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Fatigue state detection method and system based on key point detection and head posture

A technology of fatigue state and head posture, applied in the field of automatic fatigue detection, can solve the problems of large convolutional neural network model and difficulty in achieving real-time performance, and achieve high real-time performance, reduce the amount of parameters and calculations, and achieve fast speed

Pending Publication Date: 2022-04-15
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

At the same time, the common convolutional neural network model is relatively large, and it is difficult to achieve real-time results when predicting

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  • Fatigue state detection method and system based on key point detection and head posture
  • Fatigue state detection method and system based on key point detection and head posture
  • Fatigue state detection method and system based on key point detection and head posture

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Embodiment 1

[0058] Such as figure 1 As shown, the present embodiment 1 provides a fatigue state detection method based on key point detection and head posture, comprising the following steps:

[0059] S1. Construct and train the MMC multi-task prediction model using the depth separable convolutional network in the backbone network, and obtain the trained MMC multi-task prediction model;

[0060] When training the MMC multi-task prediction model, the 300W_LP data set is used for training. Since the 300W_LP data set is widely used in facial feature recognition and head pose analysis, it is a commonly used field 2D landmark data set. It consists of 61225 head pose images and is flipped Expanded to 122,450 images, and the 300W_LP dataset has the coordinates of key points of the face and the label of the head pose angle. Before using the 300W_LP dataset to train the MMC multi-task prediction model, the images in the dataset are preprocessed, including:

[0061] Crop the redundant background p...

Embodiment 2

[0094] like Figure 4 As shown, this embodiment provides a fatigue state detection system based on key point detection and head posture, including:

[0095] The model training module is used to construct and train the MMC multi-task prediction model, and obtains the trained MMC multi-task prediction model;

[0096] When training the MMC multi-task prediction model, the 300W_LP data set is used for training. Since the 300W_LP data set is widely used in facial feature recognition and head pose analysis, it is a commonly used field 2D landmark data set. It consists of 61225 head pose images and is flipped Expanded to 122,450 images, and the 300W_LP dataset has the coordinates of key points of the face and the label of the head pose angle. Before using the 300W_LP dataset to train the MMC multi-task prediction model, the images in the dataset are preprocessed, including:

[0097] Crop the redundant background part of the image according to the coordinates of the key points of the...

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Abstract

The invention discloses a fatigue state detection method and system based on key point detection and head attitude, and the method comprises the steps: constructing and training a backbone network, employing an MMC multi-task prediction model of a depth separable convolutional network, obtaining a plurality of frames of face images in unit time, employing an MTCNN network to detect the face position of each image, and cutting out a head image; inputting the head image into a trained MMC multi-task prediction model to obtain position information of a head posture angle and a face key point; respectively judging the fatigue states of the head, the eyes and the mouth by using a double-threshold method; the method comprises the following steps: setting a correlation coefficient to comprehensively judge the fatigue state of a person, combining the correlation of face key point detection and head posture, adopting an MMC multi-task prediction model with a backbone network as a deep separable convolutional network, and simultaneously performing two tasks in the same network, thereby greatly reducing the required parameter quantity and operation quantity, and improving the accuracy of the prediction result. Therefore, the detection speed of the model is improved, and the real-time effect is achieved.

Description

technical field [0001] The invention belongs to the technical field of automatic fatigue detection, in particular to a fatigue state detection method and system based on key point detection and head posture. Background technique [0002] Face detection and head pose estimation in the field of computer vision refers to detecting all faces in an image and estimating that each face can represent three orientation angles: yaw (yaw), pitch (pitch) and roll (roll) . Judging people's motives and intentions based on head posture has a wide range of applications in providing cues and gaze, such as human behavior analysis and gaze estimation. Although face detection and pose estimation have made great progress respectively, it is still a daunting task to realize a multi-task framework with good real-time and robustness in complex environments. At present, the convolutional neural network (CNN) is generally used to solve face detection and head pose estimation, which can achieve extr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V40/18G06V40/16G06V20/40G06T7/73G06N3/04G06V10/80
Inventor 唐贤伦张艺琼李洁刘庆邹密邓武权徐梓辉王会明
Owner CHONGQING UNIV OF POSTS & TELECOMM