On board monitoring of the alertness level of an automotive driver has been a
challenging research in transportation safety and management. In this paper, we
propose a robust real time embedded platform to monitor the loss of attention
of the driver during day as well as night driving conditions. The PERcentage of
eye CLOSure (PERCLOS) has been used as the indicator of the alertness level. In
this approach, the face is detected using Haar like features and tracked using
a Kalman Filter. The Eyes are detected using Principal Component Analysis (PCA)
during day time and the block Local Binary Pattern (LBP) features during night.
Finally the eye state is classified as open or closed using Support Vector
Machines(SVM). In plane and off plane rotations of the drivers face have been
compensated using Affine and Perspective Transformation respectively.
Compensation in illumination variation is carried out using Bi Histogram
Equalization (BHE). The algorithm has been cross validated using brain signals
and finally been implemented on a Single Board Computer (SBC) having Intel Atom
processor, 1 GB RAM, 1.66 GHz clock, x86 architecture, Windows Embedded XP
operating system. The system is found to be robust under actual driving
conditions.

本文提出了一种基于计算机视觉和机器学习的实时嵌入式平台，通过检测驾驶员的 PERCLOS 指标实现对日夜驾驶状态下驾驶员注意力损失的监测，并使用支持向量机（SVM）对眼睛状态进行分类。实验结果表明该算法稳健可靠，并已在单板计算机上实现。