134 results about "Motion analysis" patented technology

The invention relates to the technical field of three-dimensional analysis, in particular to an oral cavity three-dimensional motion analysis system based on dynamic occlusion scanning. The system comprises an oral cavity occlusion feature extraction module, a dynamic occlusion reconstruction module, an occlusion track smoothing module and an oral cavity motion abnormity identification module, and a multi-view structured light scanner can be used for carrying out image scanning on the oral cavity in the dynamic occlusion process so as to obtain a multi-frame original occlusion image sequence; occlusal feature extraction and three-dimensional occlusal reconstruction are carried out on the multiple frames of original occlusal image sequences to construct corresponding three-dimensional occlusal model sequences; a dynamic occlusion trajectory prediction model is constructed based on the three-dimensional occlusion model sequence, oral cavity three-dimensional motion analysis and occlusion trajectory smoothing filtering processing are carried out, and a smooth three-dimensional occlusion motion trajectory is generated; and performing oral cavity motion abnormity identification according to the smooth three-dimensional occlusion motion track so as to generate an oral cavity occlusion three-dimensional motion abnormity identification result. According to the invention, accurate capture and analysis of three-dimensional motion of the oral cavity can be realized.

The invention discloses a pet guy rope state detection method and system based on collaborative motion analysis. The method comprises the following steps: acquiring a to-be-detected video stream; performing target detection and target tracking on each frame of image to obtain a first position information sequence of at least one target person and a second position information sequence of at least one target pet, and allocating a unique identifier ID for each target person and each target pet; respectively calculating the motion characteristics of each target person and each target pet in the time sequence; pairwise pairing is carried out based on the motion features, and the collaboration degree of the motion system is calculated; if the synergy degree is preset to be a synergy degree threshold value th, judging that the target pet is in a rope pulling state, otherwise, judging that the target pet is in a non-rope pulling state; and updating the rope pulling state of the corresponding target pet ID, and generating early warning information based on the final state of all the target pet IDs. The pet guy rope detection accuracy is remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to a multi-dimensional tennis action analysis system, which comprises a data acquisition module, a data preprocessing module, an evaluation analysis module and a feedback interaction module, and is characterized in that the data acquisition module comprises a posture data acquisition unit, a ball path and walking tracking unit and a motion parameter acquisition unit; the data preprocessing module carries out space-time calibration on posture, track and motion parameters based on a timestamp synchronization mechanism, a fusion data set is formed through data cleaning and standardization processing, and the evaluation analysis module is used for distinguishing action types, integrating scores of posture dimensions, power generation dimensions and drop point dimensions and generating a comprehensive evaluation report; and the feedback interaction module displays an evaluation result in real time on an interface based on characters and visual icons and outputs historical analysis. According to the invention, the data validity is guaranteed, the error interference is eliminated, the analysis efficiency and the professionality of the evaluation result are improved, the use threshold is reduced, and the universality and practicability of the system are improved.

The invention discloses a three-dimensional human body posture estimation method fusing anatomical structure prior and state space modeling, and belongs to the technical field of computer vision. According to the method, a 2D human body posture key point sequence is obtained and is embedded into a high-dimensional feature space through a linear projection layer to obtain joint feature representation; the method comprises the following steps: constructing a backbone network containing a plurality of TriadFormer modules, wherein each TriadFormer module is integrated with a graph convolutional network GCN, a hierarchical part internal order state space model HIP-SSM and a limb pilot asymmetric local-global converter LimbFormer; capturing local spatial dependence through GCN, injecting anatomical structure prior by HIP-SSM and modeling chain kinematics dependence, and strengthening intra-limb collaboration and cross-limb global interaction through LimbFormer; and finally, a three-dimensional human body posture estimation result is obtained through mapping of a linear projection output head. According to the method, through multi-module collaborative modeling, the human anatomical structure and kinematics constraints are deeply fused, the precision and robustness of attitude estimation in a complex scene are improved, the calculation efficiency is considered, and the method is suitable for multiple fields such as human-computer interaction and motion analysis.

