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Real-time fatigue driving detection method

A detection method and fatigue driving technology, applied in the direction of instruments, biological neural network models, character and pattern recognition, etc., can solve the problems of low real-time performance and slow processing speed, and achieve reduced deployment costs, fast detection speed, and computational load low effect

Pending Publication Date: 2020-01-17
UNIV OF SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

For example, the detection rate based on the AdaBoost classifier and PERCLOS standard implemented by Xiao Qing can reach 86%-93%, but the processing speed is slow and the real-time performance is not high.

Method used

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Embodiment Construction

[0047] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0048] See attached image 3 , the fatigue driving detection method of the present invention realizes the overall process.

[0049] The input of the system realized by the present invention is the 24-bit true-color video captured by the camera, and the output is the fatigue detection result of the driver. The overall flow chart of the fatigue detection method of the present invention is as follows image 3 shown, including the following steps:

[0050] Step 1. Obtain image data from the video captured by the camera;

[0051] Step 2. Perform state recognition based on the acquired image data, including face recognition, eye and mouth positioning and classification;

[0052] Step 3, calculating multiple parameters and forming a multi-parameter composite criterion;

[0053]Step 4: Fatigue detection, determine whether fatigue is based on multi-paramete...

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Abstract

The invention discloses a real-time fatigue driving detection method. The method comprises the following steps: 1, acquiring image data from a video shot by a camera; 2, according to the obtained image data, performing state recognition, including face recognition, and completing eye and mouth positioning and classification; 3, calculating various parameters, and forming a multi-parameter composite criterion; 4, performing fatigue detection: determining whether fatigue exists or not according to a multi-parameter composite criterion, and if not, turning to the step 1; and if so, giving an alarm. According to the technology of the invention, an SSD (Single Shot MultiBox Detector) network is mainly utilized to identify faces in video images and position and classify eyes and mouths, and thena PERCLOS method, blinking frequency, yawn frequency and the like are utilized as fatigue judgment standards. In view of the detection effect of the SSD network, the technology of the invention has higher detection accuracy and real-time performance.

Description

technical field [0001] The invention belongs to the field of computer graphics and image processing, and in particular designs a driver fatigue detection method. Background technique [0002] Video image processing is one of the research focuses and hotspots in the field of computer science, and has made great progress. Fatigue driving detection based on image processing is the focus of scholars, and it is also the field involved in this patent application. [0003] The existing face recognition and fatigue driving detection algorithms mainly include AdaBoost algorithm, PERCLOS method and so on. It mainly uses the AdaBoost-based face detection and the active shape model of human eye positioning to detect the position of the human eye, and then uses the PERCLOS method to judge the opening and closing state of the driver's eyes. For example, the detection rate based on AdaBoost classifier and PERCLOS standard implemented by Xiao Qing can reach 86%-93%, but the processing spe...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V20/597G06N3/045
Inventor 尹东张锐周志鹏
Owner UNIV OF SCI & TECH OF CHINA
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