178 results about "Eye state" patented technology

The invention relates to an automobile driver fatigue health monitoring system and method. The automobile driver fatigue health monitoring system comprises a sensing system, a decision making system and an execution system. The sensing system is composed of a DMS camera and a millimeter-wave radar, the DMS camera is used for acquiring a state image of a driver, counting an eye state and a yawn state, the millimeter-wave radar is used for monitoring human body physiological indexes, and the decision making system comprises a fatigue judgment module used for counting eye information and yawn information of the driver, judging whether the driver is fatigue or not; the execution system comprises a display screen control module, a music playing module, a seat massage control module and a vibration control module; when the decision-making system detects that the driver is in a fatigue state through the sensing system, music playing, seat massage, safety belt vibration, steering wheel vibration and display screen prompting and alarming are carried out through the execution system; the method has the advantage that the fatigue state of the driver can be effectively alarmed, reminded and suppressed.

The present invention relates to the field of human-computer interaction technology, and discloses an interactive control device for an animated character based on eye tracking, comprising a mask module for collecting user facial information, a stable wearing component, and a collection component for collecting the user's eye state; the mask module comprises a cover body, and the upper and lower parts of the cover body are respectively installed with multiple groups of camera group one and camera group two, and the cover body is installed with a front camera on the opposite side of the camera group one, and the collection component comprises a frame, and the frame is fixedly installed on the surface of the cover body, and multiple eye movement collection cameras are evenly arranged in a ring on the outside of the frame, and a mounting seat is fixedly installed on the outside of the frame. Through the collaborative collection of facial expressions, eye movement data, voice information and the front visual environment by multiple groups of cameras and eye movement collection components, the accuracy and integrity of data collection are significantly improved, high-quality input is provided for the intelligent interaction of animated characters, and the naturalness and immersion of interaction are enhanced.

The embodiment of the invention provides a driver fatigue monitoring method, which relates to a driver fatigue technology, and comprises the following steps: aiming at each frame of an upper body image of a driver, judging an eye state and a mouth state of the frame of image; extracting high-level features of a plurality of RO I areas of each frame of image, and fusing the high-level features to obtain combined features; performing feature feedback prediction to obtain an initial fatigue state; determining an initial eye state and an initial mouth state; and carrying out weighted integration on the states to obtain a final fatigue estimation result. According to the technical scheme, firstly, a plurality of RO I regions are introduced to obtain combined features with higher characterization capability, in addition, an existing sequential network structure is improved, and a more effective fatigue probability prediction value is obtained through feature feedback; and the fatigue prediction value after weighted synthesis is obtained by combining the fatigue prediction value of the key point detection and the fatigue prediction value of the sequential network, so that the fatigue prediction result is more accurate, and the problems of false report and missing report in the current fatigue detection technology can be effectively solved.

The invention relates to the technical field of the Internet of Things, and discloses a safe eye protection method and system for realizing a free screen based on the Internet of Things, and the method comprises the steps: detecting a human face region of a target user under a screen display picture, calculating the region area and the head posture in the human face region, and calculating a fixation point in the human face region through the head posture; recognizing the blinking frequency of the face area, and calculating the face-screen display distance between the face area and the free screen; analyzing the visual fatigue of the target user under the screen display picture, and determining the eye state of the target user under the screen display picture; and when the eye state is abnormal, identifying an external environment and screen display characteristics of the screen display picture, extracting environment characteristics of the external environment, mining influence factors influencing the eye state based on the screen display characteristics and the environment characteristics, adjusting picture parameters of the free screen according to the influence factors and the eye state, and generating a safe eye protection report of the free screen. The safe eye protection effect of the free screen can be improved.

