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Suresh Babu, Ashwin (2023) Detecting Drowsiness in Drivers using Approaches based on Machine Learning Methodologies. Masters thesis, Dublin, National College of Ireland.
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Abstract
One of the leading causes of accidents among drivers is drowsiness and fatigue. It is increasing year by year in uninformative manner. There have been many researchers conducted on this topic and one of the prominent conclusions out of those are that a 24 hour sleep deprivation can cause the same amount of effect as a person who has an blood alcohol level of 0.10% which is well above the legal limit. Some of the previous researches on the topic of driver drowsiness detection has been focused mainly on lane deviation detection. In this research we have discussed about a method where we will be using TensorFlow to train images of different facets of a human face and the actions to make the algorithm learn how to detect drowsiness, some of the algorithms used are Deep Learning model, and the machine learning model used is Logistic Regression and Support Vector machine. In this research we have divided the dataset into four main categories namely open eyes, closed eyes, yawn and no yawn. Using these 4 facets we will train the machine learning algorithms to predict the drowsiness of the driver.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email -, - UNSPECIFIED |
| Uncontrolled Keywords: | Fatigue; Drowsiness; TensorFlow; Machine Learning; Logistic Regression; Support Vector Machine; MaxPOoling2D; Conv2D |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Tamara Malone |
| Date Deposited: | 27 May 2023 10:37 |
| Last Modified: | 27 May 2023 10:37 |
| URI: | https://norma.ncirl.ie/id/eprint/6672 |
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