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Driver monitoring algorithm is a technology used to analyze driver status and behavior, aiming to ensure the safety of the driving process. This algorithm includes multiple functions, as follows:
Face detection: This function is used to recognize and detect the driver's face in images or videos. It can be achieved by analyzing features such as color, shape, size, etc.
Face Key Point Detection: This function is used to identify key points on the driver's face, such as eyes, mouth, nose, etc., after detecting them. These key points can be used for further analysis of the driver's state and behavior.
Head posture estimation: This function can estimate the driver's head posture based on the position of facial key points, such as whether to face forward, whether to side head, etc. This can help evaluate the driver's alertness and attention.
Live detection: This function is used to confirm that the detected face is a real face, rather than other objects in the image or video. It is usually achieved by analyzing facial features and dynamic information.
Facial feature extraction: This function is used to extract features from detected faces, such as age, gender, facial expressions, etc. These features can be used to classify and analyze the characteristics of drivers.
Seat belt detection: This function is used to detect whether the driver is wearing the seat belt correctly. It can analyze the color, shape, texture, and other information in the image or video of the seat belt to achieve this.
Camera blur detection: This function is used to detect the degree of eye blur of drivers in images or videos to determine whether they are in a fatigue driving state.
Block sunglasses detection: This function is used to detect whether the driver is wearing sunglasses that block infrared and ultraviolet rays, which may affect the performance of facial recognition algorithms.
Driver monitoring algorithm is a technology used to analyze driver status and behavior, aiming to ensure the safety of the driving process. This algorithm includes multiple functions, as follows:
Face detection: This function is used to recognize and detect the driver's face in images or videos. It can be achieved by analyzing features such as color, shape, size, etc.
Face Key Point Detection: This function is used to identify key points on the driver's face, such as eyes, mouth, nose, etc., after detecting them. These key points can be used for further analysis of the driver's state and behavior.
Head posture estimation: This function can estimate the driver's head posture based on the position of facial key points, such as whether to face forward, whether to side head, etc. This can help evaluate the driver's alertness and attention.
Live detection: This function is used to confirm that the detected face is a real face, rather than other objects in the image or video. It is usually achieved by analyzing facial features and dynamic information.
Facial feature extraction: This function is used to extract features from detected faces, such as age, gender, facial expressions, etc. These features can be used to classify and analyze the characteristics of drivers.
Seat belt detection: This function is used to detect whether the driver is wearing the seat belt correctly. It can analyze the color, shape, texture, and other information in the image or video of the seat belt to achieve this.
Camera blur detection: This function is used to detect the degree of eye blur of drivers in images or videos to determine whether they are in a fatigue driving state.
Block sunglasses detection: This function is used to detect whether the driver is wearing sunglasses that block infrared and ultraviolet rays, which may affect the performance of facial recognition algorithms.
