283 results about "Head posture" patented technology

The invention provides a driving behavior intelligent identification method and system based on video analysis, and the method comprises the steps: obtaining a driver face video stream and a road environment video stream collected by a vehicle-mounted camera, and reading the driving information recorded by a whole vehicle communication network; recognizing an eyelid closing state, a sight line direction and a head posture in the driver face video stream based on a posture recognition model, and performing fatigue distraction analysis to obtain driver state information; performing motion trail analysis on the road environment video stream and the driving information, and performing driving risk assessment in combination with the driver state information to obtain driving assessment information; and performing early warning construction according to the driving evaluation information, generating early warning prompt information, and synchronously writing the early warning prompt information, the driving evaluation information and the driver state information into a safety data protection memory. The fatigue and distraction states of the driver can be recognized more accurately, and the accuracy of state judgment is improved.

The invention relates to the technical field of computers, and discloses an interaction control method of intelligent glasses. The method comprises the following steps: synchronously acquiring multi-modal data such as eye movement, voice, gestures and head postures and environment and application context information; carrying out independent time sequence feature coding on each modal data; generating a modulation vector in combination with the context, and outputting probability distribution of user intentions through a cross-modal attention fusion network; and a unique execution instruction is determined through an instruction arbitration module based on rules and a state machine. The system comprises corresponding function modules. According to the method, the accuracy, robustness and naturalness of interaction are improved through multi-modal synchronous fusion and a context self-adaption mechanism.

The invention relates to an automatic emergency braking threshold value adjusting method and system based on in-cabin multi-mode information, and relates to the technical field of auxiliary driving, the method comprises the steps that visual information and audio information of a driver and at least one passenger in a cabin are obtained, the visual information comprises facial expressions and head postures, and the audio information comprises audio information of the driver and the at least one passenger; the audio information comprises voice features and non-voice sounds; respectively calculating a driver state coefficient and a passenger state coefficient based on the visual information and the audio information; fusing the driver state coefficient and the passenger state coefficient to obtain an in-cabin safety situation coefficient; and according to the in-cabin safety situation coefficient, a triggering threshold value of the automatic emergency braking system is adjusted in a self-adaptive mode. According to the method, multi-modal information such as vision and audio in the cabin is fused, and state analysis of all passengers is combined, so that intelligent self-adaptive adjustment of the AEB braking threshold value can be realized, and the sensing accuracy and decision reliability of an emergency braking system in a complex scene are improved.

The invention discloses a data processing and interaction control system based on dynamic eye position tracking, and particularly relates to the technical field of man-machine interaction and eye movement tracking, which comprises an image acquisition module, a data processing module, an interaction control module and an equipment interface module. The image acquisition module controls a near-infrared camera to capture an eye video; the data processing module executes dynamic calibration, triggers calibration points based on a historical gaze point distribution sparse region, updates a sight line mapping model online by adopting a recursive least square method, and recognizes an interaction intention through a state machine in combination with a gaze point sequence and an eye movement event mode; the interaction control module carries out logic binding on the eye movement intention and signals such as head postures, gestures and voice, and a composite control instruction is generated through complementary fusion, concurrent binding and conflict arbitration; and the device interface module standardizes the instruction and then outputs the instruction to the controlled device. The system realizes non-intrusive dynamic calibration, effectively improves tracking precision and interaction robustness, and is suitable for a multi-modal fusion control scene.

The invention relates to the technical field of automobile safety detection, and discloses a fatigue driving real-time detection method and system based on a monitoring camera. The method comprises the following steps: acquiring a multi-source sensing data stream in a face area of a driver, and performing time sequence alignment processing to generate a synchronized physiological signal set; multi-modal feature extraction is carried out on the face muscle activity intensity image to obtain an eye movement track feature vector, a head posture change sequence and a face muscle activity intensity map; respectively generating an eye movement fatigue index and a head behavior anomaly score, and obtaining a comprehensive fatigue grade index in combination with a weighted decision algorithm; generating a graded early warning trigger strategy according to the comprehensive fatigue grade index and the real-time driving environment parameter, and calling a corresponding alarm instruction to generate a multi-stage warning signal output stream; and monitoring the change trend of the physiological state of the driver in real time, carrying out dynamic priority adjustment on a warning signal output flow, updating an alarm instruction set, and completing fatigue driving intervention closed-loop control.

