@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Alarm,Artificial intelligence,Drowsiness detection,Human eye,Image processing},
 pages = {626-637},
 volume = {11431 LNAI},
 websites = {http://link.springer.com/10.1007/978-3-030-14799-0_54},
 id = {716964de-fc65-3e63-9580-79a9f57508c4},
 created = {2020-12-09T06:11:19.152Z},
 file_attached = {false},
 profile_id = {f4bee9c8-c485-33cf-b9d4-7424ce07c46d},
 last_modified = {2021-01-31T16:39:23.266Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2019, Springer Nature Switzerland AG. At a global level, drowsiness is one of the main causes of road accidents causing frequent deaths and economic losses. To solve this problem an application developed in Matlab environment was made, which processes real time acquired images in order to determine if the driver is awake or drowsy. Using AdaBoost training Algorithm for Viola-Jones eyes detection, a cascade classifier finds the location and the area of the driver eyes in each frame of the video. Once the driver eyes are detected, they are analyzed whether are open or closed by color segmentation and thresholding based on the sclera binarized area. Finally, it was implemented as a drowsiness detection system which aims to prevent driver fall asleep while driving a vehicle by activating an audible alert, reaching speeds up to 14.5 fps.},
 bibtype = {inbook},
 author = {Herrera-Granda, Erick P. and Caraguay-Procel, Jorge A. and Granda-Gudiño, Pedro D. and Herrera-Granda, Israel D. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H. and Revelo-Fuelagán, Javier},
 doi = {10.1007/978-3-030-14799-0_54},
 chapter = {Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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