Road Accident Detection Using Machine Learning

Abstract: Road accidents pose a significant threat to life and property, with an estimated 1.35 million people losing their lives annually, according to the World Health Organization (WHO). Prompt detection of accidents is crucial for implementing effective mitigation measures. To address this, a real-time accident detection system leveraging machine learning on video streams has been proposed. The system utilizes three convolutional neural network (CNN) models - ResNet50, VGG16, and a custom-built CNN - to extract complex features from CCTV footage frames. Training on a dataset containing labeled accident and non-accident frames from Kaggle enables the models to distinguish between the two. When given CCTV footage as input, frames are extracted, preprocessed, and fed to the models for prediction. The predictions from the three models are compared, and a final prediction is determined. This system aims to enhance emergency response and overall road safety by quickly detecting accidents, thereby reducing the toll on human lives and property. By integrating machine learning and video streams, it offers a promising solution to the issue of road accidents, with the potential to provide real-time accident detection and mitigate their impact.