The invention belongs to the field of intelligent driving interaction, discloses an HUD interaction method and system combining DMS and ADAS functions, and constructs a multi-mode state sensing system by obtaining a real-time image of a driver and steering wheel grip data. According to the method, eye, pupil and head posture recognition based on deep learning is complementary with a grip distribution matrix, so that fatigue behaviors, distraction behaviors and abnormal holding behaviors can be detected earlier and more accurately, and the precision and robustness of driver state recognition are effectively improved. According to the method, after the fatigue degree of the driver is analyzed, the cognitive load of the driver is dynamically evaluated in a finer-grained mode by constructing the composite indexes based on the eyelid opening degree, the sight line deviation, the grip variation coefficient and the respiratory rhythm. The evaluation mode can reflect the attention, mental load and information processing capability of the driver in real time, so that the system can generate a prompt strategy which better conforms to human factors according to the current state of the driver.

The invention discloses a classroom interaction optimization method and system driven by a teaching posture thermodynamic diagram, and relates to the technical field of education informatization. Comprising the following steps: acquiring image data acquired by a classroom multi-angle camera; extracting a teacher skeleton point coordinate sequence and a student head posture vector; determining a teacher movement track and a teacher gesture unfolding angle parameter; mapping to a classroom plane coordinate system in a ray projection mode to generate a student watching focus coordinate set; performing correlation analysis on the teacher movement track and the student watching focus coordinate set to obtain a time-space correlation parameter; inputting a time sequence prediction model to generate an attention change rate, and taking an attention change rate time point exceeding a preset change threshold as attention attenuation inflection point time; and generating teaching movement line optimization suggestions and outputting visual prompt information. According to the invention, teaching movement line optimization based on teacher-student interaction characteristics can be realized, and classroom interaction efficiency is improved.

The present disclosure relates to a multi-mode vehicle window control method and device. The method combines voice recognition and computer vision technologies, detects a user's voice wake-up word using a vehicle-mounted microphone, turns on the vehicle-mounted camera to detect the user's face and head, analyzes the user's gaze, face pose, and head posture to estimate the user's attention region, and aligns the attention region with a vehicle body coordinate system. Intelligent control of a vehicle window is realized according to specific coordinates of an attention area on the vehicle window. Even if the user's face or eyes are not detected, the vehicle window may be operated accurately by determining the user's intention through the face or head pose.

The invention provides intelligent glasses, a shooting control method, electronic equipment and a storage medium, and the intelligent glasses are characterized in that the lower end of a miniature stable assembly is arranged on a glasses frame, a camera module is installed at the front end of the glasses frame through the upper end of the miniature stable assembly, and the miniature stable assembly is used for adjusting the shooting angle of the camera module. The micro stabilizing assembly has at least two rotational degrees of freedom which are perpendicular to each other; the posture sensor is arranged on the glasses frame and used for detecting head posture changes of a wearer in real time and outputting posture data; and the control processing module is electrically connected with the attitude sensor and the micro stable assembly, and is used for receiving the attitude data, calculating an angle correction value based on the attitude data, and driving the micro stable assembly to dynamically adjust the shooting angle of the camera module based on the angle correction value. Therefore, the shooting angle is always consistent with the actual sight direction of the wearer. Viewing offset caused by wearing slippage and posture change is effectively eliminated, and picture instability is remarkably improved.

The invention discloses a multifunctional automatic comprehensive optometry combined operation table and a use method thereof. The operating platform comprises a mechanical main body structure, a posture sensing system and a central processing and control unit. The mechanical main body structure comprises an optometry chair driven by a rotating disc, a lifting mechanism and an electric rotating shaft, and the posture sensing system is composed of a sitting and leaning posture recognition mechanism used for recognizing body postures and a head posture recognition mechanism used for capturing head movement. The central processing and control unit judges the posture of the optometry person according to the sensing data, and if the posture does not meet the preset standard, the seat height, the backrest angle and the horizontal position are adjusted through voice prompt and an automatic driving mechanism; when the posture reaches the standard, the optometry equipment is automatically controlled to complete operation, automatic and accurate correction of the posture before optometry and intelligentization of the optometry process are achieved, and the accuracy and efficiency of optometry results are effectively improved.

