Abnormal driving detection method and device
A technology for abnormal driving and detection methods, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of little driver abnormality detection, single driving abnormality detection, etc., to achieve accurate acquisition and improve robustness. the effect of improving the safety factor
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Embodiment 1
[0040] as attached Figure 4 As shown, the embodiment of the present invention provides a method for detecting abnormal driving, which specifically includes the following steps:
[0041] Step 1, detect the face image data collected within a predetermined period of time, and extract multiple human eye features; specifically include:
[0042] Using Dlib based on HOG (Histogram of Oriented Gradient, HOG for short) feature and SVM (Support Vector Machine, Support Vector Machine, SVM for short) algorithm to detect face images, obtain several face features; Extract six feature points corresponding to the left eye and right eye from the face features, the six feature points include inner eye corner feature points, outer eye corner feature points, feature points at both ends of the upper eyelid line and feature points at both ends of the lower eyelid line .
[0043] For details, see S100: Human eye detection: This system uses the face detector in the Dlib library. Dlib is a C++ mach...
Embodiment 2
[0134] Based on the first embodiment of the above detection method, the embodiment of the present invention also provides a detection device for abnormal driving, including:
[0135] A camera for collecting face images;
[0136] Sensors, used to collect vehicle driving data; including GPS vehicle speed, acceleration, longitude, latitude;
[0137] The internal detection module is connected with the camera and the alarm, and is used to detect the face image data collected within a predetermined period of time, and extract multiple human eye features; obtain the number of blinks according to the relationship between multiple human eye features and eye opening and closing degrees; The reference value of fatigue is obtained according to the number of blinks and the relationship between eye opening and closing with time during a single blink; the reference value of fatigue is input into the detection model to predict the degree of fatigue to obtain the predicted value of fatigue; when...
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