Fatigue driving early warning system and method based on depth camera

A depth camera and fatigue driving technology, applied in computer components, instruments, character and pattern recognition, etc., can solve the problems that affect the analysis of key points of the eyes and are not suitable for use, and achieve high practical value, high precision, and robustness. High stick effect

Pending Publication Date: 2021-01-19
QINGDAO UNIV +1
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Problems solved by technology

However, it is mainly based on the analysis of images obtained by ordinary cameras, and is greatly affected by the light inside the car. It is not suitable fo

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  • Fatigue driving early warning system and method based on depth camera
  • Fatigue driving early warning system and method based on depth camera
  • Fatigue driving early warning system and method based on depth camera

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Embodiment Construction

[0050] The present invention is described in further detail now in conjunction with accompanying drawing.

[0051] Such as Figure 1-6 A fatigue driving warning system based on a depth camera is shown, including a depth camera, a face feature point positioning module, a state recognition module, and a driving state warning module;

[0052]The main depth camera is installed in front of the driver, and is used to obtain the frontal image collection of the driver without glasses and without glasses;

[0053] The face feature point positioning module extracts the visual feature parameters of each frame of the depth camera image, recognizes the face through infrared image preprocessing, and then uses the LBF feature model trained by the combination of random forest and global linear regression to obtain face feature points ;

[0054] The state recognition module is used to analyze the face extracted by the face feature point positioning module, extract eye state information and m...

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Abstract

The invention relates to a fatigue driving early warning system and method based on a depth camera. The fatigue driving early warning system comprises a depth camera, a feature point positioning module, a state recognition module and a driving state early warning module. The method comprises the following steps: acquiring an infrared image sample and a depth image sample, and acquiring a human face region and a human face feature point set; based on the human face feature point set acquired in the step S2, performing calculation to obtain a corresponding classification state and fatigue stateevaluation. Aiming at the night recognition feature extraction problem, the system and the method adopt a depth camera to acquire an infrared image and a depth image, the eye state is judged accordingto the eye length-width ratio of front face feature points or the upper and lower eyelid distance of side face feature points, and the fatigue state is comprehensively evaluated according to the eyestate and the mouth state. The method can meet the requirements of real-time detection, and has a practical value for fatigue detection and early warning.

Description

technical field [0001] The invention relates to the technical field of driving monitoring and early warning, in particular to a fatigue driving early warning system and method based on a depth camera. Background technique [0002] At present, my country is a big automobile manufacturing country, and the number of automobiles ranks second in the world. The increasing number of vehicles not only brings convenience to travel, but also brings a series of problems. Among them, frequent traffic accidents have become the most important problem, and the number of deaths caused by traffic accidents that occur every year is increasing year by year. Traffic accidents caused by fatigue driving account for a large proportion. [0003] At present, there are many researches on human drowsiness / fatigue driving detection technology, which are mainly divided into (1) measuring human physiological signals, such as EEG, ECG, skin potential lamp, the main disadvantage of which is the need for p...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/46
CPCG06V40/168G06V40/171G06V40/172G06V20/597G06V10/25G06V10/44G06V10/50
Inventor 张维忠李金宝李广文
Owner QINGDAO UNIV
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