The invention relates to the technical field of AR interaction control, in particular to an AR glasses cursor control method and system based on head posture induction, and the method comprises the steps of data acquisition, mapping establishment, displacement accumulation and cursor control. Through the AR glasses equipment body, the natural rotation posture of the head of the user is collected and used as an input instruction, and a reverse mapping normal form is constructed, so that the physical direction of cursor movement is opposite to the physical direction of user head rotation, and a non-intuitive chasing relation between the cursor and a target is avoided; intuitive mapping between natural rotation of the head of the user and cursor control of the AR glasses is achieved, accurate control over the cursor can be achieved without additional auxiliary actions or a controller, and convenience and privacy are high.

The invention relates to the related technical field of student behavior monitoring, in particular to a classroom student behavior monitoring system, which comprises the following functional modules: an identity label generation module; an image acquisition module; a data storage module; a behavior analysis module; a multi-modal data integration module; and an interaction feedback module. According to the classroom student behavior monitoring system, the identity label generation module, the image acquisition module, the data storage module, the behavior analysis module, the multi-modal data integration module and the interaction feedback module work cooperatively; algorithm design of a head posture analysis unit, a hand action recognition unit, an electronic equipment use detection unit and a sitting posture evaluation unit is further optimized, and a student behavior track diagram and an abnormal condition report are provided as dynamic monitoring results to an external user by means of an interaction feedback module. The defects of the prior art in the aspects of recognition precision, real-time performance, multi-dimensional data analysis and privacy protection are overcome.

The invention discloses an intelligent glasses system and method for health risk monitoring of old people, and belongs to the technical field of intelligent health monitoring. The system is integrated on a glasses body and comprises a multi-mode sensing module, an edge calculation module and an interaction module. The method comprises the following steps: synchronously acquiring physiological parameters and behavior posture data of a user, extracting head posture change characteristics for representing body instability on a local edge side, performing time sequence fusion on the head posture change characteristics and the physiological parameter data, and inputting the fusion result into a pre-trained risk identification model to identify a falling risk or a sudden disease risk. And early warning is triggered when the risk is identified. According to the invention, non-inductive, continuous and accurate monitoring and early warning of high-risk scenes of old people are realized, the defects of single data, high false alarm rate and large response delay of existing equipment are overcome, and the real-time performance and reliability of health monitoring are remarkably improved.

The invention provides a virtual human interaction action generation method for unmarked augmented reality equipment, and the method comprises the steps: obtaining a head posture, a hand posture and a first-person visual angle image sequence of a user through a built-in sensor of an augmented reality helmet, and jointly reconstructing a whole-body posture of the user on the basis; generating an interaction action of a virtual human according to the reconstructed user posture; according to the method, the interactive action of the virtual human can be more natural and real and is kept consistent with the actual behavior of the user; in the field of man-machine interaction, the prior art depends on user control and needs additional hardware equipment and manual participation to maintain an interaction process; or depending on predefined conditions, generalization and practicability are poor; compared with the prior art, additional manual intervention and predefined design are not needed, interaction with the natural response virtual human can be achieved only through the lightweight augmented reality helmet, hardware dependence and labor cost are reduced, automation and usability of the method are promoted, and interaction convenience and immersion are improved.

The invention discloses an intelligent cooperative control system and method for vestibular function inspection. The invention discloses an intelligent cooperative control system and method for vestibular function examination, and belongs to the technical field of medical instruments. The system comprises a cold and hot air perfusion module, a dynamic visual target module, a head posture detection module, a sight distance detection module, an eye movement acquisition module, a voice interaction module, an adjustable examination chair and a central controller. The central controller realizes closed-loop control based on multi-source sensing data: during a cold and hot test, if the elevation angle of the head deviates from 30 degrees + / -2 degrees, stimulation is paused and voice prompt is performed; during visual target testing, visual target parameters are automatically compensated according to the actually measured sight distance of 1.5 m + / -0.1 m so as to maintain a constant visual angle, and the upright state (90 degrees + / -2 degrees) of the head is verified; abnormities such as eye closing more than or equal to 2 seconds or head movement more than or equal to 5 degrees per second are monitored in the whole process, graded voice intervention is implemented, and the abnormities are paused. Body position conversion is guided by voice, multi-source data are synchronously stored, and peripheral and central function correlation analysis is supported. According to the invention, the accuracy and compliance of inspection can be improved.

