摘要:
疲劳驾驶是造成交通事故的重要原因.通过机器视觉技术对眼睛动作和视线转移特征的分析可实现驾驶人疲劳状态的有效估计.然而,实际行车环境中光照条件的随机、快速变化以及驾驶人面部姿态的不确定性使得眼睛区域的鲁棒性定位变得异常困难.为此,本文引入基于点分布模型的主动形状模型（ASM）算法并针对其在实际行车环境中存在的问题提出了三点改进.首先,建立了基于自商图的局部纹理模型以克服光照变化的影响;其次,充分利用面部局部区域良好的聚类性,建立了层叠式全局形状模型,以适应驾驶人姿态的大角度偏转;再次,在行车过程中,通过对驾驶人面部形状的在线学习实现模型参数分布特征的获取,为ASM算法的配准提供了更加紧致的约束.实验结果显示,本文算法对光照和姿态变化具有较强的鲁棒性,在驾驶人面部器官不发生自遮挡的情况下可实现眼睛区域的高精度配准.
Abstract:
Driver＇s drowsiness is one of the major causes of road accidents.The monitoring of a given driver＇s eye state by the use of a camera is considered to be a promising way to detect driver＇s drowsiness due to its accuracy and non-intrusiveness.However,eye location remains a challenging vision problem because of the constantly changing of illumination and driver＇s pose.Active shape model（ASM） is introduced in this paper to align the face.Though the ASM is a powerful statistical tool,it can suffer from changes in illumination and posture.Three contributions are involved in this paper.First,in order to maximize the tolerance of the ASM algorithm to illumination changes,we propose a robust ASM method with a novel local texture model learned from the self-quotient image instead of the original image.Second,a double layer overall shape model is proposed to enhance the adaptability of ASM.Third, strong constraints are achieved by an on-line learning of the distribution characteristics of the model parameters.The results show that the proposed algorithm is robust to the variation of illumination and driver＇s pose.
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