546 results about "Head posture" patented technology

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention provides a driver fatigue monitoring technology using eye movement tracking, and relates to the field of auxiliary driving, and the technology comprises the steps: collecting a face image frame through a front-end camera, carrying out Medipe (Media Pipeline) detection, recording a head posture, and recognizing an iris center region; extracting continuous eye closing time and mouth state information of the driver through the face information; a PNP algorithm is used; a world coordinate system and a camera coordinate system are established, and the sight line vector and the external environment are located in the same space; modeling is carried out on the human eyes and the fixation area; coordinates of a fixation point in a three-dimensional space are obtained through calculation of the geometric model; real-time attention analysis is carried out through the fixation point, and an analysis result is determined; the attention change condition and the fatigue state of the driver are effectively recognized through a BP neural network in combination with a fixation point analysis result. And early warning measures are taken in time to remind a driver. The system is crucial for improving the driving safety, and necessary warning and intervention can be provided when the driver is distracted or fatigued.

The invention relates to the technical field of computer vision security and protection, and discloses a helmet normative wearing identification method and system considering head key point positions, and the method comprises the steps: constructing a human body detection model and a helmet normative wearing classification model, and carrying out the training; collecting a monitoring video, performing frame extraction on a video stream according to a specific time interval to obtain a video frame, and positioning a human body in the video frame by using the trained human body detection model to output a human body image; reasoning each human body image through a human body key point recognition algorithm, positioning a plurality of key point positions of a human body, judging a head deflection posture by taking mutual positions among key points in a head region, and cutting a head region image according to the key points in the head region; and inputting the head region image into the standard helmet wearing classification model for reasoning and recognition, judging the head posture through head key point recognition, and judging whether the helmet is worn in a standard manner through the classification model, so that the method has relatively high generalization ability and recognition precision.

The invention relates to the technical field of image recognition and storage, and discloses a driving video recording method and system based on four-way monitoring. The method comprises the following steps: synchronously acquiring and processing the surrounding environment of a vehicle and the head posture of a driver through a video stream to obtain driving attention reference data; compressing and storing the sight blind area data according to the head posture features to obtain hierarchical video data; performing enhanced mapping on the graded video data to obtain an attention compensation image; performing threat identification and sorting on the compensation image to obtain threat level data; establishing a threat level index structure; and performing hierarchical storage on the data according to the attention coverage degree to obtain a storage data set. According to the method and the device, the technical problem that key scene data may be lost due to lack of consideration of driver attention distribution in a multi-channel video real-time recording process is solved.

In various examples, head pose prediction for automotive occupant sensing systems and applications is presented. The systems and methods described herein provide for a machine learning model trained using a dataset that comprises ground truth head pose data computed using a registered head model of a training subject. While operating a vehicle, one or more cameras and a depth sensor capture synchronized images of the training subject. To compute a ground truth 3D head pose, angular deviations between a 3D point cloud and the registered head model may be computed to obtain a 3D ground truth head pose measurement. Using an extrinsic calibration transform, the head pose measurement may be mapped into the sensor coordinate frame. Training samples may be produced for training the machine learning model that comprise an optical image frame and the head pose measurement transposed into the frame of reference for that optical image frame.

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.

A method includes determining at an extended reality (XR) device, a first set of vertex adjustment values of a distortion mesh and receiving image frame data of a scene captured at a first time and at a first head pose using a see-through camera of the XR device. The method further includes applying the first set of vertex adjustment values of the distortion mesh to the image frame data to obtain intermediate image data, and predicting a second head pose at a second time subsequent to the first time. The method also includes generating, based on the predicted second head pose, a second set of vertex adjustment values of the distortion mesh, applying the second set of vertex adjustment values of the distortion mesh to the intermediate image data to generate a rendered virtual frame and displaying the rendered virtual frame by the XR device at the second time.