The invention relates to the technical field of computer vision, in particular to a face recognition intelligent footpath terminal system applied to a scenic spot, which comprises a face image acquisition module, a feature point positioning module, a vertical motion analysis module, an energy consumption judgment module and a data communication module. According to the method, a time sequence vertical velocity difference and vertical kinetic energy quantification model is constructed through the displacement of the eyebrow arch key points in the continuous image, and movement intensity periodic data is formed by continuous displacement change, acceleration amplitude and duration, so that the local fine movement of the face is stably analyzed; dynamic behavior characteristics are kept in differential expression in illumination fluctuation and attitude change environments, so that the system obtains higher dynamic judgment consistency in a complex footpath scene, the accuracy and continuity of passenger flow recognition and behavior statistics are enhanced, uploaded data have clearer motion quantification significance and reliability, and the system is more suitable for popularization and application. And the problems of statistical offset and judgment lag caused by dynamic environment interference are improved.

The invention discloses a tracking motion analysis method and system based on a four-wheel drive wheel type ROS platform, and the method comprises the steps: obtaining an RGB image and depth information, carrying out the preprocessing, extracting two-dimensional coordinates of a joint key point, and mapping the two-dimensional coordinates into three-dimensional coordinates; calculating to obtain a target center point coordinate; the rotation angle and the movement distance are converted into movement instruction signals; constructing a skeleton edge set, extracting spatial features and dynamic features through calculation, and integrating the spatial features and the dynamic features to obtain a fusion feature vector; inputting the fusion feature vector into a preset action evaluation model, and outputting an evaluation result; and generating personalized action correction feedback according to an evaluation result. According to the method, target tracking, motion capture, dynamic evaluation and personalized feedback generation can be realized in a mobile environment, so that the scientificity, the real-time performance and the intelligent level of training are remarkably improved.

The invention relates to the technical field of tool monitoring, in particular to a tool wear monitoring system based on a numerically controlled lathe tool use video. The system comprises a video acquisition and motion analysis module, a texture evolution analysis module, a key frame judgment and generation module, a visual feature extraction module, a time sequence change rule analysis module, a feature screening module and a wear judgment module, and is used for acquiring a continuous video stream of a machining area, identifying a contact area of a cutter and a workpiece, tracking a pixel motion track and judging the wear of the workpiece. Recording texture structure change to generate a surface texture evolution sequence; determining a key video frame and generating a sequence, extracting a first-order wear feature, and analyzing a feature dimension time sequence change rule to obtain a time sequence change curve; comparing the curve with a reference curve form, screening refining wear characteristics, and generating a wear process judgment result according to a time sequence evolution trend; the tool wear monitoring efficiency can be improved.

The invention discloses an IMU (Inertial Measurement Unit) visual fusion motion analysis method which comprises the following steps: a multi-source data synchronous acquisition step: acquiring three-dimensional linear acceleration and angular velocity original data through an inertial measurement unit, and acquiring a visual image sequence at the same time; a dynamic zero offset compensation step: executing a preset action on the inertial original data to calculate a zero offset compensation value; a time-space synchronization triggering step: generating a synchronization signal when detecting that the calibration inertial data reaches a preset threshold value, and establishing a mapping relationship between the timestamp and the visual frame sequence; a motion attitude fusion step: taking an attitude derived from calibration inertial data as prediction input, taking a joint coordinate extracted from a visual image sequence as observation input, and updating a fusion state through Kalman filtering; and a quantitative index extraction step: calculating a joint motion range, a motion instantaneous rate and a coherence coefficient. The method solves the technical realization problem of multi-source data acquisition and fusion, improves the accuracy and real-time performance of motion analysis, and is suitable for the fields of motion teaching and rehabilitation training.

The invention discloses a visual perception and trajectory restoration method, system and device for chicken motion analysis and anomaly detection in a large cage farm and a storage medium, and relates to the technical field of video monitoring and behavior analysis. Carrying out fine feature extraction by adopting a space and channel attention mechanism; fine features are combined with Kalman filtering and a Hungary algorithm, chicken individual identities are continuously tracked and adjusted through time sequence prediction and space matching, and a target bounding box is output; adjusting the individual identity of the chicken according to tracking, performing smooth repair on interrupted and shielded trajectory segments through spline interpolation and Kalman filtering, and performing reasonable trajectory recovery by adopting biological kinematics constraint; based on the target bounding box, a frequency domain feature analysis mechanism and a deep learning coding technology are introduced, a behavior feature vector is generated, and health early warning is triggered according to the abnormal confidence coefficient; according to the method, the chicken monitoring precision and the anomaly detection reliability are improved.

