Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices
Drowsy driving contributes to over 6000 fatal incidents annually in the US, underscoring the need for effective, non-intrusive drowsiness detection. This study seeks to address detection challenges, particularly in non-standard head positions. Our innovative approach leverages computer vision by com...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/638 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589279624167424 |
|---|---|
| author | Valerius Owen Nico Surantha |
| author_facet | Valerius Owen Nico Surantha |
| author_sort | Valerius Owen |
| collection | DOAJ |
| description | Drowsy driving contributes to over 6000 fatal incidents annually in the US, underscoring the need for effective, non-intrusive drowsiness detection. This study seeks to address detection challenges, particularly in non-standard head positions. Our innovative approach leverages computer vision by combining facial feature detection using Dlib, head pose estimation with the HOPEnet model, and analyses of the percentage of eyelid closure over time (PERCLOS) and the percentage of mouth opening over time (POM). These are integrated with traditional machine learning models, such as Support Vector Machines, Random Forests, and XGBoost. These models were chosen for their ability to process detailed information from facial landmarks, head poses, PERCLOS, and POM. They achieved a high overall accuracy of 86.848% in detecting drowsiness, with a small overall model size of 5.05 MB and increased computational efficiency. The models were trained on the National Tsing Hua University Driver Drowsiness Detection Dataset, making them highly suitable for devices with a limited computational capacity. Compared to the baseline model from the literature, which achieved an accuracy of 84.82% and a larger overall model size of 37.82 MB, the method proposed in this research shows a notable improvement in the efficiency of the model with relatively similar accuracy. These findings provide a framework for future studies, potentially improving sleepiness detection systems and ultimately saving lives by enhancing road safety. |
| format | Article |
| id | doaj-art-a1882aa79381469d8175945b2939674b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a1882aa79381469d8175945b2939674b2025-01-24T13:20:13ZengMDPI AGApplied Sciences2076-34172025-01-0115263810.3390/app15020638Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing DevicesValerius Owen0Nico Surantha1Computer Science Department, BINUS Graduate Program—Master of Computer Science, Bina Nusantara University, Jakarta 11480, IndonesiaComputer Science Department, BINUS Graduate Program—Master of Computer Science, Bina Nusantara University, Jakarta 11480, IndonesiaDrowsy driving contributes to over 6000 fatal incidents annually in the US, underscoring the need for effective, non-intrusive drowsiness detection. This study seeks to address detection challenges, particularly in non-standard head positions. Our innovative approach leverages computer vision by combining facial feature detection using Dlib, head pose estimation with the HOPEnet model, and analyses of the percentage of eyelid closure over time (PERCLOS) and the percentage of mouth opening over time (POM). These are integrated with traditional machine learning models, such as Support Vector Machines, Random Forests, and XGBoost. These models were chosen for their ability to process detailed information from facial landmarks, head poses, PERCLOS, and POM. They achieved a high overall accuracy of 86.848% in detecting drowsiness, with a small overall model size of 5.05 MB and increased computational efficiency. The models were trained on the National Tsing Hua University Driver Drowsiness Detection Dataset, making them highly suitable for devices with a limited computational capacity. Compared to the baseline model from the literature, which achieved an accuracy of 84.82% and a larger overall model size of 37.82 MB, the method proposed in this research shows a notable improvement in the efficiency of the model with relatively similar accuracy. These findings provide a framework for future studies, potentially improving sleepiness detection systems and ultimately saving lives by enhancing road safety.https://www.mdpi.com/2076-3417/15/2/638computer visiondrowsy drivingPERCLOSPOMhead poseDlib |
| spellingShingle | Valerius Owen Nico Surantha Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices Applied Sciences computer vision drowsy driving PERCLOS POM head pose Dlib |
| title | Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices |
| title_full | Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices |
| title_fullStr | Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices |
| title_full_unstemmed | Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices |
| title_short | Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices |
| title_sort | computer vision based drowsiness detection using handcrafted feature extraction for edge computing devices |
| topic | computer vision drowsy driving PERCLOS POM head pose Dlib |
| url | https://www.mdpi.com/2076-3417/15/2/638 |
| work_keys_str_mv | AT valeriusowen computervisionbaseddrowsinessdetectionusinghandcraftedfeatureextractionforedgecomputingdevices AT nicosurantha computervisionbaseddrowsinessdetectionusinghandcraftedfeatureextractionforedgecomputingdevices |