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A method for detecting fatigue driving based on face features

A face feature, fatigue driving technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of unsatisfactory detection effect and low accuracy without considering the driver wearing glasses, and achieve accuracy High, improve accuracy, reduce the effect of misjudgment probability

Inactive Publication Date: 2019-03-01
HARBIN UNIV OF SCI & TECH
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  • Summary
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  • Application Information

AI Technical Summary

Problems solved by technology

Although these methods can achieve a certain detection effect, most of them do not take into account the situation that the driver wears glasses, and generally only detect and identify a single part of the eyes or mouth, especially the use of machine vision technology based on the external characteristics of the driver Changing the technical means of monitoring can easily lead to unsatisfactory detection results, and there are defects of misjudgment and low accuracy

Method used

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  • A method for detecting fatigue driving based on face features
  • A method for detecting fatigue driving based on face features
  • A method for detecting fatigue driving based on face features

Examples

Experimental program
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Effect test

specific Embodiment approach 1

[0051] Specific embodiment one: the method for detecting fatigued driving based on facial features in this embodiment is carried out in the following steps:

[0052] 1. Image acquisition;

[0053] 2. Image processing: use the adaptive median filter method to denoise the collected images, and use the adaptive threshold method to perform light balance on the collected images;

[0054] 3. Face positioning based on the improved Adboost algorithm classifier; among them, only when the weight of the sample is smaller than the update threshold at this time, and the sample is classified incorrectly, the weight will be adjusted accordingly, increasing weights; otherwise, the weights will all be scaled down;

[0055] 4. Proceed to the next step if a face is detected, and proceed to step 1 if no face is detected;

[0056] 5. Facial Feature Recognition

[0057] 5.1 Human eye positioning: the Gaboreye model combines the radiation symmetry algorithm to locate the position of the eye;

[...

specific Embodiment approach 2

[0069] Specific embodiment two: the difference between this embodiment and specific embodiment one is: the self-adaptive median filter method adopts the median filter template of 3 * 3, δ=0.8, The Gaussian template and mean filter template. Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0070] Embodiment 3: The difference between this embodiment and Embodiment 1 is that in step 2, the minimum window of the adaptive median filtering method is 3, and the maximum window is 19. Other steps and parameters are the same as those in Embodiment 1.

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Abstract

The invention discloses a method for detecting fatigue driving based on face features, and relates to a method for detecting fatigue driving through artificial intelligence. The fatigue state of the driver can be judged by jointly detecting the characteristics of the eyes and the mouth, and the problem that accurate detection cannot be carried out due to wearing of glasses is avoided. The detection method comprises the steps of 1, image acquisition; 2, image processing; 3, carrying out face positioning based on an improved Adaboost algorithm classifier; 4, carrying out the next step when the human face is detected, and carrying out the step 1 when the human face is not detected; 5, face feature recognition; 6, determining a fatigue state;. Compared with a traditional monitoring method, themethod has the advantages that the eye and mouth states are combined for fatigue state feature extraction, the judgment accuracy is improved, and the misjudgment probability of fatigue driving detection is reduced.

Description

technical field [0001] The invention relates to a method for artificial intelligence detecting fatigue driving. Background technique [0002] Traffic safety is an important issue facing the world at present. Relevant studies have shown that traffic accidents account for the largest proportion of human unnatural deaths. Many people have lost their precious lives as a result, and have brought great economic losses to the country. negative impact. In order to solve a series of problems caused by fatigue driving, with the efforts of many experts and scholars at home and abroad, many novel and effective methods have emerged. Today, fatigue detection algorithms can basically be divided into two categories, objective and subjective. In terms of subjective aspects, most of the real-time status is judged by the driver's own feeling or observation by others. However, due to differences in personal physique and carefulness of observation, there is no uniform standard and the accuracy...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V20/597
Inventor 兰朝凤毛秀欢刘春东赵宏运刘岩
Owner HARBIN UNIV OF SCI & TECH
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