The invention provides a driver anti-fatigue monitoring alarm method, medium and system, and belongs to the technical field of image data processing. A vehicle-mounted camera device is used for collecting a facial image sequence, and a Dlib key point screening function is used for feature point positioning; the feature extraction accuracy is improved by adopting an adaptive illumination compensation algorithm and a minimum spanning tree algorithm, eye features such as an eye closing proportion value and a blinking frequency and head motion features such as a head posture angle value and a nodding frequency are calculated, and physiological data such as blood pressure, heart rate and body temperature are collected by utilizing a smart watch. And inputting the multi-dimensional features into a pre-trained FatgueNet model through an adaptive feature selection gating mechanism, outputting a fatigue state evaluation index, and dividing alarm levels. When the severe fatigue state is recognized and the driver enters the sleepy state, the system sends a pulse stimulation instruction to the smart watch to wake up the driver, and the adaptability and accuracy of the system are 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 provides a target distance estimation method and system based on optical characteristics of a holder, and the method comprises the steps: carrying out the automatic target detection and tracking shooting of a to-be-detected target at a current visual angle, and obtaining a target video of the to-be-detected target; meanwhile, the cradle head posture and the camera focal length are collected in real time; performing target detection according to the target video to obtain key feature points of the target; performing distortion correction on the key feature points through image coordinate mapping to form a unified image coordinate system; combining the image coordinate system with the attitude information of the holder, and mapping the image coordinate system to a three-dimensional virtual space to construct a virtual coordinate system; and calculating the target distance of the to-be-measured target according to the geometric position of the to-be-measured target in the virtual coordinate system. According to the invention, the target distance can be accurately estimated in a complex scene and at different visual angles based on the holder system with a dynamic attitude adjustment function, and the method has the technical effects of real-time performance and robustness.

The invention discloses a device with sleep assessment and intervention functions, which is arranged on a pillow or a back cushion, and comprises an information acquisition module for acquiring physiological data, head posture data and sound data of a human body, and sending the acquired data to a sleep assessment module, the sleep assessment module is used for extracting multi-modal sleep features according to the collected data and performing sleep assessment according to the multi-modal sleep features; wherein the source of the physiological data is analyzed by using the head posture data, and a dynamic weight is given, so that a self-adaptive sleep staging algorithm is completed; and the sleep intervention module is used for acquiring a sleep intervention scheme according to the sleep evaluation result, and activating different working modes of a vibration motor and / or a loudspeaker according to the sleep intervention scheme so as to achieve the effect of intervening the sleep of the user. According to the method, the source of the physiological data is analyzed by using the head posture data, and the dynamic weight is given, so that a self-adaptive sleep staging algorithm is completed, and the sleep staging evaluation accuracy of a real scene is improved.

The invention provides a head posture estimation method and system based on a neural implicit field, and belongs to the technical field of image processing. According to the method, discretization processing is carried out on a head posture label, and a Gaussian function and a Gumbel function are used for joint modeling to generate an asymmetrically distributed label. Sampling points are arranged at fixed intervals in a three-dimensional space, and global features of an input RGB image are extracted through a RepVang network. And establishing a mapping relationship between image features and spatial sampling points by means of a neural implicit field, mapping face information to a point cloud space, and performing binary classification on each sampling point by using a multi-layer perceptron to generate human face point cloud data. Geometric features of the point cloud data are extracted and fused with global features of the RGB image, and the head posture is predicted through combination of a classification module and a regression module. By adopting the method and the system, the precision and the robustness of head posture estimation can be effectively improved, and a reliable solution is provided for practical application.

Examples of wearable systems and methods can use multiple inputs (e.g., gesture, head pose, eye gaze, voice, totem, and / or environmental factors (e.g., location)) to determine a command that should be executed and objects in the three-dimensional (3D) environment that should be operated on. The wearable system can detect when different inputs converge together, such as when a user seeks to select a virtual object using multiple inputs such as eye gaze, head pose, hand gesture, and totem input. Upon detecting an input convergence, the wearable system can perform a transmodal filtering scheme that leverages the converged inputs to assist in properly interpreting what command the user is providing or what object the user is targeting.

The invention discloses an intelligent interaction system applied to AI glasses, relates to the technical field of man-machine interaction, and is used for solving the problems that resource utilization redundancy is increased and equipment power consumption efficiency is reduced. Fusing to form a head posture variable quantity, and analyzing the dispersion degree of the head posture variable quantity based on an MAD algorithm to judge the complexity of an interaction scene; selecting to execute resource optimization or enhance an interaction response strategy according to the complexity, such as improving the speech recognition accuracy and adapting to a complex sound field; further introducing a proportionality coefficient of illumination intensity, movement speed and noise intensity, constructing a long-short-term memory neural network model to predict interaction complexity, realizing dynamic interaction mode adjustment through a gating mechanism and a regularization method, and judging whether to improve the interaction mode according to prediction output. And the self-adaption and high-reliability interaction capability of the AI glasses in a complex environment is ensured.

