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

TL;DR

This paper presents a real-time, vision-based system for monitoring driver attention loss using eye closure detection and various image processing techniques, validated on an embedded platform for enhanced transportation safety.

Contribution

The paper introduces a robust, real-time embedded system for driver attention monitoring that combines face detection, eye tracking, and classification under various conditions.

Findings

System operates in day and night conditions

Achieves accurate eye closure detection

Validated on embedded hardware with robust performance

Abstract

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.