This utility model discloses an intelligent AI golf teaching camera, including a front shell and a rear shell, which are connected by a snap-fit. The front shell has a fixed glass panel and a camera bracket installed inside. The bracket has a posture camera and a trajectory camera tilted on it, which are used to collect the swing action and the ball trajectory, respectively. The outer side of the front shell has an elliptical conical opening that matches the camera, forming an "eyelash"-like anthropomorphic design. The motherboard integrates a BirdieCoach hardware module, which completes the core AI calculation through a local main control unit. The mobile phone is only used as a display and interaction medium. The rear shell has a fan bracket that holds the battery. The fan is close to the motherboard and the battery, and together with the bottom ventilation port, it forms an active heat dissipation channel. The rear shell has a built-in magnetic ring for easy installation. The motherboard integrates an IMU sensor to calibrate the tilt state of the device in real time. This design achieves high-precision motion analysis and teaching guidance through dual-camera collaboration, local AI calculation, and emotional interaction, and is portable, has efficient heat dissipation, and strong user stickiness.

The invention discloses a handheld net capture launching system for low-altitude target trajectory prediction, and relates to the technical field of anti-unmanned aerial vehicles, and the technical scheme is characterized in that the method comprises the steps: obtaining the image information of an unmanned aerial vehicle, and obtaining the position, speed information and acceleration information of a target unmanned aerial vehicle through the motion analysis of continuous frames; and inputting the position, speed information and acceleration information of the unmanned aerial vehicle into a Kalman filtering algorithm to obtain a predicted trajectory of the unmanned aerial vehicle, and calculating a predicted net capturing position after delta t time. According to the visual guidance provided by the invention, the target trajectory prediction result is visually presented to the user, so that the operation difficulty of the equipment is reduced, and non-professionals can also quickly master and accurately hit the target.

This invention provides a video matching method for uncertainty-aware navigation in ophthalmic surgery, comprising: performing anatomical structure segmentation and neighboring frame fusion matching based on ophthalmic surgical video sequences to obtain a highly reliable neighboring frame matching library; performing motion analysis and frame pair selection and pairing based on a full-frame anatomical segmentation mask to obtain a globally selected set of difficult sample frame pairs; constructing a spatial lookup table to generate a pseudo-label training dataset; performing uncertainty-guided contrastive learning to obtain a final uncertainty-aware matching network; calculating the final filtered rotation angle using a reference frame and the current frame; and updating the incision position, capsulorhexis range, and intraocular lens implantation axis to obtain a real-time superimposed navigation visualization result. This invention can significantly reduce the algorithm training cost, enhance robustness in complex surgical environments, solve occlusion and deformation interference, effectively suppress error accumulation in semi-supervised learning, and improve the absolute accuracy of matching.

The invention discloses a golf intelligent glasses system, which comprises a sensor assembly at least comprising a camera, an inertial measurement unit IMU, a millimeter wave radar and a microphone array; the processing module is in communication connection with the sensor assembly and is used for executing environment perception, interaction recognition and golf motion analysis; the processing module comprises a multi-mode depth estimation unit used for fusing the visual and inertial data to generate environment depth information, and a golf AI analysis unit used for generating a ball hitting strategy; the display assembly is in communication connection with the processing module and comprises an optical waveguide lens and a driving unit; the driving unit is configured to drive the optical waveguide lens to display augmented reality (AR) guidance information to a wearer and to display a dynamic visual effect to an external environment. According to the invention, laser-free environment perception and personalized ball hitting strategy generation are realized, through AR guidance, double-sided display and natural interaction, the training efficiency and the use experience are improved, and the professional application of intelligent wearable equipment is expanded.

The invention discloses a motion analysis and feedback method and device and a computer readable storage medium, and the method comprises the steps: building a neutral position reference model based on the skeleton features of a user, and calculating the instantaneous deviation features of a real-time posture and the reference model; resolving a time domain accumulation feature representing the stubborn degree of the action error and a time domain change rate feature representing the deterioration trend of the action error in real time; retrieving the kinematic chain knowledge graph to obtain attribution information; inputting the features into an adaptive feedback control model, dynamically calculating abstract hierarchy parameters of a teaching strategy based on the magnitude of time domain accumulation features, respectively calculating mood specificity parameters and intervention opportunity parameters in combination with instantaneous deviation features and time domain change rate features, and generating prompt control parameters; and finally, system prompts are assembled based on the prompt control parameters, and self-adaptive feedback information is generated. According to the application, the crossing from mechanical error correction to adaptive closed-loop teaching can be realized.

