000			01834nam a22001937a 4500
003			OSt
005			20220107122843.0
008			180717b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aVineetha Vijayan (93517014) _914533
245			_aReal time detection of driver drowsiness based on representation learning using deep neural networks
300			_aMPhil CS 2017-2018
500			_aDrowsiness detection is a system that can detect a snoozing driver in order to prevent an accident. Drowsiness is one of the main issues in road accidents. The fatality rate due to drowsiness is higher. An accident involving driver drowsiness has a high fatality rate because the observation, acknowledgement and vehicle control abilities reduce sharply while falling asleep. The growing number of accident fatalities in world in recent years has become a problem of serious concern for the society, so accidents must be prevented before they happen and this thing lies with the driver. Accidents usually lead both economic as well as social loss to the society. If accidents are prevented we can save many lives and along with that the environment is also preserved. Preventing accidents caused by drowsiness requires a system for detecting sleepiness in a driver. This work proposes a deep neural architecture for learning effective features and detecting drowsiness for a given RGB input video of a driver. The architecture consists of three deep networks for attaining global robustness to background and environmental variations and learning local facial movements and head gestures important for reliable drowsiness detection.
502			_bMPhil CS _c2017-2018 _dINT _eDr. Elizabeth Sherly
650			_aDROWSINESS DETECTION _914534
650			_aDEEP NEURAL NETWORKS _914535
650			_aACCIDENTS _914536
942			_2ddc _cPR
999			_c6162 _d6162