Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State

• Published in: IEEE transactions on intelligent transportation systems Vol. 18; no. 3; pp. 545 - 557
• Main Authors: Mandal, Bappaditya, Liyuan Li, Wang, Gang Sam, Jie Lin
• Format: Journal Article
• Language: English
• Published: IEEE 01-03-2017
• Subjects: Cameras; Driver monitoring; Estimation; Face; Fatigue; fatigue detection; fusion; machine learning; Monitoring; percentage of eyelid closure (PERCLOS); spectral regression; Vehicles; video analytics
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2016.2582900

Driver's fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles (such as buses and heavy trucks) due to prolonged driving periods and boredom in working conditions. In this paper, we propose a vision-based fatigue detection system for bus driver monitoring, which is easy and flexible for deployment in buses and large vehicles. The system consists of modules of head-shoulder detection, face detection, eye detection, eye openness estimation, fusion, drowsiness measure percentage of eyelid closure (PERCLOS) estimation, and fatigue level classification. The core innovative techniques are as follows: 1) an approach to estimate the continuous level of eye openness based on spectral regression; and 2) a fusion algorithm to estimate the eye state based on adaptive integration on the multimodel detections of both eyes. A robust measure of PERCLOS on the continuous level of eye openness is defined, and the driver states are classified on it. In experiments, systematic evaluations and analysis of proposed algorithms, as well as comparison with ground truth on PERCLOS measurements, are performed. The experimental results show the advantages of the system on accuracy and robustness for the challenging situations when a camera of an oblique viewing angle to the driver's face is used for driving state monitoring.