The invention relates to the technical field of intelligent driving assistance and computer vision, in particular to a driver fatigue state image recognition method based on facial video stream analysis. Comprising the following steps: step 1, collecting a driver face video stream, extracting a face key point sequence of continuous frames, and forming manifold feature points; step 2, introducing an affine invariant Riemannian metric in a symmetrical positive definite Riemannian manifold space, calculating a geodesic line distance of a manifold feature point relative to a preset reference waking state point, and obtaining an expression deformation feature; step 3, constructing an attention entropy field model, calculating the drift velocity of the manifold feature points on the manifold surface, and converting the drift velocity into the entropy acceleration rate of the system; and 4, presetting a potential energy well threshold value, obtaining a fatigue situation evaluation result, and generating a corresponding driver state instruction. According to the method, the system can still accurately extract real fatigue characteristics under the condition that the driver does not look at the front or the head posture is frequently changed, and the misjudgment rate caused by posture change is remarkably reduced.

The invention discloses an eye movement track analysis and classification method and system based on three-dimensional fixation estimation, and relates to the field of computer vision, and the method comprises the specific steps: obtaining a face video, estimating a head posture based on the face video, and extracting a plurality of face images; inputting the facial image into a multi-task learning model, extracting facial features and eye features in parallel, processing the fused features, and outputting an eye prediction state and a three-dimensional fixation vector estimation value; converting the three-dimensional fixation vector estimation value into a fixation point sequence under a screen coordinate system based on a relative pose relationship between the camera and the screen; clustering the fixation point sequence to obtain multi-dimensional eye movement features; and inputting the head posture and the multi-dimensional eye movement features into a fusion de-noising variational auto-encoder to obtain a classification result. According to the method, the text reading task is combined with the space-time distance function, and the extracted multi-dimensional eye movement features such as fixation, eye jump and review can accurately reflect the reading process of the subject, so that the classification accuracy is improved.

The invention discloses a multi-modal speech enhancement method and device based on a deep learning model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the head posture data, binaural audio signals and visual context information of a user in a virtual reality environment, coding the binaural audio signals into three-dimensional space acoustic features, and carrying out the coding of the three-dimensional space acoustic features; and extracting virtual sound source position features and lip motion features from the visual context information, inputting the features into an immersive fusion enhancement network, selectively enhancing or inhibiting acoustic features from different spatial directions, generating an enhanced audio stream, and outputting the enhanced audio stream through a binaural rendering engine. According to the method, the technical problems that in the prior art, due to the fact that multi-modal prior information cannot be effectively fused, voice enhancement lacks spatial selectivity, and an interference sound source irrelevant to vision is difficult to restrain are solved, and head posture dynamic attention and lip motion cross-modal constraint are fused; the technical effects of natural binding of auditory attention and a visual focus and effective suppression of an interference sound source are achieved.

The invention discloses a safe takeover and man-machine cooperation automatic driving control method. The method comprises the following steps: acquiring a face image, eye movement data, a head posture, a sight direction, a blinking frequency and a steering wheel holding state of a driver in real time through a vehicle-mounted camera and a steering wheel torque sensor; the collected driver state information is input into a driver ability evaluation module constructed based on a time sequence model, a driver ability level C is output, and the ability level C is divided into four levels of C0 to C3; and acquiring current driving scene information through an environment sensing sensor carried by the vehicle. According to the invention, all-weather and full-scene environment understanding is realized, the camera provides semantic information of lane lines and traffic signs, the millimeter-wave radar can still accurately measure distance at rainy and foggy nights, the laser radar generates high-precision three-dimensional point cloud, depicts the contour of an obstacle, and injects prior knowledge into a high-precision map.