The invention provides a classroom student concentration degree monitoring system and method based on computer vision. The system comprises a vision collection module, an edge calculation unit and a cloud analysis platform. A multi-dimensional sensing mode combining head posture analysis, eye state tracking, facial expression recognition and body movement detection is adopted to evaluate the concentration degree, through a dynamic feature fusion mechanism, the system can intelligently adjust the weight ratio of different modal features, the limitation of single-dimensional analysis in a traditional method is effectively solved, and the accuracy of the system is improved. The system adopts edge calculation and cloud analysis, ensures high efficiency of data processing, ensures reliability of complex algorithm operation, and has important value for improving classroom teaching quality and learning efficiency. According to the invention, through multi-modal visual analysis, behavior characteristics and physiological reactions of students in class are monitored in real time, the concentration level of the students is comprehensively evaluated, and objective teaching feedback and improvement basis are provided for teachers.

The invention relates to the field of fatigue recognition, in particular to an unmanned aerial vehicle operator fatigue state detection method, a storage medium and equipment. Comprising the following steps: performing eye state detection on each frame of to-be-detected face image, and generating eye opening and closing state information corresponding to each frame of to-be-detected face image; generating a fatigue state coefficient of the to-be-detected person according to the eye opening and closing state information corresponding to each frame of to-be-detected face image; and if the fatigue state coefficient is greater than Y1, generating fatigue alarm information of the to-be-detected person. The fatigue degree is evaluated by using the aspect ratio of the eyes. Therefore, the fatigue state of the target person in each frame can be accurately evaluated. Besides, in order to enable the model to better identify the fatigue features, a CBAM module and a C3 module are combined in the first YOLOv5 network, the network can focus on the feature part, the ability of the model to extract important information in the target person image can be improved, and thus the identification ability and accuracy of the model are improved.

The invention discloses an optical adjustment method, head-mounted display equipment and a computer readable storage medium, and relates to the technical field of head-mounted display equipment. The optical adjustment method is applied to the head-mounted display device, the head-mounted display device comprises a variable-focus lens, and the method comprises the following steps: acquiring eye state change data of a wearer; according to the eye state change data, through a pre-trained optical parameter prediction model, a target optical parameter is obtained through prediction, and the target optical parameter comprises a target curvature radius; and based on the target curvature radius, performing curvature adjustment on the variable-focus lens. According to the invention, the focusing state of the optical system can be adjusted in a more accurate matching manner, so that the VAC effect of the head-mounted display device is reduced.

The invention relates to the technical field of data analysis, in particular to a data analysis method and system for a comprehensive therapeutic instrument for myopia and amblyopia. The method comprises the following steps: acquiring eye data of a patient before treatment, determining preliminary light information before treatment according to the eye data of the patient, and sending the preliminary light information to a comprehensive therapeutic apparatus, so that the comprehensive therapeutic apparatus performs treatment; acquiring a real-time eye image during treatment, and determining a real-time eye state of the patient according to the real-time eye image; according to the initial light information and the real-time eye state, treatment light information is determined, and the treatment light information is sent to a comprehensive treatment instrument to be executed so that the comprehensive treatment instrument can conduct treatment. By analyzing the real-time state of the eyes of the patient, an accurate and reasonable reference basis is provided for determination of the treatment light information, the patient is treated according to the treatment light information, and the treatment accuracy and effectiveness are ensured.

The invention discloses a control method and device for a head-up display device in a vehicle, the vehicle and a readable storage medium. The method comprises the steps that current eyeball state information of eyeballs of a driver driving the vehicle is collected; determining current focusing state information of eyeballs based on the current eyeball state information; determining projection parameters of the head-up display device based on the current focusing state information; and controlling the head-up display device based on the projection parameters. According to the method, the projection effect of the head-up display device in the vehicle is improved.

The invention discloses a vehicle control method, device and equipment based on the driver state and a storage medium, and relates to the technical field of vehicles, and the vehicle control method based on the driver state comprises the steps that the target driver state is determined according to the driver eye state and the driver behavior state; and when the target driver state is the target dangerous driving state, performing safe parking control of the vehicle according to current vehicle associated data. Whether the safe parking action is carried out or not is judged according to the eye state and behavior ability of the driver, safe parking is guaranteed under the condition that the driver does not have the ability to take over the vehicle (unconsciously), and the problem that accidents happen under the condition that the driver does not have the ability to take over the vehicle is solved.

