12 results about "Blink detection" patented technology

The invention relates to a data evaluation method and device based on artificial intelligence, computer equipment and a medium, and is suitable for smart televisions, laptops, projectors, tablet personal computers and the like, and the method comprises the following steps: collecting video streams related to a user, and obtaining environment illumination intensity; performing face key point detection on the video stream to obtain key point information; performing blink detection and frequency calculation based on the key point information to obtain a blink frequency; performing head posture estimation on the key point information, and calculating continuous fixation duration based on a posture estimation result; generating a watching distance of the user to the screen of the target equipment; integrating the environment illumination intensity, the continuous fixation duration, the blinking frequency and the watching distance to obtain index data; evaluating the face image of the user, and obtaining a target index threshold value based on the target age; and performing data evaluation on the index data based on the target index threshold to generate a health evaluation result. According to the invention, the accuracy of health assessment processing of eye using behaviors is improved.

A method, system, and computer readable medium for switching between motion tracking mode. The method includes detecting a significant change in confidence level for motion tracking. A change from a first tracking mode to a second tracking mode is initiated when a significant change in confidence level is detected and the change is paused until a saccade or blink is detected. by eye tracking device. After a saccade or blink is detected the tracking mode switches from the first tracking mode to the second tracking mode.

The invention relates to the technical field of computer vision processing, in particular to an online education video behavior analysis and early warning system fusing facial expression and eye movement parameters, which comprises a video data acquisition and preprocessing module, a video data processing module, a video data processing module and a video data processing module, and extracting a video frame sequence, a face key point, a head posture parameter and an eye region image. According to the invention, video data of students are collected through a camera, blink detection and original frequency calculation are carried out in sequence, a posture and image quality joint evaluation module is introduced innovatively, and multi-dimensional quality scoring is carried out on collection conditions. And performing quality correction on the blink frequency based on the score, and eliminating the individual difference influence of the user in combination with an individual baseline modeling and self-adaptive correction module. And finally, performing time sequence integration and behavior analysis on the effective data to realize accurate early warning of the fatigue state of the student. And the problem of analysis distortion caused by image quality fluctuation and individual difference is effectively solved.

Provided in the embodiments of the present application are a blinking guidance method, a method for updating a screen display parameter, and an electronic device. In the blinking guidance method, after the current operating state of an electronic device is acquired, when the current operating state of the electronic device satisfies a first predetermined state, the electronic device performs blinking measurement on a user who uses the electronic device, so as to obtain a blinking frequency of the user; and when the blinking frequency of the user is less than or equal to a blinking threshold value, the electronic device performs blinking guidance on the user, such that when the blinking frequency of the user is relatively low, the user is prompted to appropriately blink, thereby improving the stability of a tear film, and relieving dry eyes and visual discomfort.

The invention discloses a non-contact blink detection method and system based on a video stream, and the method comprises the steps: 1, capturing an eye video image sequence of a user in real time, calculating the average gray value of the ROI of the left eye and the right eye in each frame of image, and forming a gray value data sequence of the left eye and the right eye; step 2, maintaining a data sequence in a sliding window with a fixed length L for the gray value data sequences of the left eye and the right eye; step 3, on the basis of the threshold parameter of the selected threshold mode, traversing the data sequence in the sliding window obtained in the step 2, determining an effective blink wave crest, and calculating the normalized depth of the effective blink wave crest; and 4, for the normalized decline depth of the effective blink wave crest calculated in the step 3, carrying out joint judgment by adopting a double-threshold mechanism so as to judge a blink event. The method solves the problems that a blink detection mode is extremely sensitive to the change of ambient light, so that misjudgment is easily caused, the ability of identifying the complete form of a blink signal is lacked, and the robustness is poor.

The application discloses an eye blinking detection method and system, a wearable device, and a computer readable storage medium. The method comprises: collecting an electromyography signal of an eye region of a user; detecting an eye blinking event based on the electromyography signal; in a first detection state, when an eye blinking event meeting a first interaction judgment condition is detected, identifying the eye blinking event as an interaction eye blinking event, and outputting a corresponding human-computer interaction instruction; in a second detection state, only when an eye blinking event meeting both the first interaction judgment condition and a second interaction judgment condition is detected, identifying the eye blinking event as an interaction eye blinking event, and outputting a corresponding human-computer interaction instruction; when the first detection state is currently used, after detecting an eye blinking event not meeting the first interaction judgment condition, switching to the second detection state. The application can improve the interaction judgment threshold when natural blinking occurs frequently, inhibit the interference of natural blinking on human-computer interaction, and improve the reliability in the human-computer interaction process.

