Machine learning systems for detecting driver drowsiness
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Vural, Esra and Çetin, Müjdat and Erçil, Aytül and Littlewort, Gwen and Bartlett, Marian and Movellan, Javier (2007) Machine learning systems for detecting driver drowsiness. In: 3rd Biennial Workshop on Digital Signal Processing for Mobile and Vehicular Systems, Istanbul, Turkey
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. Previous approaches to drowsiness detection primarily make pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to data-mine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learningbased classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. 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.
|Item Type:||Papers in Conference Proceedings|
|Uncontrolled Keywords:||Driver fatigue; Drowsiness; Machine learning; Facial expressions; Facial action unit; Head movements; Multinomial logistic regression; Support vector machine (SVM); Driver behavior|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Deposited By:||Müjdat Çetin|
|Deposited On:||30 Oct 2007 01:13|
|Last Modified:||22 May 2019 12:08|
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