The invention relates to the technical field of machine learning, in particular to the field of machine learning supporting a neural network model, and provides an eye movement detection method and device in an eye-closed state, which are used for identifying an image in the eye-closed state and obtaining a real-time movement condition of an eyeball by constructing a deep learning neural network model. The detection result of the continuous frame is associated with a tracker, a motion track is calculated according to the position change of the target in the continuous frame, and the track is represented by a series of coordinate points and can be further converted into motion parameters such as distance, speed and acceleration; and a detection frame is arranged, and a tracking trajectory and motion parameters are displayed on a video picture in real time, so that a user can conveniently observe and analyze a result.

The invention relates to an eye state detection method and apparatus, a display interface, a device and a medium. The method comprises the steps of obtaining multiple frames of target eye images; inputting each frame of the target eye image into a trained preset classification network to obtain a probability of an eye state corresponding to the target eye image; recognizing a blink event based on the probability of the eye state; wherein the preset classification network is a lightweight convolutional neural network, the lightweight convolutional neural network comprises a feature extraction module, and the feature extraction module comprises three convolutional layers and two maximum pooling layers. According to comparison between the eye state probability and the preset threshold value, accurate judgment of the blink state is achieved, the sensitivity and accuracy of the detection method are enhanced, the normal state and the blink signal are distinguished through fine logic calculation, and misjudgment is effectively reduced.

The invention relates to the field of optical lenses, in particular to an optical lens design and manufacturing method, which comprises the following steps of: determining respective characteristic quantities of a central region and a microstructure region in an optical lens according to an actual eye state of a target object; according to the boundary and the characteristic quantity of the microstructure area, determining an initial microstructure layout of the front and rear surfaces of the microstructure area so as to determine an out-of-focus change characteristic of the optical lens, thereby correcting the initial microstructure layout into a final microstructure layout; according to the final microstructure layout, constructing respective three-dimensional curved surface models of the front surface and the rear surface of the microstructure area; and according to the three-dimensional curved surface model, the front surface and the rear surface of the microstructure area are machined. The front surface and the rear surface of the optical lens are matched and designed with proper microstructures, so that the optical lens is ensured to increase higher diopter in the whole microstructure area, and defocus stimulation with enough strength is provided.

The application discloses an eye state detection device and a detection method. The device comprises a support frame, the back of the support frame is provided with a bandage, the front side of the support frame is provided with a lens frame, the front side of the support frame is provided with a driving assembly for driving the horizontal movement of the lens frame, the inside of the lens frame is fixedly provided with a lens, the front side of the lens frame is provided with a rotating groove, the rotating groove is rotatably provided with a ring plate, the outside of the ring plate is provided with a limiting assembly for limiting the ring plate in the rotating groove and preventing the ring plate from falling out, and the top of the ring plate is fixedly provided with a stand column. The application can accurately adjust the position of the eyeball of a patient, position the eyeball by the close observation of the patient on the bottom end of the indicating sheet, facilitate the observation and detection of the eyeball, magnify the eyeball through the lens, facilitate the careful observation of the eyeball, and facilitate the adjustment of the angle of the lens according to the needs, so as to meet the observation needs.

The invention discloses a non-perceived interaction method based on eye state recognition, and belongs to the field of man-machine interaction, and the method comprises the following steps: S1, selecting a proper neural network model; s2, creating a database; s3, training a neural network model; s4, converting the neural network model; the method has the advantages that a highly-optimized eye opening recognition algorithm is directly deployed to the local processor of the eye protection equipment, the design selection brings remarkable safety and efficiency advantages, dependence on network connection is abandoned, and the method is simple and convenient to operate. According to the technology, the eye state monitoring task can be accurately executed in real time in a completely offline state, and the fluency and continuity of user experience are remarkably improved.

