Real-Time Accident Detection and Notification System Using Convolutional Neural Networks

In today's scenario, ensuring road safety and quick accident response has become crucial. As the volume of traffic on the road increases, so do the number of accidents. Traditional surveillance systems rely on manual monitoring making them inefficient and slow, therefore the aim of building this smart surveillance system is to monitor the roads of India for anomalies, categorize them based and finally notify relevant authorities real-time. Anomalies on the road can be anything ranging from accidents, illegal parking, to hazardous obstructions so we need to classify them based on criticality to ensure proper response. A smart surveillance system that can automatically detect and classify road anomalies will improve response times, reduce human error, and ensure timely intervention during emergencies. The core functionality of the smart surveillance system is real-time anomaly detection. Using convolutional neural networks (CNN) we are able to recognize anomalies on the road. The anomalies can then be classified automatically using a pretrained classification model. We then plan to send out notifications to the concerned authorities so that they can take action. The alerts are sent to administrators of public local Closed-Circuit Television (CCTV) networks who then coordinate with emergency services to provide relief. In the future, the focus can be on enhancing the detection algorithm, adding predictive analytics, and integration with IoT devices for better incident tracking.