ACCIDENT DETECTION WITH CCTV USING CONVOLUTIONAL NEURAL NETWORK

Abstract : Accident detection is essential for ensuring timely emergency response, reducing fatalities, and minimizing property damage. This project presents an automated accident detection system that utilizes Closed- Circuit Television (CCTV) footage combined with Convolutional Neural Networks (CNNs) to identify accidents on roads in real time. CCTV cameras installed at key traffic locations continuously capture video footage, which is analyzed frame by frame using a CNN model trained on a comprehensive dataset of annotated accident and non-accident scenarios. The model learns to recognize patterns indicative of accidents, such as sudden changes in vehicle motion, anomalies in traffic flow, and the presence of emergency vehicles. Once trained, the system can accurately detect potential accidents from live footage and immediately trigger alerts to emergency services, providing vital details such as the location and nature of the incident. In addition to notifying authorities, the system can also be configured to send warnings to nearby vehicles and pedestrians, enabling them to take precautionary actions or reroute to avoid the affected area. This approach eliminates the dependency on human operators, who may miss critical events due to fatigue or limited attention, and ensures a faster, more reliable response. By integrating artificial intelligence with existing CCTV infrastructure, the system offers a scalable and cost-effective solution for improving road safety and traffic monitoring. Future enhancements may include incorporating additional sensor inputs—such as vehicle speed, traffic density, and weather conditions—to improve detection accuracy and enable predictive accident prevention. Overall, the proposed system demonstrates a practical and impactful application of deep learning in intelligent transportation systems.

Keyword Convolutional Neural Network; Accident Detection; Deep Learning; Video Classification; Recurrent Neural Network.