Driver fatigue detection method based on CNN eye state recognition

A driver fatigue and state recognition technology, applied in the field of driver fatigue detection, can solve the problems of few applicable scenarios, low detection efficiency, dependence, etc., and achieve the effects of wide application range, reduced calculation amount, and favorable transplantation

Inactive Publication Date: 2018-07-20
TIANJIN POLYTECHNIC UNIV
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Problems solved by technology

[0004] There are many traditional methods for detecting the eye state, but because the position of the iris in the eyelid is not fixed, and the gray-scale projection curve of the iris area of ​​the eye is used to judge the eye state, there are scenarios that are prone to false detection, low detection efficienc

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  • Driver fatigue detection method based on CNN eye state recognition
  • Driver fatigue detection method based on CNN eye state recognition
  • Driver fatigue detection method based on CNN eye state recognition

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[0027] In order to enable your examiners to further understand the structure, features and other purposes of the present invention, it is now described in detail as follows in conjunction with the attached preferred embodiments. invention.

[0028] The process of the present invention is as follows figure 1 As shown in the figure, firstly, the AdaBoost algorithm based on haar features is used (to detect the face region of interest, based on the result, the method of combining random forest and linear regression is used to detect the face feature points, and the eye region is extracted; then according to The basic structure of convolutional neural network convolutional layer, downsampling layer and fully connected layer and the Lenet5 network structure, reduce the number of neurons in the network by optimizing the neural network structure by convolution of local receptive field, weight sharing and downsampling The number and weights are obtained to obtain a new specific convol...

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Abstract

The invention relates to a driver fatigue detection method based on CNN eye state recognition. Driver fatigue detection can be completed by acquiring useful information of a face through the eye staterecognition. With the application of the method provided by the invention, eye states can be recognized and classified more accurately. As for the eye state recognition method via a convolutional neural network (CNN), a recognition rate for a circumstance that sunglasses are worn is improved via an infrared video sample; and finally, a plurality of constraint conditions are detected on the basisof fatigue/drowse physical quantity (PERCLOS) and blinking frequency, so that the driver fatigue states can be judged. Based upon experiments, it is proved that with the application of the method, theeye states can be recognized accurately in real time and fatigue driving behaviors can be early warned effectively.

Description

technical field [0001] The invention relates to a driver fatigue detection method based on CNN eye state recognition. The method can adapt to illumination changes and glasses blocking conditions, and uses the eye state to determine the driver's fatigue state. It belongs to the field of machine vision technology and can be applied to Assisted driving and driving safety field. Background technique [0002] Studies have shown that fatigue driving is one of the main causes of traffic accidents, which has attracted the attention of many countries and governments. Therefore, the research on accurate and rapid driver fatigue detection is of great significance. The detection method based on machine vision has become an important method for driver fatigue detection due to its advantages of non-contact and real-time. [0003] In the application of vision-based driver fatigue detection system, PERCLOS (percentage of eyelid closure over the pupil over time) and eye blink frequency are ...

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Application Information

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IPC IPC(8): A61B5/11G06K9/62G06K9/00G06N3/08
CPCA61B5/0077A61B5/1103G06N3/082A61B5/72G06V40/161G06V40/171G06V40/18G06F18/24
Inventor 耿磊梁晓昱肖志涛张芳吴骏苏静静
Owner TIANJIN POLYTECHNIC UNIV
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