The invention relates to a fatigue driving detection method, a terminal, a server and a readable storage medium, and the method comprises the steps: obtaining video data collected by a visual sensor used for monitoring a target object, carrying out the facial feature detection of the video data, and obtaining the eye state information and mouth state information of the target object; determining a fatigue state type of the target object according to the eye state information, and determining a fatigue state type of the target object according to the mouth state information; if the target object is in the suspected fatigue state, uploading sub-video data when the target object is in the suspected fatigue state to a server, so that the server executes a fatigue detection process according to the sub-video data to determine fatigue state information of the target object; and receiving fatigue state information of the target object returned by the server. According to the method, the terminal and the server can jointly detect the fatigue state, and meanwhile, the accuracy and recall rate of fatigue state detection are guaranteed.

The application provides a bullet cabinet with alcohol detection function, and the alcohol detection system comprises: a data acquisition module which acquires detection video of a cabinet opening personnel through a camera, acquires detection voice of the cabinet opening personnel through a microphone, acquires historical cabinet opening data, and acquires current time data; a personnel state determination module which processes the detection video, the detection voice, the historical cabinet opening data, and the current time data to obtain a face alcohol state score, an eye state abnormality score, a body action amplitude score, a voice instability score, and a time risk score; a risk index determination module which determines a final risk index according to the face alcohol state score, the eye state abnormality score, the body action amplitude score, the voice instability score, and the time risk score; and a cabinet lock control module which controls the opening and closing state of an intelligent cabinet lock according to the final risk index. The alcohol detection precision of the cabinet opening personnel is improved, and the risk of using a gun by the cabinet opening personnel is improved.

The invention discloses a vehicle-mounted intelligent monitoring method and system based on multi-mode communication and behavior recognition, and relates to the related field of vehicle-mounted electronics and computer vision, the method adopts multi-mode communication to upload the acquired network signal intensity, vehicle driving state and cab real-time image and video data to the system, and the system is used for monitoring the behavior of the vehicle. Network signal strength changes are predicted according to historical data, and a multi-factor decision strategy is made to automatically switch communication networks; a pre-training target detection model adaptive to a night light environment is applied to position a face area of a driver, face key points are obtained through key point detection, dynamic fatigue characteristics of the driver are tracked in a cross-frame mode in combination with a target tracking algorithm, the eye state, the head posture and the yawning behavior of the driver are analyzed, and the fatigue index of the driver is calculated. And the vehicle driving safety is guaranteed through layered early warning. The problems that an existing vehicle-mounted intelligent monitoring system is single in communication mode, and a fatigue driving detection model embedded in the system cannot accurately position the face area of a driver are solved.

The invention relates to the field of fatigue detection, in particular to a driver fatigue state detection method and system based on machine vision. The method comprises the following steps: acquiring a multi-frame preprocessed facial image of a driver, and marking key points; any sampling moment is used as a target frame, the target frame is used as a midpoint, a preset time period is used as a length to construct a time frame, sampling moments except the target frame in the time frame are used as reference frames, and the initial fatigue degree of the face image of the target frame is calculated according to the displacement amount of key points in the reference frame and the target frame in the time frame; and calculating eye state evaluation according to the fluctuation of the Euclidean distance between the key points in the time frame and the initial fatigue degree, and generating and sending an alarm signal in response to the fact that the eye state evaluation is greater than a preset fatigue threshold value so as to complete fatigue state detection. According to the technical scheme, the precision of the fatigue state detection result can be improved.

The invention provides a virtual expression rendering method and device and electronic equipment, and the method comprises the steps: carrying out the sampling of a preset expression map set in response to a specific expression rendering event, and obtaining a target expression map containing an eye state and / or a mouth state corresponding to a specific expression switching event; and replacing the head three-dimensional mesh model of the currently displayed virtual object with a first head model containing the target expression map, so that the displayed virtual object shows a target expression corresponding to the target expression map. According to the mode, the expression map set can be sampled to obtain the target expression map, and the head three-dimensional mesh model of the currently displayed virtual object is replaced by the first head model containing the target expression map, so that the virtual object can display the target expression corresponding to the target expression map; due to the fact that the expression map set can comprise the eye states and / or the mouth states corresponding to the different expressions, various stylized virtual expressions can be rendered.

