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Research on Driver Fatigue Detection Based on Multi-feature Integration

Author: HeZuoWen
Tutor: ChenAiBin
School: Central South University of Forestry Science and Technology
Course: Applied Computer Technology
Keywords: Face Detection Multi - feature extraction BP neural network Fatigue detection
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 58
Quote: 0
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Abstract


With the social and economic development , the increasing motor vehicle , followed by personal security more and more the world's attention . Driver fatigue has become one of the major factors that lead to accidents . Therefore , many countries have actively carried out research on driver fatigue , especially in the Western developed countries , more comprehensive research in this regard . Driver fatigue detection is crucial in today 's society . In this paper, the design of the driver fatigue detection system is divided into four parts : face detection , face tracking , eye and mouth feature extraction and driver fatigue state recognition . Face Detection has a very important role in driver fatigue detection system , This paper the fusion color detection and principal component analysis , a new face detection method . First skin color detection for face candidate region positioning and thus PCA method for face detection . Experiments show that the method greatly reduces the number of face - like region , to improve the efficiency and accuracy of face detection , and better performance under different lighting conditions . Face tracking process using the Kalman filter to track the face , and further enhance the speed and positioning accuracy of the detection . Use of segmentation on the basis of the positioning the mouth of the mouth area detection and localization , using a priori knowledge to determine the eyes approximate range , and the edge extraction to obtain the feature point of the eye based on the histogram of the threshold value ; Finally , based on the edge of the eye of each feature point slope change a feature point deviation of corrective methods . Finally , get the eyes and mouth and other parts of the multi- feature information using BP neural network for state recognition of driver fatigue , and method based on PERCLOS principle .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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