The invention discloses a distance measurement system based on artificial intelligence, and the system can achieve the automatic grabbing of size parameters of glasses. According to the system, a camera is adopted to capture a glasses image, a gyroscope and an artificial intelligence algorithm are adopted to recognize the spatial position of glasses, spatial coordinates of feature points are obtained, the pixel scale # imgabs0 # of the image is obtained subsequently based on the physical geometrical relationship of the glasses and the real pupil distance of a user, and finally the size parameters of the glasses are obtained. The glasses parameters comprise a glasses width, a pupil distance, a front inclination angle, a surface bending angle and a glasses-eye distance; the camera is an apple panel built-in camera; the gyroscope can obtain the pitch angle # imgabs1 #, the deflection angle # imgabs2 # and the inclination angle # imgabs3 #, so that the head posture of a glasses wearer is obtained, and the size measurement precision of the glasses is greatly improved. According to the system, automatic measurement of glasses parameters is realized, the working intensity of manual glasses matching is greatly reduced, and digital upgrading of high-end customized glasses is realized.

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 provides a man-machine interaction sight tracking method, which comprises the following steps of: intercepting and amplifying eye positions in a whole image, training a Yolov5 algorithm by adopting a data set to mark pupil positions and light spot positions from the image, and determining the position of a pupil according to a central position coordinate and a height value and a width value of a detection frame. Marking the pupil position and the light spot position; then, according to a better embodiment of the invention, a 3 * 3 rotation matrix is adopted, the head posture of the interaction object is marked, and the rotation angle of the head posture is obtained; finally, in the mapping process of the coordinates of the display area, the rotation angle of the head posture and the difference value of the pupil light spot position are fitted, so that the tracking precision is remarkably improved, and especially the positioning accuracy of the edge position of the display area is improved.

The invention discloses a method and device for recalibrating the gazing direction of a driver, and belongs to the technical field of intelligent driving. The method comprises the steps that a driver face image in a natural driving scene is collected for head posture estimation, and head posture data of a driver standardized image are obtained; on the basis of a gaze area prediction model, gaze data corresponding to the head posture data of the driver standardized image is obtained, the gaze data is processed, and a calibration image and a target image are further obtained; and respectively inputting the calibration image and the target image into a feature extraction network to extract image features, inputting the calibration image features and the target image features into a direction classification network to find an association relationship between the target image and the calibration image, and completing sight line calibration based on the association relationship. The method can be used for self-adaptive deployment of the pre-training sight estimation model, implicit gaze direction mapping under the condition of few samples and even zero samples is achieved, and extra hardware calibration or model training is not needed.

The invention relates to the technical field of intelligent glasses, and discloses an intelligent glasses wearing identification method based on multi-modal perception, and the method comprises the steps: collecting the capacitance change rate, the facial contour matching degree, the body surface temperature, the vital signs, the head attitude angle and other multi-source feature data in real time, generating five groups of feature data, and carrying out the standardization processing and weight fusion, forming a comprehensive feature vector; calculating a preliminary confidence score by combining a dynamic weight adjustment mechanism and a rule engine, and correcting a deviation caused by head movement; judging a wearing state according to the corrected confidence score, outputting a label, and realizing function control in linkage with a gesture interaction module; the abnormal state triggers local or cloud alarm; the sampling frequency of the sensor is dynamically adjusted according to the wearing state, the frequency is reduced to 1Hz when the sensor is not worn, the frequency is improved to 10Hz when the sensor is worn so as to enhance the response precision, and the decision process is only triggered when the feature is suddenly changed in combination with an event-driven strategy. According to the invention, the precision and the reliability can be improved, and low-power-consumption design is realized.

