A fatigue detection method based on deep learning face posture estimation

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...

AI Technical Summary

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 it is impossible to make an accurate judgment on whether the driver is tired or not, and proposes a fatigue detection method based on deep learning face pose estimation

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