Real-Time Sleepiness Detection for Driver State Monitoring System
This addresses driver fatigue monitoring to prevent accidents, but it is incremental as it builds on existing computer vision techniques.
The paper tackled real-time detection of driver sleepiness by developing a system that tracks eye positions and classifies them as open or closed using a support vector machine with HOG features, achieving real-time performance to trigger alarms when eyes remain closed for a set duration.
A driver face monitoring system can detect driver fatigue, which is a significant factor in many accidents, using computer vision techniques. In this paper, we present a real-time technique for driver eye state detection. First, the face is detected, and the eyes are located within the face region for tracking. A normalized cross-correlation-based online dynamic template matching technique, combined with Kalman filter tracking, is proposed to track the detected eye positions in subsequent image frames. A support vector machine with histogram of oriented gradients (HOG) features is used to classify the state of the eyes as open or closed. If the eyes remain closed for a specified period, the driver is considered to be asleep, and an alarm is triggered.