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Vehicle-mounted fatigue detection method based on multi-scale binary mode

A fatigue detection, multi-scale technology, applied in character and pattern recognition, image data processing, instruments, etc., can solve problems such as poor detection accuracy

Inactive Publication Date: 2019-08-02
CHONGQING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is the technical problem of poor detection accuracy existing in the prior art

Method used

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  • Vehicle-mounted fatigue detection method based on multi-scale binary mode
  • Vehicle-mounted fatigue detection method based on multi-scale binary mode
  • Vehicle-mounted fatigue detection method based on multi-scale binary mode

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Embodiment 1

[0069] This embodiment provides a vehicle fatigue detection method based on multi-scale binary patterns, such as figure 1 , the vehicle fatigue detection method based on the multi-scale binary pattern comprises:

[0070] Step 1: Manually classify and mark fatigue samples and non-fatigue samples on the driver image, select training samples and test samples respectively, and preprocess the training samples and test samples;

[0071] Step 2, divide the training sample image into several non-repetitive sub-regions, use multi-scale local binary patterns for feature extraction, and obtain multi-scale local binary image features;

[0072] Step 3, performing discrete Fourier transformation on the multi-scale local binary image features to obtain the histogram Fourier feature vector of the multi-scale binary pattern;

[0073] Step 4, connect the histogram Fourier feature vectors that constitute the multi-scale binary pattern to characterize the image features, select the kernel functi...

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Abstract

The invention relates to a vehicle-mounted fatigue detection method based on a multi-scale binary mode, which solves the technical problem of low detection precision. The method comprises the following steps: dividing a training sample image into a plurality of non-repeated sub-regions, and carrying out feature extraction by using the multi-scale local binary mode to obtain multi-scale local binary image features; performing discrete Fourier transform on the multi-scale local binary image features to obtain a histogram Fourier feature vector of a multi-scale binary mode; step 4, connecting histogram Fourier feature vectors which form a multi-scale binary mode, for representing image features, selecting a kernel function, carrying out classification training on MLBP features of sample images through a nonlinear support vector machine, and obtaining trained SVM classification models and parameters. The problem is well solved, and the method can be used for vehicle-mounted fatigue detection.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a vehicle fatigue detection method based on a multi-scale binary pattern. Background technique [0002] With the development of society and the progress of economy, cars have become a necessary means of transportation for people to expand their living space, improve their living efficiency and improve their quality of life. The rapid increase in the number of automobiles has brought many social problems while the society is prosperous. The primary issue is road safety, and road traffic accidents have become the primary factor causing abnormal deaths of human beings. Statistics show that in all road traffic accidents, human factors account for 80%, and fatigue driving is the most common human factor. Therefore, the recognition and early warning of fatigue driving will play a vital role in avoiding vicious traffic accidents and ensuring the safety of people's live...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06T5/10
CPCG06T5/10G06T2207/20056G06V40/161G06V40/168G06V40/172G06V20/597G06V10/467G06V10/50G06F18/2411
Inventor 方昳凡许清陆相羽黄子恒易和阳滕飞宇杨森元戈洋
Owner CHONGQING UNIV OF POSTS & TELECOMM
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