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 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 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 a driver distracted driving detection method and device, terminal equipment and a program product, and the method comprises the steps: obtaining a real-time image of a vehicle driven by a driver when the driving speed of the vehicle is greater than a preset speed threshold value; determining a real-time head pose and a real-time sight angle corresponding to each real-time image; the real-time head pose comprises a real-time head pitch angle and a real-time head yaw angle; the real-time sight angle comprises a real-time sight pitch angle and a real-time sight yaw angle; if the continuous preset number of the real-time head poses are within the preset head pose range and the real-time sight angles exceed the preset sight angle range, it is judged that the driver has the distracted driving behavior. According to the method, whether the driver has the distracted driving behavior or not can be judged by combining the real-time head posture and the real-time sight angle of the driver in the vehicle driving process, whether the driver has the distracted driving behavior or not can be more accurately determined, and misjudgment is avoided.

The invention provides a head-eye posture fused driver state grading intervention method and system, and the method comprises the steps: dividing a fixation region which comprises a safety region, an alarm region and a dangerous region based on an in-vehicle three-dimensional coordinate system; calculating an initial line-of-sight vector according to the driver face key points, obtaining a final line-of-sight vector by combining line-of-sight deviation angle correction, and obtaining a fixation point coordinate based on the eye point coordinate and the final line-of-sight vector; based on fixation point coordinates and fixation area matching, the driver fixation area category is judged; and acquiring the current vehicle speed and the fixation duration in the fixation area, inputting the current vehicle speed and the fixation duration into a pre-trained decision model to obtain a graded intervention strategy, and executing an intervention action according to the strategy. The accuracy of the fixation point position is ensured through fusion of the head posture and the sight line deviation; a grading intervention strategy is obtained by combining the fixation point position, the fixation duration and the vehicle speed, interference to normal driving is reduced, effective early warning can be achieved in the risk accumulation stage, and the method has adaptability to complex vehicle use scenes.

A computing device for generating a three-dimensional (3D) representation of a head of a participant in a video communication session is provided. The computing device comprises processing circuitry causing the computing device to be operative to acquire a captured 3D representation (300) of the head, and to identify positions (1-27) of a set of facial landmarks in the captured 3D representation (300). The set of facial landmarks comprises facial landmarks indicative of a boundary of the human face. The computing device is further operative to determine a pose of the head, and to determine a boundary (310) between an inner part (320) and an outer part (330) of the captured 3D representation (300), based on the identified positions (1-27) of the set of facial landmarks. The inner part (320) of the captured 3D representation represents the face of the participant. The computing device is further operative to generate an avatar representation corresponding to the outer part (330) of the captured 3D representation (300), using a Machine-Learning (ML) model trained for human heads, with the determined pose of the head as input.

The invention relates to the technical field of TWS earphone low-delay audio processing methods, in particular to a TWS earphone low-delay audio processing method based on head motion prediction, which comprises the following steps: S1, collecting head motion parameters of a user, the head motion parameters comprising a head posture theta, a head rotation angular velocity omega, a head movement direction phi and a head movement velocity V; s2, establishing a relation model between the head motion parameters and the audio time delay; s3, predicting head motion parameters in a future time period based on a recurrent neural network; s4, according to the predicted head motion parameters, audio time delay is calculated, and audio rendering is carried out; s5, outputting the audio signal subjected to time delay compensation; wherein the steps from S2 to S5 are executed circularly so as to adjust audio output in real time; head motion prediction is carried out by using the recurrent neural network, and the head position and the motion trend of the user can be accurately estimated in a millisecond level, so that audio pre-rendering and accurate output are realized.

The invention relates to the technical field of video communication, in particular to an intelligent AI glasses video call auxiliary system which comprises a visual adjustment module, a voice interaction module, a communication scheduling module, a posture recognition module and a feedback prompt module. According to the invention, by acquiring the eye movement track and the pupil diameter change, adjusting the image brightness and the color contrast in combination with the ambient light, relieving visual fatigue, enhancing the call comfort, analyzing the voice frequency band and matching the keywords, the accurate recognition of the operation intention is realized, the call response efficiency is improved, and the mistaken touch and delay are avoided; the call priority is judged according to the action state, the acceleration trend and the contact person weight, the task access accuracy is improved, the user intention is judged according to the head posture and the sight line trend, misoperation and repeated confirmation are avoided, interaction convenience is enhanced, meanwhile, the audio intensity and image prompt are regulated and controlled according to the task type and the prompt level, the multi-environment prompt requirement is met, and the user experience is improved. Information omission is reduced, and call experience is optimized.