The invention comprises a device and a method for detecting and evaluating the movements of a person over time and for generating feedback to the person, comprising detecting the person by means of a motion detection sensor, generating an observation point model of observation points for the detected person, evaluating the detected movements with regard to defined movement deviations in a first time interval, and outputting feedback to the person by means of an output unit in a second time interval following the first time interval regarding the determined movement deviation.

The application provides a vehicle detection method based on millimeter wave radar and video data motion analysis, and aims to solve the problem that a single sensor cannot accurately detect vehicles in multiple scenes and all-weather conditions. The method comprises the following steps: collecting and calibrating video and millimeter wave radar data; generating a lane hotspot map based on multiple millimeter wave radar data; a radar and video fusion strategy based on target motion state analysis; calculation and transmission of traffic statistics; and pushing of real-time video stream. The application uses a more reasonable processing method, which improves the accuracy and robustness of target detection through motion analysis and adaptive fusion of millimeter wave radar and video data, is suitable for low-power edge computing devices, and reduces the cost of the device. At the same time, the traffic flow, queue length, parking frequency, coil existence and other statistics are balanced.

Devices, systems, and techniques for analyzing video information to objectively identify patient behavior are disclosed. A system may analyze obtained video information of patient motion during a period of time to track one or more anatomical regions through a plurality of frames of the video information and calculate one or more movement parameters of the one or more anatomical regions. The system may also compare the one or more movement parameters to respective criteria for each of a plurality of predetermined patient behaviors and identify the patient behaviors that occurred during the period of time. In some examples, a device may control therapy delivery according to the identified patient behaviors and / or sensed parameters previously calibrated based on the identified patient behaviors.

The invention discloses a synchronous control system for multi-channel small lamp sequential light emitting and three-line array camera synchronous shooting and a time delay compensation method thereof, and the system comprises a high-performance control module, a time delay detection module, a small lamp driving module, a camera triggering module, a parameter storage module and a man-machine interaction module. According to the time delay compensation method, multi-scale delay modeling, dynamic prediction and layered closed-loop control principles are combined, a real-time time delay characteristic function is established by using exponential weighted filtering and an autoregression prediction model, error self-correction is realized through proportional-integral adaptive control, and an outlier rejection mechanism is introduced to suppress noise interference. The system can be applied to scenes such as industrial visual detection, scientific experiment high-speed imaging, medical visual acquisition and motion analysis, the problem of synchronization errors caused by delay drift and signal coupling in a multi-channel system is effectively solved, stable synchronization of the system under the high-frequency triggering condition is achieved, and the errors are controlled within + / -5 microseconds.

A motion analysis device (100) is provided with: a joint position information acquisition unit (103) that acquires joint position information indicating the positions of M joints (M is an integer of 2 or more) of a worker included in an image; a work specifying unit (105) that uses one of one or a plurality of learned models for estimating the work being performed by the person from at least the positions of the joints of the person, estimates the work being performed by the worker from the positions of the M joints, and specifies the work being performed by the worker from the result of the estimation; and a use object selection unit (106) that selects N joints from the M joints if the estimated reliability is less than a predetermined threshold value (N is an integer satisfying 1 < = Nlt; when the estimated reliability is less than a predetermined threshold value, the work specifying unit (105) uses one of the one or a plurality of learned models to re-estimate the work being performed by the worker on the basis of the object to be used, using the positions of the N joints as the object to be used, and if the estimated reliability is less than the predetermined threshold value, the work specifying unit (105) uses one of the one or a plurality of learned models to re-estimate the work being performed by the worker on the basis of the object to be used.

