Title :
Detecting driver drowsiness using computer vision techniques
Author :
Vural, Esra ; Çetin, Müjdat ; Erçil, Aytül ; Littlewort, Gwen ; Bartlett, Marian ; Movellan, Javier
Author_Institution :
Muhendislik ve Doga Bilimleri Fak.,, Sabanci Univ., Istanbul
Abstract :
The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Here we employ machine learning techniques to detect driver drowsiness. The system obtained 98% performance in predicting driver drowsiness. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy driving.
Keywords :
automated highways; computer vision; learning (artificial intelligence); traffic engineering computing; computer vision; drowsy driver detection system; intelligent vehicle system; machine learning; Computer vision; Electroencephalography; Humans; Information analysis; Intelligent vehicles; Machine learning; Vehicle detection;
Conference_Titel :
Signal Processing, Communication and Applications Conference, 2008. SIU 2008. IEEE 16th
Conference_Location :
Aydin
Print_ISBN :
978-1-4244-1998-2
Electronic_ISBN :
978-1-4244-1999-9
DOI :
10.1109/SIU.2008.4632673
