Drive-Net: Convolutional Network for Driver Distraction Detection
This addresses the problem of preventing motor vehicle accidents by detecting driver distraction, but it is incremental as it builds on existing machine learning approaches.
The paper tackled driver distraction detection by proposing Drive-Net, a method combining a CNN and random decision forest, which achieved 95% accuracy, outperforming other methods by 2% on a dataset of 22,425 annotated images.
To help prevent motor vehicle accidents, there has been significant interest in finding an automated method to recognize signs of driver distraction, such as talking to passengers, fixing hair and makeup, eating and drinking, and using a mobile phone. In this paper, we present an automated supervised learning method called Drive-Net for driver distraction detection. Drive-Net uses a combination of a convolutional neural network (CNN) and a random decision forest for classifying images of a driver. We compare the performance of our proposed Drive-Net to two other popular machine-learning approaches: a recurrent neural network (RNN), and a multi-layer perceptron (MLP). We test the methods on a publicly available database of images acquired under a controlled environment containing about 22425 images manually annotated by an expert. Results show that Drive-Net achieves a detection accuracy of 95%, which is 2% more than the best results obtained on the same database using other methods