A visual approach for driver inattention detection

Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes ope...

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Published inPattern recognition Vol. 40; no. 8; pp. 2341 - 2355
Main Authors D’Orazio, T., Leo, M., Guaragnella, C., Distante, A.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.08.2007
Elsevier Science
Subjects
Online AccessGet full text
ISSN0031-3203
1873-5142
DOI10.1016/j.patcog.2007.01.018

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Abstract Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes open or closed, and then it has to evaluate the temporal occurrence of eyes open to estimate the driver's visual attention level. In this paper we propose a visual approach that solves both problems. A neural classifier is applied to recognize the eyes in the image, selecting two candidate regions that might contain the eyes by using iris geometrical information and symmetry. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Several experiments were carried out on images of subjects with different eye colors, some of them wearing glasses, in different light conditions. Tests show robustness with respect to situations such as eyes partially occluded, head rotation and so on. In particular, when applied to images where people have eyes closed the proposed algorithm correctly reveals the absence of eyes. Next, the analysis of the eye occurrence in image sequences is carried out with a probabilistic model to recognize anomalous behaviors such as driver inattention or sleepiness. Image sequences acquired in the laboratory and while people were driving a car were used to test the driver behavior analysis and demonstrate the effectiveness of the whole approach.
AbstractList Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes open or closed, and then it has to evaluate the temporal occurrence of eyes open to estimate the driver's visual attention level. In this paper we propose a visual approach that solves both problems. A neural classifier is applied to recognize the eyes in the image, selecting two candidate regions that might contain the eyes by using iris geometrical information and symmetry. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Several experiments were carried out on images of subjects with different eye colors, some of them wearing glasses, in different light conditions. Tests show robustness with respect to situations such as eyes partially occluded, head rotation and so on. In particular, when applied to images where people have eyes closed the proposed algorithm correctly reveals the absence of eyes. Next, the analysis of the eye occurrence in image sequences is carried out with a probabilistic model to recognize anomalous behaviors such as driver inattention or sleepiness. Image sequences acquired in the laboratory and while people were driving a car were used to test the driver behavior analysis and demonstrate the effectiveness of the whole approach.
Author Leo, M.
D’Orazio, T.
Guaragnella, C.
Distante, A.
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Issue 8
Keywords Mixture Gaussian model
Behavior analysis
Neural network
Iris detection
Performance evaluation
Automatic classification
Probabilistic approach
Image processing
Mixture theory
Motor vehicle
Algorithm
Signal classification
Color image
Image segmentation
Gaussian process
Iris(eye)
Image sequence
Automatic system
Robustness
Visual attention
Monitoring
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Snippet Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver...
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crossref
elsevier
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SubjectTerms Applied sciences
Artificial intelligence
Behavior analysis
Computer science; control theory; systems
Connectionism. Neural networks
Exact sciences and technology
Image processing
Information, signal and communications theory
Iris detection
Mixture Gaussian model
Neural network
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Title A visual approach for driver inattention detection
URI https://dx.doi.org/10.1016/j.patcog.2007.01.018
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