VigilEye -- Artificial Intelligence-based Real-time Driver Drowsiness Detection
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917701554798592 |
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| author | Sengar, Sandeep Singh Kumar, Aswin Singh, Owen |
| author_facet | Sengar, Sandeep Singh Kumar, Aswin Singh, Owen |
| contents | This study presents a novel driver drowsiness detection system that combines deep learning techniques with the OpenCV framework. The system utilises facial landmarks extracted from the driver's face as input to Convolutional Neural Networks trained to recognise drowsiness patterns. The integration of OpenCV enables real-time video processing, making the system suitable for practical implementation. Extensive experiments on a diverse dataset demonstrate high accuracy, sensitivity, and specificity in detecting drowsiness. The proposed system has the potential to enhance road safety by providing timely alerts to prevent accidents caused by driver fatigue. This research contributes to advancing real-time driver monitoring systems and has implications for automotive safety and intelligent transportation systems. The successful application of deep learning techniques in this context opens up new avenues for future research in driver monitoring and vehicle safety. The implementation code for the paper is available at https://github.com/LUFFY7001/Driver-s-Drowsiness-Detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15646 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VigilEye -- Artificial Intelligence-based Real-time Driver Drowsiness Detection Sengar, Sandeep Singh Kumar, Aswin Singh, Owen Computer Vision and Pattern Recognition This study presents a novel driver drowsiness detection system that combines deep learning techniques with the OpenCV framework. The system utilises facial landmarks extracted from the driver's face as input to Convolutional Neural Networks trained to recognise drowsiness patterns. The integration of OpenCV enables real-time video processing, making the system suitable for practical implementation. Extensive experiments on a diverse dataset demonstrate high accuracy, sensitivity, and specificity in detecting drowsiness. The proposed system has the potential to enhance road safety by providing timely alerts to prevent accidents caused by driver fatigue. This research contributes to advancing real-time driver monitoring systems and has implications for automotive safety and intelligent transportation systems. The successful application of deep learning techniques in this context opens up new avenues for future research in driver monitoring and vehicle safety. The implementation code for the paper is available at https://github.com/LUFFY7001/Driver-s-Drowsiness-Detection. |
| title | VigilEye -- Artificial Intelligence-based Real-time Driver Drowsiness Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.15646 |