The invention discloses a blink fatigue detection method and system based on deep learning, and the method comprises the steps: obtaining an eye image data set with an eye opening and closing state label, carrying out the preprocessing, and dividing a training set, a verification set, and a calibration data set; performing training and performance verification on the convolutional neural network model by using the training set and the verification set to obtain a blink detection model; the calibration data set is used for conducting INT8 quantification processing on the blink detection model, and the blink detection model is converted into a light-weight blink detection model and then deployed to an embedded device; taking a continuous video frame sequence which is acquired by a camera in real time and contains a face image as input data of a lightweight blink detection model for detection, and obtaining an effective blink frequency; the number of effective blinking times is counted, the blinking frequency in unit time is calculated, the blinking frequency is compared with a preset multi-level fatigue level threshold value level by level to determine the corresponding fatigue level, and whether the user is in a fatigue state or not is judged. According to the invention, blink fatigue detection can be efficiently and accurately carried out on low-power-consumption equipment.

The embodiment of the invention provides a blink guiding method, a screen display parameter updating method and electronic device.In the blink guiding method, after the current running state of the electronic device is obtained, when the current running state of the electronic device conforms to a first preset state, the electronic device carries out blink detection on a user using the electronic device, and the screen display parameter updating method is used for updating the screen display parameter. And obtaining the blinking frequency of the user. When the blinking frequency of the user is smaller than or equal to the blinking threshold value, the electronic equipment guides blinking of the user, so that when the blinking frequency of the user is low, the user is prompted to blink properly, the tear film stability is improved, and eye dryness and visual discomfort are relieved.

This disclosure relates to the field of terminal control, specifically to an eye state detection method, device, wearable device, and storage medium. The eye state detection method includes: continuously collecting the wearer's periocular electromyography (EMG) signals and corresponding actual posture information during wearable device operation; determining the wearer's wearing displacement state based on a comparison of the actual signal characteristics of the periocular EMG signals with preset reference EMG signal characteristics, and / or the actual posture information; if a wearing offset is determined, correcting blink detection parameters or eye movement detection parameters based on the current periocular EMG signals; and performing eye state detection on the wearer based on the corrected blink detection parameters or eye movement detection parameters. It can autonomously trigger the correction of blink detection parameters or eye movement detection parameters without human intervention, effectively ensuring the wearable device's responsiveness and system stability.

An eye blink detection method and system are disclosed. The eye blink detection method comprises: photographing a face using a DVS camera to obtain a stream of DVS pixels; integrating DVS pixels of the stream of DVS pixels to form a plurality of DVS frames, wherein each of the plurality of DVS frames comprises a plurality of first and second color pixels, each being associated with one or more DVS pixels indicating a brightening event and each of the second color pixel being associated with one or more DVS pixels indicating a darkening event; and determining whether there exists an eye blink action in a DVS frame of the plurality of DVS frames, wherein the determining comprises: determining whether there exists a pattern in which a first and second color region are distributed one above the other in an eye region of the at least one DVS frame.

The application provides an eye blinking detection method and device based on a dynamic threshold, equipment and a storage medium. The eye blinking detection method based on the dynamic threshold comprises the following steps: S1, acquiring an electromyographic signal set; the electromyographic signal set comprises electromyographic signals of an eye region of a user corresponding to a current time window and / or at least one time window before the current time window; S2, extracting an eye blinking feature parameter of each electromyographic signal in the electromyographic signal set; S3, updating an eye blinking detection threshold based on the eye blinking feature parameter; and S4, performing eye blinking detection by using the updated eye blinking detection threshold. The method can dynamically update the eye blinking detection threshold based on the electromyographic signals of the user, realize adaptive adjustment of the threshold, and help improve the accuracy of detecting eye blinking of the user.

The application provides a driver dry eye risk assessment method and device and electronic equipment. First, key point positioning is performed on the facial image data of the driver collected by the driver monitoring system, and then blink detection, eye rubbing detection and squeeze blinking detection are performed based on the key point positioning results. Then, core basic feature extraction is performed based on the blink detection results, eye rubbing detection results and squeeze blinking detection results, and a time sequence is constructed based on the core basic feature extraction results. Then, based on the time sequence, a dry eye risk score prediction is performed through a pre-trained deep learning model. Finally, based on the dry eye risk score prediction result, a dry eye risk level determination is performed according to a preset risk level determination rule. The driver dry eye disease risk can be quantitatively and accurately evaluated by using the application, thereby ensuring driving safety.