Embedded Fatigue Detection Using Convolutional Neural Networks with Mobile Integration

Abstract

Fatigued or drowsy drivers pose a significant risk of causing life-threatening accidents. Yet, many sleep-deprived drivers are behind the wheels exposing lives to danger. In this paper, we propose a low-cost and real-time embedded system for fatigue detection using convolutional neural networks (CNN). Our system starts by spatially processing the video signal using a real-time face detection algorithm to establish a region of interest and reduce computations. The video signal comes from a camera module mounted on the car dashboard connected to an embedded Linux board set to monitor the driver's eyes. Detected faces are then passed to an optimized fatigue recognition CNN binary classifier to detect the event of fatigued or normal driving. When temporally persistent fatigue is detected, alerts are sent to the driver's smart phone, and to possibly others, for prevention measures to be taken before accidents happen. Our testing shows that the system can robustly detect fatigue and can effectively be deployed to address the problem.