Robust multi-pose fatigue monitoring method based on face shape regression model

Abstract

The invention relates to a robust multi-pose fatigue monitoring method based on a face shape regression model. According to the robust multi-pose fatigue monitoring method, the face alignment technology is adopted, an existing face alignment method is improved to be used for accurate human eye location according to practical problems in practical application of fatigue monitoring, and the face pose information can be provided at the same time; the stable human eye position can be provided on the condition of complex light environments and different poses of personnel, and the accurate human eye position can be provided even if the personnel turn away; during application of fatigue monitoring, the closing state sequence of the human eyes can be judged preliminarily according to the given human eye area, and the stability and the applicability of a fatigue monitoring product are further improved because the fatigue state of the personnel is judged through the face pose cooperatively; the closing state of the eyes in a front face image can be accurately judged, and the closing state of the eyes in a sideway face image can be judged. The fatigue state is judged through the face pose information cooperatively, and therefore the robustness is further improved, and the fatigue monitoring requirement of the personnel in a complex scene can be met.

Description

technical field [0001] The invention belongs to the technical field of video intelligent monitoring, and relates to a robust multi-posture fatigue monitoring method based on a human face shape regression model. Background technique [0002] In daily life, you will encounter situations such as too little sleep time the previous day, poor sleep quality, or boring work, which will lead to inattention and fatigue, which will induce work mistakes and cause great losses to society and individuals. Fatigue monitoring system is undoubtedly an effective means to avoid work mistakes. [0003] In the application of fatigue monitoring based on video analysis, it is mainly based on information such as eye closure status and closing frequency to determine whether a person is driving fatigued. At present, the industry mainly detects the position of human eyes in video sequences through edge analysis, active shape model, texture model and their derivative algorithms, and trains classifiers...

AI Technical Summary

Problems solved by technology

There are also academics who directly use the above-mentioned edge analysis, active shape model, texture model and other algorithms to detect the eyelid contour line, and directly judge whether the eyes are closed according to the shape of the eyelid. However, due to the low detection accuracy of the eyelid contour line, a large number of false alarms and false alarms are caused. , this type of technology is currently limited to academic research and is difficult to apply in practical scenarios

[0004] In the above two types of methods, the precise positioning of the human eye position is a prerequisite for accurate fatigue monitoring. In practical applications, due to the complex and changeable posture of the person, the current product uses the face surface modeling method, such as the active shape model ASM Algorithm, texture model AAM and its derivative algorithm CLM, etc., for human eye positioning, are seriously affected by scale, rotation, lighting, etc., especially sensitive to rotation in the plane of the face, and the effect is not ideal when the person turns sideways, which affects fatigue monitoring products The scope of application is currently limited to the field of train drivers, but for people in complex scenarios, such as trucks, buses, private cars and other ordinary vehicle drivers, the background, lighting and other factors change frequently, making it difficult to achieve stability and reliability. fatigue monitoring

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