Real-Time System for Driver Fatigue Detection by RGB-D Camera

Abstract

Drowsy driving is one of the major causes of fatal traffic accidents. In this article, we propose a real-time system that utilizes RGB-D cameras to automatically detect driver fatigue and generate alerts to drivers. By introducing RGB-D cameras, the depth data can be obtained, which provides extra evidence to benefit the task of head detection and head pose estimation. In this system, two important visual cues (head pose and eye state) for driver fatigue detection are extracted and leveraged simultaneously. We first present a real-time 3D head pose estimation method by leveraging RGB and depth data. Then we introduce a novel method to predict eye states employing the WLBP feature, which is a powerful local image descriptor that is robust to noise and illumination variations. Finally, we integrate the results from both head pose and eye states to generate the overall conclusion. The combination and collaboration of the two types of visual cues can reduce the uncertainties and resolve the ambiguity that a single cue may induce. The experiments were performed using an inside-car environment during the day and night, and theyfully demonstrate the effectiveness and robustness of our system as well as the proposed methods of predicting head pose and eye states.