Paper Title
AN EFFICIENT APPROACH FOR ACCIDENT DETECTION USING CNN

Abstract
Road accident is one of the major killers of human life across the globe, and detection over time can minimize loss in human life and properties. In this project, an innovative approach toward accident detection using Convolutional Neural Networks has been presented. By adopting deep learning techniques from advanced systems, it analyzes real-time video feeds acquired from surveillance cameras to classify frames accordingly for identifying traffic accidents effectively. The proposed model is trained with a wide variety of traffic scenarios, including accidents and sparse conditions, to increase the sensitivity of the model to distinguishing accident events from normal traffic conditions. Designing the architecture of CNN automatically learns space-critical features such as vehicle motion, collisions, and obstructions on roads to ensure robust and reliable detection of performance. The system is designed to be accurate in the real-time operation so it becomes deployable in both the urban traffic network and highways. In case of an accident, the system can send immediate alerts to emergency services, thereby reducing response times and probably saving lives. This project well proves the applicability and effectiveness of CNNs in traffic surveillance as a low-cost and scalable solution that increases road safety by intelligent monitoring. Keywords— Detection of Accidents, Convolutional Neural Network, Real Time Processing, Traffic Surveillance, Deep Learning, Emergency Response.