Fatigue state detection method based on sub-block characteristic matrix algorithm and SVM (support vector machine)

Abstract

The invention discloses a fatigue state detection method based on a sub-block characteristic matrix algorithm and an SVM (support vector machine), and belongs to the technical field of image processing and mode recognition. The method analyzes and judges whether a driver is in a fatigue state or not through facial features. The method includes the steps: firstly, acquiring a driver video image, and performing illumination compensation and face area detection; secondly, performing eye and mouth area detection in a face area. According to the method, characteristic extraction of an eye image isperformed by an eye sub-block characteristic matrix algorithm, influence of illumination conditions and glasses wearing on detection can be reduced, characteristic extraction of a mouth image is performed by a mouth sub-block characteristic matrix algorithm, interference of tooth appearing and mouth beard in detection can be reduced, images after characteristic extraction are classified by an SVMalgorithm, and reliability is improved under the condition of a small sample training set. According to the method, fatigue characteristics are analyzed according to the eyes and the mouth, the methodtransmits warning information when the driver is in a fatigue state, and traffic accidents can be decreased.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and in particular relates to a fatigue state detection method based on block feature matrix algorithm and SVM. Background technique [0002] In recent years, there are tens of thousands of traffic accidents caused by fatigue of motor vehicle drivers every year in our country. Fatigue driving has become one of the important factors of frequent traffic accidents, which has brought huge damage to the life safety and property of drivers and pedestrians. loss. Therefore, driver fatigue detection has become a research hotspot in current safe driving assistance measures, and more and more scholars are interested in fatigue detection. [0003] In response to this problem, researchers have proposed many fatigue detection methods, which can be roughly divided into three types: physiological parameters, vehicle behavior, and facial feature analysis. The first method is to ...

AI Technical Summary

Problems solved by technology

The first method is to measure the driver's physiological parameters, such as electroencephalogram (EEG), electrocardiogram (ECG), etc., but these methods are invasive and require physical contact between the device and the driver, which will interfere with driving

The second method is to measure the behavior of the vehicle, such as speed, steering wheel angle, and lane departure detection, etc., but this method is greatly affected by driving conditions, driving experience, vehicle type, etc.

Horizontal and vertical grayscale projection of the mouth area to obtain the height and width of the mouth opening, and judge whether to yawn according to its aspect ratio, but when the teeth are exposed or the mouth has a beard, this detection method does not work well

[0005] Due to the defects of the above algorithms, it is difficult to achieve ideal results in practical applications and needs to be improved.

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