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Real-Time Detection of Early Drowsin...

Tran, Chinh.

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Real-Time Detection of Early Drowsiness Using Convolutional Neural Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Real-Time Detection of Early Drowsiness Using Convolutional Neural Networks./
作者:	Tran, Chinh.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	47 p.
附註:	Source: Masters Abstracts International, Volume: 82-04.
Contained By:	Masters Abstracts International82-04.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28086825
ISBN:	9798678178053

Real-Time Detection of Early Drowsiness Using Convolutional Neural Networks.
Tran, Chinh.

Real-Time Detection of Early Drowsiness Using Convolutional Neural Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 47 p.

Source: Masters Abstracts International, Volume: 82-04.

Thesis (M.S.E.)--The Catholic University of America, 2020.

This item must not be sold to any third party vendors.

Drowsy driving is one of the major problems in the United States. The number of accidents caused by drowsy driving amounts to over 6000 fatal crashes each year. This project intends to propose algorithms to detect early driving drowsiness, which can help drivers to have enough time to handle sleepiness. The basis of the proposed approach is to use the amount of eye closure, yawning, eye blinking to classify the level of drowsiness. After successful classification, there is a sound alarm to awake the user to prevent accidents. We use convolutional neural networks and facial landmarks to select best model for this classification. Experimental results are provided to validate the proposed method.

ISBN: 9798678178053Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Convolution neural network

Real-Time Detection of Early Drowsiness Using Convolutional Neural Networks.
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520 $a Drowsy driving is one of the major problems in the United States. The number of accidents caused by drowsy driving amounts to over 6000 fatal crashes each year. This project intends to propose algorithms to detect early driving drowsiness, which can help drivers to have enough time to handle sleepiness. The basis of the proposed approach is to use the amount of eye closure, yawning, eye blinking to classify the level of drowsiness. After successful classification, there is a sound alarm to awake the user to prevent accidents. We use convolutional neural networks and facial landmarks to select best model for this classification. Experimental results are provided to validate the proposed method.
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