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.
@article{arxiv.2504.14807,
title = {Real-Time Sleepiness Detection for Driver State Monitoring System},
author = {Deepak Ghimire and Sunghwan Jeong and Sunhong Yoon and Sanghyun Park and Juhwan Choi},
journal= {arXiv preprint arXiv:2504.14807},
year = {2025}
}