The invention discloses a display adjustment method and device, electronic equipment and a computer readable storage medium. The display adjustment method comprises the steps that eye state information of a target object is acquired; wherein the eye state information at least comprises eye fatigue and a sight line position; in response to the fact that the eye fatigue degree is larger than a preset fatigue degree threshold value, display parameters of part or all of the display content at the sight line position are adjusted so as to reduce the eye fatigue degree; wherein the display parameters at least comprise at least one of the size, the style, the color, the brightness and the contrast ratio of the display content. Based on the mode, the display effect of the display equipment can be effectively improved, so that the user experience is improved.

The invention relates to a human eye state detection method and device, and belongs to the technical field of intelligent cabin perception, and the detection method comprises the steps: obtaining an eye opening degree value of a driver through the eye opening feature information of the driver, obtaining a standard eye opening degree value according to the eye opening feature information of a standard human face, obtaining a threshold value adjustment factor through the driver eye opening degree value and the standard eye opening degree value, obtaining a target opening degree threshold value according to the threshold value adjustment factor and a preset standard opening degree threshold value, and obtaining a real-time opening degree value based on the real-time eye feature information of the driver. The eye state of the driver is determined according to the real-time opening degree value and the target opening degree threshold value; according to the method, the eye feature information of the driver and the eye feature information of the standard face are combined, the target opening degree threshold value is adjusted, the eye state is judged based on the target opening degree threshold value, the situations of missing detection and error detection caused by the size of the eyes can be effectively avoided, and therefore the detection accuracy of the eye state is improved.

To provide a yawn detection system for appropriately detecting a yawn of a driver.SOLUTION: A yawn detection system 1 includes an opening determination section 11, a mouth covering determination section 12, a thinner eye determination section 13, and a yawn detection section 14. The opening determination section 11 determines whether an opening state where a driver opens the mouth continues for equal to or more than a first time on a face image. The mouth covering determination section 12 determines whether a mouth covering state where the driver covers a part of the mouth or the whole mouth by hand continues equal to or more than a second time on the face image when it is determined that the opening state continues for equal to or more than the first time. The thinner eye determination section 13 determines whether the driver becomes in a thinner eye state with a thinner vertical width on the face image compared with a state before the driver opens the mouth when it is determined that the mouth covering state continues for equal to or more than a second time. The yawn detection section 14 detects a yawn of the driver when it is determined that the driver becomes in the thinner eye state.SELECTED DRAWING: Figure 1

The application relates to the technical field of brain detection, and particularly discloses a test auxiliary system and method for brain diseases, wherein the system comprises an automatic control module, a light source control module and a data synchronization module. The automatic control module is used for inputting an open-closed eye test rule, converting the open-closed eye test rule into a light period control rule, and generating an open or off light instruction according to the light period control rule. The light source control module is used for controlling the switch state of an access light source according to the open or off light instruction, so as to simulate an open eye state and a closed eye state. The data synchronization module is used for acquiring environment brightness data and light source switch state data, and is also used for acquiring an electroencephalogram from an electroencephalogram test device and generating a visual report of the open-closed eye test. The technical scheme of the application can effectively solve the problem that a patient with a brain problem found in a nuclear magnetic resonance examination does not cooperate in an open-closed eye test, and improve test accuracy.

The application relates to a line-of-sight calibration, motion tracking and precision testing method based on adaptive time sequence analysis prediction, which specifically comprises the following steps: through image color recognition technology, color blocks and calibration point positions in a screen are recognized in a scene image, the actual size of the screen is combined, and the screen position is roughly positioned; distortion calibration is carried out based on the calibration point information, the screen position is precisely positioned, and the mapping relationship between the scene image and the precisely positioned screen is determined; eye control type nine-point calibration is carried out, the mapping model of the eyeball physical information and the gazing space position of the tester is determined, the screen gazing point coordinates are obtained in combination with the mapping relationship; line-of-sight positioning precision quantitative testing is carried out; through an adaptive time sequence analysis prediction algorithm, the current line-of-sight positioning is optimized and the future gazing point position is predicted in combination with historical line-of-sight information, meanwhile, the parameters in the time sequence analysis prediction algorithm are adaptively adjusted according to real-time precision or the intensity of shaking; through an eye state detection algorithm, the state of the human eye in the open and closed eye state is detected, and eye movement control is carried out.

