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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15741114 |
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| _version_ | 1866901823164514304 |
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| author | V.Vijaya Bhasker, Srinivas Bachu, A.Veeresh, S.Anusha, P.Rani, K.Naga Pavansai |
| author_facet | V.Vijaya Bhasker, Srinivas Bachu, A.Veeresh, S.Anusha, P.Rani, K.Naga Pavansai |
| contents | <p>The proposed system is designed to enhance driver and road safety using computer vision techniques.<br>A color video captured within the vehicle is processed to detect the driver’s face. Once the face is<br>identified, eye regions are located and used as templates for continuous eye tracking in subsequent video<br>frames. These tracked eye images are then analyzed to detect signs of driver drowsiness, triggering<br>warning alerts when necessary. The system operates in three main phases: face detection, eye detection,<br>and drowsiness detection. Image processing plays a crucial role by recognizing the driver's face and<br>extracting the eye regions for further analysis. The Haar Cascade face detection algorithm is utilized to<br>process video frames, producing the driver’s face as the output. This approach offers a cost-effective and<br>efficient solution to mitigate accidents caused by driver drowsiness, thereby improving overall<br>transportation safety. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15741114 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Driver Drowsiness Detection System Using Raspberry Pi V.Vijaya Bhasker, Srinivas Bachu, A.Veeresh, S.Anusha, P.Rani, K.Naga Pavansai <p>The proposed system is designed to enhance driver and road safety using computer vision techniques.<br>A color video captured within the vehicle is processed to detect the driver’s face. Once the face is<br>identified, eye regions are located and used as templates for continuous eye tracking in subsequent video<br>frames. These tracked eye images are then analyzed to detect signs of driver drowsiness, triggering<br>warning alerts when necessary. The system operates in three main phases: face detection, eye detection,<br>and drowsiness detection. Image processing plays a crucial role by recognizing the driver's face and<br>extracting the eye regions for further analysis. The Haar Cascade face detection algorithm is utilized to<br>process video frames, producing the driver’s face as the output. This approach offers a cost-effective and<br>efficient solution to mitigate accidents caused by driver drowsiness, thereby improving overall<br>transportation safety. </p> |
| title | Driver Drowsiness Detection System Using Raspberry Pi |
| url | https://doi.org/10.5281/zenodo.15741114 |