A Vision-Based System for Monitoring the Loss of Attention in Automotive Drivers

Onboard monitoring of the alertness level of an automotive driver has been challenging to 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 and night driving conditions. The percentage of eye closure has been used to indicate the alertness level. In this approach, the face is detected using Haar-like features and is tracked using a Kalman filter. The eyes are detected using principal component analysis during daytime and using the block local-binary-pattern features during nighttime. Finally, the eye state is classified as open or closed using support vector machines. In-plane and off-plane rotations of the driver's face have been compensated using affine transformation and perspective transformation, respectively. Compensation in illumination variation is carried out using bihistogram equalization. The algorithm has been cross-validated using brain signals and, finally, has been implemented on a single-board computer that has an Intel Atom processor with a 1.66-GHz clock, a random access memory of 1 GB, $\times$86 architecture, and a Windows-embedded XP operating system. The system is found to be robust under actual driving conditions.