1]. QiangJi, Xiaojie Yang, "Real-time eye, gaze, and face pose tracking for monitoring driver vigilance", Journal of Real-Time Imaging.
This paper proposes a real-time prototype computer vision system for monitoring driver vigilance. The main component include a remotely located video CCD camera, a specially designed hardware system for real-time image acquisition and for controlling the illuminator and the alarm system, and various computer vision algorithms for simultaneously, real-time and non-intrusively monitoring various visual bio-behaviors that typically characterize a driver's level of vigilance. The visual behaviors include eyelid movement, face orientation, and gaze movement (pupil movement). The system was implemented in an environment with subjecting to different ethnic backgrounds, different genders, ages, with/without glasses, and under different illumination conditions, and it was found very robust, reliable and accurate.
2]. Guang-Yuan Zhang, Bo Cheng, Rui-JiaFeng, Jia-Wen Li “Real-time driver eye detection method using Support Vector Machine with Hu invariant moments”, International Conference on Machine Learning and Cybernetics.
In the making of advanced vehicle safety systems, monitoring the driver vigilance level and issuing an alert when he is not paying adequate attention to the road is a promising way to prevent or avoid the road accidents. In such a system, developing a reliable real-time driver eye detection method is a crucial part. A real-time eye detection method using support vector machine (SVM) with Hu invariant moments is proposed here. The test sets from the experiment were used to validate the classification results. The validation results and conclusions about the performance of the method were presented in this paper.
3]. Fabian Friedrichs and Bin Yang, “Camera based Drowsiness Reference for Driver State Classification under Real Driving Conditions”, 2010 IEEE Intelligent Vehicles Symposium.
To develop warning systems that detect reduced vigilance based on the behavior of driving, a reliable and accurate drowsiness reference is necessary. Studies show that measures of the driver's eyes are capable of detecting drowsiness under simulator or experimentational conditions. Here, the performance of the latest eye tracking based invehicle fatigue prediction measures are evaluated. These measures are assessed statistically and by a classification method based on a large dataset of 90 hours of real road drives. The results show that eye-tracking based drowsiness detection works well for some drivers as long as the blinks detection works properly.
4]. M. Wang, H. P. Chou, C. F. Hsu, S. W. Chen, and C. S. Fuh, “Extracting Driver’s Facial Features During Driving ”, 2011 14th International IEEE Conference on Intelligent Transportation Systems Washington, DC, USA.
A vision system for monitoring driver's facial features is discussed here. To begin with, the driver's face is located in the input video sequence. Then it is tracked over the subsequent images. The facial features of eyes, mouth and head are kept detecting in the course of face tracking. Feature detection and tracking are performed in parallel, so that the precise can be improved.
5]. Momin and Parag P. Abhyankar “Current Status and Future Research Directions in Monitoring Vigilance of Individual or Mass Audience in Monotonous Working Environment” International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012
Working in monotonous environment often causes lack of concentration or fatigue in an operator and many times such non-vigilance leads to accidents. That is why, early detection of fatigued state has become crucial in monotonous working environments like driving vehicle, operating machines etc. Such fatigued state often gets developed gradually and can be identified by certain symptoms. Different types of symptoms help in measuring non-vigilance in different ways.