A fatigue detection method based on deep learning face posture estimation

A face posture and fatigue detection technology, which is applied in computing, computer parts, instruments, etc., can solve the problems of inability to make accurate judgments of drivers and the inability to accurately identify the status of drivers, so as to reduce the probability of accidents, Ingenious design, strong anti-interference effect

Inactive Publication Date: 2019-06-21
以萨技术股份有限公司 +1
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Problems solved by technology

[0004] The purpose of the present invention is to solve the shortcomings in the prior art that the state of the driver cannot be accurately and reliably identified, so

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  • A fatigue detection method based on deep learning face posture estimation
  • A fatigue detection method based on deep learning face posture estimation
  • A fatigue detection method based on deep learning face posture estimation

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[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0026] Reference Figure 1-2 , The fatigue detection method based on deep learning face pose estimation includes the following steps:

[0027] S1. Obtain the video stream of the camera in the cab: First, collect the driving state video of the driver through the on-board camera in the cab, detect the face area based on the HOG extraction algorithm, and frame it with a rectangular frame, and use the face recognition module, including the face recognition module Use the on-board camera installed in front of the driver to collect the driver's driving status video, use the HOG-based face detector to detect each frame of the video stream, identify ...

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Abstract

The invention discloses a fatigue detection method based on deep learning face posture estimation. The fatigue detection method comprises the following steps: S1, acquiring a cab camera video stream;S2, positioning face key points; S3, defining a 3D face model with six key points; S4, positioning eye and mouth positions according to the face key points in the step S3; and S5, determining an alarmexceeding a set threshold value according to the driver nodding accumulation frequency, and determining an alarm indicating that the mouth opening and eye closing state exceeds the set threshold value. The method can perform real-time detection, has the characteristics of high anti-interference performance, comprehensive identification and detection, high accuracy, high stability and the like, and can fundamentally remind a driver to drive; By comparing the aspect ratio with a set eye opening and closing threshold value and a set mouth opening and closing threshold value and combining the comparison between the duration of eye closing or mouth opening and a set time threshold value, whether a driver is in a fatigue state or not is comprehensively judged, and if the driver is in the fatigue state, an alarm is triggered to give an alarm.

Description

technical field [0001] The invention relates to the technical fields of computer vision processing and AI intelligent transportation, in particular to a fatigue detection method based on deep learning face pose estimation. Background technique [0002] With the development of expressways and the increase of vehicle speed, it has become an important part of automobile safety research. Many car owners and friends will doze off and feel sleepy when driving in the afternoon. This is a very dangerous driving behavior. Because automobiles are running at high speeds, and the traffic flow on roads in major cities is very large, traffic accidents will occur if you are not careful. In order to hurry up, some driver friends will doze off and drive, and fall asleep unconsciously. It is these few seconds of drowsiness, which brings great harm to the safety of drivers and passengers. threaten. [0003] At present, it mainly focuses on the three core technologies of fatigue driving iden...

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Application Information

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IPC IPC(8): G06K9/00
Inventor 武传营李凡平石柱国
Owner 以萨技术股份有限公司
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