Real-Time Sleepiness Detection for Driver State Monitoring System
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866916699191640064 |
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| author | Ghimire, Deepak Jeong, Sunghwan Yoon, Sunhong Park, Sanghyun Choi, Juhwan |
| author_facet | Ghimire, Deepak Jeong, Sunghwan Yoon, Sunhong Park, Sanghyun Choi, Juhwan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time Sleepiness Detection for Driver State Monitoring System Ghimire, Deepak Jeong, Sunghwan Yoon, Sunhong Park, Sanghyun Choi, Juhwan Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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. |
| title | Real-Time Sleepiness Detection for Driver State Monitoring System |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2504.14807 |