This application discloses a device control method, apparatus, electronic device, and computer-readable storage medium, relating to the field of computer technology. The method includes: receiving raw electroencephalogram (EEG) signals of a target user collected by a signal detection device, and acquiring the raw operating state of an Internet of Things (IoT) device; performing frequency domain conversion processing on the raw EEG signals, and determining the target user's eye state information in the frequency domain based on the converted signal data; adjusting the operating state of the IoT device based on the eye state information, the raw operating state, and a preset state adjustment relationship, thereby achieving rapid and stable adjustment of the IoT device's operating state based on the user's actual eye state, with high applicability.

The application relates to the field of eye detection, and discloses a three-dimensional model construction method based on ophthalmic detection. First, human eye state information is collected, and the human eye state information is pretreated to obtain initial eye state information; data analysis is performed on the initial eye state information to establish initial parameters of an eye three-dimensional model; parameter optimization is performed to obtain screening parameters of the eye three-dimensional model; the eye three-dimensional model is established, eye key points in the eye three-dimensional model are collected, and eye screening point data are bound for the eye key points; the eye key points are normalized, screening operation is performed through big data comparison, and final eye state information is obtained; geometric structure reconstruction is performed to obtain a reconstructed eye three-dimensional model; the application solves the problems that the prior art eye detection is inaccurate, is easy to affect a diagnosis result, and the existing eye model has a large difference in size, position and shape from actual patient eyes.

Providing runtime power profile tuning based on eye state in a processor-based augmented reality (XR) device is disclosed herein. In this regard, in some exemplary aspects, a processor of a processor-based XR device is configured to apply a default power profile and to detect eye closing events of a user of the processor-based XR device using a blink detection circuit. In response to detecting the eye closing event, the processor selects a test power profile from a plurality of power profiles and applies the test power profile. The processor then measures an operational characteristic of the processor-based XR device and compares the operational characteristic to a corresponding reference range. Based on the comparison, the processor updates one or more of the plurality of power profiles.

The invention discloses a drunkenness state detection method, system and device and a medium, and the method comprises the steps: obtaining the eye data of a driver in a preset time period, and enabling the eye data to comprise eyeball state data; performing time domain feature extraction on the eyeball state data to obtain time domain feature data; performing frequency domain feature extraction on the eyeball state data to obtain frequency domain feature data; fusing the time domain feature data and the frequency domain feature data to obtain eyeball tremor feature information; and based on the eyeball tremor characteristic data, utilizing a neural network model to detect the drunkenness state of the driver. Through fusion of frequency domain and time domain feature information, the eyeball tremor state of the driver is evaluated from multiple features, the robustness of data and the generalization ability of a model can be improved, and the drunk driving state detection precision is improved. And the judgment result can be fused with a drunkenness state detection result based on the physiological feature information of the driver and the vehicle state information, so that the accuracy of deducing the drunkenness state of the driver is further improved.

The invention discloses a fatigue driving detection method, device and equipment, a medium and a product, and relates to the technical field of image processing.The method comprises the steps that vehicle driving parameters, driver images and steering wheel pressure sensing parameters in the vehicle driving process are collected; wherein the driver image comprises a head image of the driver; analyzing the vehicle driving parameters to obtain driving behavior information; analyzing the driver image and the steering wheel pressure sensing parameter to obtain driver state information; wherein the driver state information at least comprises an eye state, a face state and steering wheel holding force information; the driving behavior information and the driver state information are used, the fatigue driving detection result is obtained through analysis, the vehicle driving parameters and the steering wheel pressure sensing parameters except the physiological parameters of the driver can be collected, the fatigue degree of the driver is detected jointly from multiple angles, and therefore the accuracy of the fatigue driving detection result is improved.