Are You Paying Attention? Detecting Distracted Driving in Real-Time

Each year, millions of people lose their lives to fatal road accidents. An ever increasing proportion of these accidents is due to distracted driving caused by co-passengers and mobile devices. Thus, there is a growing need for a system which detects the distracted driver in real-time and raises a warning alarm. To this end, we present a simple and robust architecture using foreground extraction and a Convolutional Neural Network (CNN). Our ConvNet model has significantly fewer parameters (0.5M) than state-of-the-art models. Detailed experimental evaluation on two publicly available datasets, the State Farm Distracted Driver Detection dataset (SFD3) and the AUC Distracted Driver dataset (AUCD2), confirm that our model either outperforms or compares with the models proposed so far on both the datasets, with a test accuracy of 98.48% and 95.64%, respectively. Our experiments also suggest that incorporating additional features such as the posture of a driver through foreground extraction using GrabCut substantially improves the performance of our model. Furthermore, our ConvNet is capable of detecting real-time distractions without any parallel processing.