The invention provides a panoramic fusion and AI change monitoring method and system based on a video image, and belongs to the technical field of computer vision and geographic information processing.The method comprises the steps that firstly, a road network, land data and a double-spectrum video of a target area are obtained, and a double-spectrum panoramic image is generated through cradle head posture correction fusion; secondly, constructing a road network plot topological graph, generating a space risk probability matrix by using a space-time diagram neural network, and generating semantic masks in combination with land categories; then, performing weighted product operation on the risk probability, the semantic mask and the infrared and visible light features of the panorama to generate an enhanced feature matrix; and finally, comparing the characteristic matrixes in historical periods to calculate differential distribution, and locking an area exceeding a threshold value as a target change area, thereby realizing accurate monitoring based on spatial-temporal logic driving. According to the method, adaptive weighted sensing of high-risk area changes can be realized, and the problems of false alarm and missing alarm caused by traditional full-graph unified threshold monitoring are effectively solved.

The invention provides an emotional feature recognition system and method for classroom teaching. The method comprises the following steps: acquiring facial image data and behavior time sequence data of a student in a classroom teaching process through a camera after the student clearly knows and agrees to authorize; determining dynamic expression features of the student through a preset facial analysis model according to the facial image data; on the basis of a head posture change sequence and an eye fixation focus track extracted from behavior time sequence data, constructing concentration indexes and confusion indexes of students in different teaching links through multi-feature fusion; and when it is detected that a preset condition is met, generating an emotional feature tag of the student in classroom teaching based on the dynamic expression feature, the concentration index and the confusion index, and pushing the emotional feature tag to a teacher terminal for visual display. By adopting the scheme of the invention, multi-modal feature fusion analysis of the classroom state of the student can be realized, so that the accuracy of classroom teaching regulation and control is improved.

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.

The application discloses a driver fatigue behavior detection method and system based on video posture invariance, and relates to the technical field of computer vision. The application proposes a key frame selection model based on facial geometric information and a head and face action information fusion space-time network. First, the driver video captured by the vehicle-mounted camera is subjected to sequential processing, and image preprocessing is performed. Then, the key frame selection model based on facial geometric information is constructed based on the geometric features of the facial key points and a two-stage decision mechanism, and the key frames in the video sequence are extracted. Finally, the facial action modalities under any posture are extracted based on the facial forward processing, and the head posture attributes obtained based on the head posture estimation are combined to construct the head and face action information fusion space-time network, which is used for detecting the yawning, speaking, normal and other driver states. The application fully considers the head posture attributes, has high posture robustness, and can effectively distinguish the yawning and other fatigue behaviors from other driver states.

The invention provides a post-retinal detachment reduction body position management method and system and wearable equipment, and relates to the technical field of medical monitoring. The method comprises the following steps: continuously acquiring head motion data through an inertial measurement unit integrated on head-mounted equipment, resolving the data through an embedded processor to obtain a real-time head posture angle, and comparing the real-time head posture angle with a preset rehabilitation position angle range to generate a posture comparison result; meanwhile, neck strain signals are collected through a flexible strain sensor array, and biomechanical characteristic signals are obtained through signal conditioning; and finally, the two types of data are fused, and a judgment result of the consistency of the overall body position of the user is generated through a classification algorithm, so that multi-modal data fusion analysis is realized, the postoperative body position state can be accurately monitored in real time, immediate feedback is provided, and the accuracy of body position management and patient compliance are effectively improved.

Disclosed is a method, system and device for estimating the three-dimensional gaze direction of a human eye based on appearance. The method comprises: acquiring an image or video containing a face; performing face detection in the image or video frame, introducing a face key point detection algorithm to detect and locate the face key points; cutting out the face image and left and right eye images according to the face detection result and the positions of the key points; and estimating the three-dimensional gaze of the human eye in the image using a gaze estimation model, wherein the training process of the gaze estimation model comprises: using a Base-CNNs network to extract features of the face image to obtain a full-face image feature vector, using two asymmetric branch structures of an EE-Net network to extract features of the left and right eye images to obtain double-eye image feature vectors, estimating a head posture vector in combination with the face key point detection algorithm, and finally using a full connection layer to regress the full-face image feature vector, the double-eye image feature vector and the head posture vector for the final gaze estimation task.

