Automatic driver fatigue detection based on visual computing
In the context of the growing e-commerce sector, the complexity of supply chains has surged, placing heightened demands on drivers facing increased fatigue. This is particularly critical for timely deliveries of perishable goods and time-sensitive services. To address this challenge and enhance t...
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| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/174831 |
| _version_ | 1811695317964292096 |
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| author | Li, Wei |
| author2 | Chen Songlin |
| author_facet | Chen Songlin Li, Wei |
| author_sort | Li, Wei |
| collection | NTU |
| description | In the context of the growing e-commerce sector, the complexity of supply chains has
surged, placing heightened demands on drivers facing increased fatigue. This is
particularly critical for timely deliveries of perishable goods and time-sensitive
services. To address this challenge and enhance transportation efficiency while
mitigating road accidents, a driver fatigue detection system is essential.
This dissertation explores the integration of real-time monitoring of driving behavior,
utilizing RGB cameras to detect signs of fatigue such as eye closure, yawning, and
head position. The system issues warnings through an in-car display to prompt timely
driver response. Notably, test results demonstrate a robust 97.8% accuracy in
detecting eye closure. Future work could refine alert mechanisms to correct driver
behavior more efficiently and add add infrared cameras to the system for easy
detection in the dark, further optimizing the proposed fatigue detection system. |
| first_indexed | 2024-10-01T07:21:33Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/174831 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:21:33Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1748312024-04-20T16:54:53Z Automatic driver fatigue detection based on visual computing Li, Wei Chen Songlin Lyu Chen School of Mechanical and Aerospace Engineering lyuchen@ntu.edu.sg, Songlin@ntu.edu.sg Engineering In the context of the growing e-commerce sector, the complexity of supply chains has surged, placing heightened demands on drivers facing increased fatigue. This is particularly critical for timely deliveries of perishable goods and time-sensitive services. To address this challenge and enhance transportation efficiency while mitigating road accidents, a driver fatigue detection system is essential. This dissertation explores the integration of real-time monitoring of driving behavior, utilizing RGB cameras to detect signs of fatigue such as eye closure, yawning, and head position. The system issues warnings through an in-car display to prompt timely driver response. Notably, test results demonstrate a robust 97.8% accuracy in detecting eye closure. Future work could refine alert mechanisms to correct driver behavior more efficiently and add add infrared cameras to the system for easy detection in the dark, further optimizing the proposed fatigue detection system. Master's degree 2024-04-15T02:28:52Z 2024-04-15T02:28:52Z 2023 Thesis-Master by Coursework Li, W. (2023). Automatic driver fatigue detection based on visual computing. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/174831 https://hdl.handle.net/10356/174831 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Li, Wei Automatic driver fatigue detection based on visual computing |
| title | Automatic driver fatigue detection based on visual computing |
| title_full | Automatic driver fatigue detection based on visual computing |
| title_fullStr | Automatic driver fatigue detection based on visual computing |
| title_full_unstemmed | Automatic driver fatigue detection based on visual computing |
| title_short | Automatic driver fatigue detection based on visual computing |
| title_sort | automatic driver fatigue detection based on visual computing |
| topic | Engineering |
| url | https://hdl.handle.net/10356/174831 |
| work_keys_str_mv | AT liwei automaticdriverfatiguedetectionbasedonvisualcomputing |