Real-Time Sleepiness Detection for Driver State Monitoring System

Deepak Ghimire, Sunghwan Jeong, Sunhong Yoon, Sanghyun Park, Juhwan Choi
4/21/2025
cs.CVcs.HCcs.LG

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Abstract

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.

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Cite this paper

@article{ghimire2025realtime,
  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},
  year   = {2025},
  eprint = {2504.14807},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.14807v1}
}

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