Results 171 to 178 of about 1,740 (178)

Drowsiness Detection for Safe Driving Using PERCLOS and YOLOv2 Method

2022
Ngo Kah Lock, Weng Mun Ng, Norrabiatul Adawiyah Jusoh, Nazhatul Hafizah Kamarudin, Rizauddin Ramli, Zuliani Zulkoffli   +5 more
openaire   +1 more source

Au-delà de la conduite : validation du PERCLOS comme mesure de la somnolence en IRMf de repos

Médecine du Sommeil
Ivan Igor Gaez, E. Attoh-Mensah, Clément Nathou, Jacques Brehelin, L. Brun, O. Etard   +5 more
semanticscholar   +1 more source

Streamlining Drowsiness Assessment: An In-Depth Review of ORD and PERCLOS Methods

Every year, drowsy and fatigued driving contributes to thousands of crashes and their resulting injuries and fatalities. Naturalistic driving data allows researchers an opportunity to better understand drowsy driving through review of driver-facing video capturing the driver’s behavior and eyes.
Soccolich, Susan A., Hammond, Rebecca, Camden, Matthew C., Walker, Stuart   +3 more
openaire   +1 more source

Vision based eye closeness classification for driver’s distraction and drowsiness using PERCLOS and support vector machines

Eleventh International Conference on Machine Vision (ICMV 2018), 2019
Zalhan Mohd Zin, Anis Hazirah Rodzi, Norazlin Ibrahim   +2 more
openaire   +1 more source

Fatigue Driving Detection System Based on PERCLOS

, 2012
Zhang Cuiqing, Han Cuiying, Yang Lijuan, Zheng Pei   +3 more
semanticscholar   +1 more source

PERCLOS

openaire   +1 more source

Enhancing Road Safety: PERCLOS Analysis of a Dozy Driver

Priyanga Gopalakrishnan, Aida Jones, Harini Kalidoss, Jesiya Arputham   +3 more
openaire   +1 more source

SISTEM DETEKSI KELELAHAN PADA PENGENDARA MOTOR MENGGUNAKAN METODE EYE ASPECT RATIO (EAR) DAN PERCENTAGE OF EYELID CLOSURE (PERCLOS)

Kelelahan merupakan salah satu faktor penyumbang kecelakaan pada sepeda motor terbanyak di Indonesia. Pengendara yang cenderung abai dalam memperhatikan kondisi fisik memungkinkan terjadinya kecelakaan lalu lintas. Penelitian ini mengajukan sistem deteksi kelelahan pada pengendara sepeda motor menggunakan metode eye aspect ratio (EAR) dan percentage of
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