hour." This paper presents a comprehensive, infrastructure-based solution for real-time accident detection and automated ambulance rescue notification, leveraging the synergy between Computer Vision and advanced Deep Learning (DL) techniques for road surveillance. The system utilizes a novel Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM) hybrid model to process video surveillance footage frame-by-frame, enabling the highly accurate detection of dynamic accident events. After an accident is confirmed, the system automatically retrieves the camera's GPS coordinates and sends an emergency email alert to the nearest medical center. This removes manual reporting delays and enables faster response dispatch center. The integration of spatial-temporal analysis with a practical, robust communication method aims to significantly decrease the emergency response time, thereby enhancing the probability of saving lives in smart city environments.