Real time detection of driver drowsiness based on representation learning using deep neural networks (Record no. 6162)

MARC details
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fixed length control field 01834nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122843.0
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fixed length control field 180717b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Vineetha Vijayan (93517014)
9 (RLIN) 14533
245 ## - TITLE STATEMENT
Title Real time detection of driver drowsiness based on representation learning using deep neural networks
300 ## - PHYSICAL DESCRIPTION
Extent MPhil CS 2017-2018
500 ## - GENERAL NOTE
General note Drowsiness detection is a system that can detect a snoozing driver in order to prevent an accident.<br/>Drowsiness is one of the main issues in road accidents. The fatality rate due to drowsiness<br/>is higher. An accident involving driver drowsiness has a high fatality rate because the observation,<br/>acknowledgement and vehicle control abilities reduce sharply while falling asleep. The<br/>growing number of accident fatalities in world in recent years has become a problem of serious<br/>concern for the society, so accidents must be prevented before they happen and this thing lies<br/>with the driver. Accidents usually lead both economic as well as social loss to the society. If<br/>accidents are prevented we can save many lives and along with that the environment is also<br/>preserved. Preventing accidents caused by drowsiness requires a system for detecting sleepiness<br/>in a driver. This work proposes a deep neural architecture for learning effective features<br/>and detecting drowsiness for a given RGB input video of a driver. The architecture consists of<br/>three deep networks for attaining global robustness to background and environmental variations<br/>and learning local facial movements and head gestures important for reliable drowsiness<br/>detection.
502 ## - DISSERTATION NOTE
Degree type MPhil CS
Name of granting institution 2017-2018
Year degree granted INT
-- Dr. Elizabeth Sherly
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element DROWSINESS DETECTION
9 (RLIN) 14534
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Topical term or geographic name entry element DEEP NEURAL NETWORKS
9 (RLIN) 14535
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Topical term or geographic name entry element ACCIDENTS
9 (RLIN) 14536
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   17/07/2018   R-1444 17/07/2018 17/07/2018 Project Reports