DocumentCode :
2698206
Title :
Efficient eye states detection in real-time for drowsy driving monitoring system
Author :
Xu, Cui ; Zheng, Ying ; Wang, Zengfu
Author_Institution :
Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei
fYear :
2008
fDate :
20-23 June 2008
Firstpage :
170
Lastpage :
174
Abstract :
In this paper, we propose a reliable method of eye states detection for drowsy driving monitoring. Given a restricted local block of eye regions, the Local Binary Pattern (LBP) histogram of the block is extracted and each bin of the histogram is treated as a feature of the eye. An AdaBoost based cascaded classifier is then trained to select the significant features from the large feature sets and classify the eye states as open or closed. According to the states of the eye, the PERCLOS score is measured in real time to decide whether the driver is at drowsy state or not. Experimental results demonstrate that our eye states detection algorithm can give an average eye states detection rate of over 98% under different illuminations, face orientations and subjects. This reveals that our method can work reasonably well for the purpose of driver awareness.
Keywords :
feature extraction; monitoring; object detection; traffic engineering computing; video signal processing; AdaBoost; PERCLOS score; cascaded classifier; drowsy driving monitoring system; eye states detection; local binary pattern histogram; Automation; Computerized monitoring; Face detection; Fatigue; Feature extraction; Histograms; Infrared detectors; Iris; Lighting; Real time systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation, 2008. ICIA 2008. International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-2183-1
Electronic_ISBN :
978-1-4244-2184-8
Type :
conf
DOI :
10.1109/ICINFA.2008.4607990
Filename :
4607990
Link To Document :
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