To provide a motion analysis system, a motion analysis method and a program capable of accurately evaluating a posture of a person performing a motion.SOLUTION: The frame image extraction section 64 extracts a first frame image representing a first scene and a second frame image representing a second scene. An object position specification part 66 specifies a position where the first object is shown in the first frame image and a position where the second object is shown in the second frame image. The evaluation process execution unit 70 evaluates the posture of the person in the second scene based on the position of the first object in the first frame image and the position of the second object in the second frame image.SELECTED DRAWING: Figure 7

An object of the system according to the embodiment is to check a health condition by comprehensively analyzing daily health data and video data.SOLUTION: A system includes a data storage part, a health condition check part, a video recording part, and a motion analysis part. The data storage unit stores data of a meal amount, an exercise amount, and excretion. The health condition check unit checks a health condition using the data accumulated by the data accumulation unit. The video recording unit records daily video data. The motion analysis unit analyzes the video data recorded by the video recording unit to detect a change in motion or an injury.SELECTED DRAWING: Figure 1

The invention relates to a panda behavior recognition method based on deep learning and time sequence prediction, and the method comprises the steps: S1, constructing a panda behavior deep learning recognition model which comprises a region division module, a multi-view motion analysis module, a multi-scale posture estimation module and a posture guide fusion module; s2, carrying out region division and part coding on the input video, and generating a part coding video comprising three regions, namely a panda head region, a panda whole region and a background region; s3, performing multi-view motion analysis on the part coding video, and extracting multi-view motion features of the panda; s4, extracting multi-scale features of the joint attitude information of the panda in the part coding video, and performing feature fusion to generate an attitude thermodynamic diagram; and S5, taking the posture features in the posture thermodynamic diagram as guide signals to be fused with the multi-view action features, and outputting the giant panda behavior category. According to the method, the accuracy and robustness of giant panda behavior monitoring can be improved.

We provide the system. [Solution] Means for receiving information regarding body shape and constitution, A means for analyzing motor movements from images or videos, A means for generating an optimized exercise instruction menu based on the results of exercise analysis, A means of providing the generated exercise instruction menu to the user, A means of updating the exercise instruction menu based on the image or video data received again, A means of evaluating and providing feedback on user behavior in real time, A system that includes this.

The invention discloses a mine disaster self-rescuer AI video identification practical training system, which belongs to the technical field of coal mine safety simulation, and comprises a shooting simulation module for simulating an underground environment, an operator wearing a mine disaster self-rescuer in the simulated environment, shooting a wearing process and a simulation process, and carrying out real-time video identification on the mine disaster self-rescuer. The video optimization module optimizes the video frame image with poor image quality, and the action analysis module performs key point detection and action recognition on the optimized video to obtain the operation steps of an operator and judge whether the action is completed or not; the multi-dimensional scoring module constructs a quantitative scoring model according to the time dimension, the action specification dimension and the component operation dimension, and scores the operation process of the operator, so that the operator can practice in an environment close to reality, the operation proficiency and psychological quality of the operator in an emergency scene are improved, and the user experience is improved. Emergency operation errors caused by insufficient environment adaptation are avoided, the action judgment accuracy is improved, and a comprehensive and objective quantitative basis is provided for practical training effect evaluation.

The invention discloses a method and device for automatically switching on and off linkage equipment through AI monitoring of a target object, and relates to the field of intelligent Internet of Things control. The method comprises the following steps: S1, collecting a video stream and multi-source sensing data for preprocessing to obtain a time sequence image frame set and an environment state basic parameter; s2, calculating a robust background disturbance index based on the time sequence image frame set and the environmental state basic parameters; s3, performing motion analysis based on the robust background disturbance index to obtain a target candidate region and calculating a semantic structure consistency index; s4, on the basis of the target candidate region and the semantic structure consistency index, recognizing a behavior event in combination with a robust background disturbance index, and calculating the behavior credibility; and S5, generating an equipment control instruction based on the behavior event and the behavior credibility. By constructing a robust background disturbance index and a semantic structure consistency index, intelligent evaluation and self-adaptive decision on environment disturbance and target authenticity are realized, and the accuracy and reliability of an automatic switching system in a complex environment are improved.