The application discloses a method and system for eye use, sitting posture monitoring and adjustment based on multi-sensor fusion, relates to the technical field of eye use health management, and comprises the following steps: wearing smart glasses equipment for a measured person, collecting real-time eye use data of the measured person, including eye use distance data, head posture data and sitting posture data, performing feature extraction on the real-time eye use data, obtaining a distance sequence, a posture angle set and a sitting posture set, performing time sequence evolution analysis on the distance sequence, the posture angle set and the sitting posture set, obtaining a plurality of bad eye use nodes, grouping the nodes based on bad types to obtain different types of abnormal node groups, performing early warning based on the abnormal node groups, performing lens fogging on the smart glasses equipment according to a warning result, and adjusting the sitting posture of the measured person, recording operation contents performed according to the warning result to a device terminal, and pushing the operation contents to a mobile device of the measured person, so that timely supervision and correction of eye use behavior and sitting posture are realized.

The application discloses a mobile phone screen display image enhancement method and system, and relates to the technical field of mobile phone screen control.The method comprises the following steps: acquiring a user face video stream and mobile phone space motion data; identifying head posture information according to the user face video stream; identifying mobile phone posture information by using an extended Kalman filter according to the mobile phone space motion data; calculating a polarization axis included angle between a polarization axis of polarized sunglasses and a polarization axis of a mobile phone screen according to the head posture information and the mobile phone posture information; determining mobile phone screen adjustment parameters according to the polarization axis included angle; and performing compensation processing on the mobile phone screen according to the mobile phone screen adjustment parameters, wherein the compensation processing comprises brightness compensation and color compensation.The application can determine mobile phone screen adjustment parameters in combination with head posture information and mobile phone posture information, and perform brightness compensation and color compensation, so as to realize display image enhancement, and improve accuracy and user experience.

The invention discloses a multi-task learning-based driver driving-suitable credibility identification method and system, and relates to the technical field of multi-task learning, and the method comprises the steps: determining the multi-modal data of a driver based on the feature positions of a plurality of key face features, the corresponding feature forms and the real-time face image of the driver; the multi-modal data simultaneously contains the accurate head posture of the driver, the dynamic geometric information of the eyes and the mouth and the visual texture and expression details of fatigue, distraction or specific emotion on the face of the driver, and a preset credibility value is determined based on the previous driving event of the driver, a multi-task learning system and the current state of the driver; the corresponding driving early warning item is triggered based on the real-time comparison of the preset credibility value and the driving-suitable credibility value, so that the real-time performance and the accuracy of the driving early warning item are ensured.

The invention relates to a fatigue monitoring and early warning method and system based on image processing and pressure sensing. The method comprises the following steps: capturing a facial image of a driver in real time; extracting facial features of the driver from the facial image; when eye features exist in the facial features, the eye closing ratio and the eye continuous closing time are analyzed and compared with a preset fatigue state model, and the driving state of the driver is obtained; when the eye features do not exist in the facial features, head posture changes of the driver are analyzed and compared with preset judgment conditions, and the driving state of the driver is obtained; when the driving state of the driver is a fatigue state, analyzing pressure distribution changes from a seat headrest, a backrest and a cushion area, and judging whether the posture of the driver is abnormal or not; and when the driving state of the driver is determined to be the fatigue state, reminding the driver at different levels according to the fatigue level. The accuracy of fatigue detection can be enhanced, and the state of the driver can be evaluated more comprehensively.

The invention relates to a control method and device of intelligent glasses, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting eye movement tracking data and head posture data of a wearing object of the intelligent glasses; according to the eye movement tracking data and the head posture data, predicting a target fixation area of the wearing object in the target future time period and a moving trend of the target fixation area; determining a target rendering area of the intelligent glasses in the target future time period according to the moving trend of the target fixation area; determining rendering parameters of target display data of the target rendering area in the target future time period according to the target rendering strategy; wherein the target rendering strategy is used for indicating that the first image quality of the target fixation area is greater than the second image quality of other areas except the target fixation area in the target rendering area. By adopting the method, the problems of response delay, insufficient power consumption optimization and poor dynamic scene adaptability of the intelligent glasses can be solved.