Fu Xianping, Guan Xiao, Peli Eli, Liu Hongbo, Luo Gang
Schepens Eye Research Institute, Harvard Medical School, Boston, MA 02114 USA. He is currently with the Information Science and Technology College, Dalian Maritime University, Dalian 116026, China (
Tulane University, New Orleans, LA 70118 USA.
IEEE trans Intell Transp Syst. 2012 Sep 21;14(1):303-310. doi: 10.1109/TITS.2012.2217377.
Gaze tracking is crucial for studying driver's attention, detecting fatigue, and improving driver assistance systems, but it is difficult in natural driving environments due to nonuniform and highly variable illumination and large head movements. Traditional calibrations that require subjects to follow calibrators are very cumbersome to be implemented in daily driving situations. A new automatic calibration method, based on a single camera for determining the head orientation and which utilizes the side mirrors, the rear-view mirror, the instrument board, and different zones in the windshield as calibration points, is presented in this paper. Supported by a self-learning algorithm, the system tracks the head and categorizes the head pose in 12 gaze zones based on facial features. The particle filter is used to estimate the head pose to obtain an accurate gaze zone by updating the calibration parameters. Experimental results show that, after several hours of driving, the automatic calibration method without driver's corporation can achieve the same accuracy as a manual calibration method. The mean error of estimated eye gazes was less than 5°in day and night driving.
注视跟踪对于研究驾驶员注意力、检测疲劳以及改进驾驶员辅助系统至关重要，但在自然驾驶环境中却颇具难度，这是因为光照不均匀且变化极大，同时头部运动幅度也很大。传统的校准方法要求受试者跟随校准器，在日常驾驶场景中实施起来非常麻烦。本文提出了一种新的自动校准方法，该方法基于单个摄像头来确定头部方向，并利用侧视镜、后视镜、仪表盘以及挡风玻璃上的不同区域作为校准点。在自学习算法的支持下，该系统跟踪头部，并根据面部特征将头部姿态分类到12个注视区域中。粒子滤波器用于估计头部姿态，通过更新校准参数来获得准确的注视区域。实验结果表明，经过数小时的驾驶后，无需驾驶员配合的自动校准方法能够达到与手动校准方法相同的精度。在白天和夜间驾驶中，估计眼睛注视的平均误差均小于5°。
