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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| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/174831 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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