The invention relates to the technical field of mine safety monitoring, in particular to a gas sensor violation operation real-time monitoring method based on multi-source information fusion, and provides a monitoring scheme based on double fusion of an OCR technology and a target detection technology. According to the method, real-time images of a sensor working area are collected through a high-definition camera, sensor readings are recognized through an OCR model of a GAN network, YOLOv8 and CRNN fusion architecture, the readings change rate is calculated through a mathematical formula to judge the abnormal condition, when abnormity is detected, a YOLOv8X target detection model is started to analyze video frames, and the abnormal condition is detected. And illegal behaviors such as air inlet shielding, air pipe interference, illegal adjustment and the like are identified. The system accurately judges violation operation through algorithms such as continuous frame motion analysis and shielding rate calculation, saves evidence in time and gives an alarm. According to the method, single data monitoring is converted into behavior data state total factor auditing, the problem of gas data distortion caused by man-made interference is effectively restrained, and the accuracy and reliability of mine gas monitoring are remarkably improved.

This application discloses a motion standardization counting system and motion counting device, relating to the field of motion recognition technology. The system includes: a motion detection module for acquiring user training motions and filtering target motion data from the training motions based on preset motion data; a motion analysis module for receiving target motion data and comparing it with preset motion requirements to generate standardization analysis results; a counting module for receiving target motion data and counting the motions after each received data point to obtain counting information; and an output module for outputting motion feedback information to the user based on the standardization analysis results and the counting information. Compared to existing counting devices that only accumulate the number of motions, this application performs multi-parameter standardization comparison and real-time analysis of the filtered motion data through preset motion requirements, thereby achieving an objective evaluation and real-time feedback of the user's motion execution quality.

This invention relates to the field of thickness visual measurement technology, specifically to a method and system for detecting icing on power transmission lines based on unmanned aerial vehicles (UAVs). The method acquires a continuous video stream, performs global motion compensation using feature point matching, analyzes the temporal grayscale changes of pixels after image stabilization to extract a photometric fluctuation map, locates the insulator string connection point physically connected to the conductor as the source point, performs diffusion connectivity analysis in the wavefront propagation velocity field constructed by the photometric fluctuation, filters out non-connectivity interference based on the difference in connectivity propagation time, and obtains the conductor region. Within the conductor region, the cross-sectional width is measured, and the actual physical icing thickness is calculated. This invention integrates video stabilization, temporal micro-motion analysis, and spatial topological connectivity physical constraints, improving detection accuracy in environments with weak texture and dynamic interference, achieving a more accurate quantitative perception of the true state of iced conductors in complex environments, and providing more reliable data support for the operation and maintenance of power systems with icing.

The application discloses a kind of railway dangerous rock rockfall three-dimensional numerical simulation calculation methods based on remote sensing technology, comprising the following steps: obtaining target area remote sensing image and digital elevation data and engineering data;Build three-dimensional space physical motion analysis model;Calculate dangerous rock rockfall three-dimensional migration path;Determine dangerous rock rockfall prevention parameter;Dangerous rock rockfall three-dimensional simulation achievement display.The railway dangerous rock rockfall three-dimensional numerical simulation calculation method based on remote sensing technology of the application can accurately calculate the three-dimensional migration path of dangerous rock rockfall by remote sensing and GIS technology, obtain the prevention parameter of dangerous rock rockfall, and can be used to determine the protection range, strength and height of the blocking project, while realizing the three-dimensional simulation achievement display of dangerous rock rockfall, not only high accuracy, simple and feasible method, and low physical cost investment, strong engineering practicability.

The invention discloses a cold region riverway ice tray diving accumulation amount monitoring method comprising the following steps: obtaining remote sensing images, and determining the extension length of an ice plug body by obtaining the front end positions of the ice plug body at different moments; collecting a time sequence image of an upstream flow-ice river section, extracting flow-ice intensity in combination with an image recognition algorithm, and interpreting flow-ice average flow velocity through a motion analysis algorithm; and calculating the equivalent area of the flow ice passing through the section in unit time, subtracting the surface accumulation area, performing correction in combination with the thickness of the ice tray and the porosity to obtain the actual under-ice accumulation volume, and performing inversion on the underwater accumulation thickness based on an accumulation geometric model. According to the method, non-contact, safe and dynamic quantification of the submerging ice amount and the accumulation thickness of the ice plug body is achieved, the technical problems that traditional radar detection is interfered by water, the manual measurement risk is high, and longitudinal accumulation parameters are difficult to obtain are solved, and data support is provided for ice flood early warning and emergency dispatching.