Mr. Jaeik Jo Profile

Jaeik Jo

at Yonsei Univ

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SPIE Journal Paper | 1 December 2011

Vision-based method for detecting driver drowsiness and distraction in driver monitoring system

Jaeik Jo, Sung Joo Lee, Jaihie Kim, Ho Gi Jung, Kang Ryoung Park

OE, Vol. 50, Issue 12, 127202, (December 2011) https://doi.org/10.1117/12.10.1117/1.3657506

KEYWORDS: Eye, Video, Principal component analysis, Head, Glasses, Databases, Eyeglasses, Feature extraction, Optical engineering, Fiber optic illuminators

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Most driver-monitoring systems have attempted to detect either driver drowsiness or distraction, although both factors should be considered for accident prevention. Therefore, we propose a new driver-monitoring method considering both factors. We make the following contributions. First, if the driver is looking ahead, drowsiness detection is performed; otherwise, distraction detection is performed. Thus, the computational cost and eye-detection error can be reduced. Second, we propose a new eye-detection algorithm that combines adaptive boosting, adaptive template matching, and blob detection with eye validation, thereby reducing the eye-detection error and processing time significantly, which is hardly achievable using a single method. Third, to enhance eye-detection accuracy, eye validation is applied after initial eye detection, using a support vector machine based on appearance features obtained by principal component analysis (PCA) and linear discriminant analysis (LDA). Fourth, we propose a novel eye state-detection algorithm that combines appearance features obtained using PCA and LDA, with statistical features such as the sparseness and kurtosis of the histogram from the horizontal edge image of the eye. Experimental results showed that the detection accuracies of the eye region and eye states were 99 and 97%, respectively. Both driver drowsiness and distraction were detected with a success rate of 98%.

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