320 results about "Motion analysis" patented technology

The invention relates to the technical field of image recognition, in particular to an image processing method and system for rehabilitation training action analysis. According to the method, a multi-view image sequence is collected based on a binocular camera device, skeleton key point data of a user in a training process is extracted by utilizing a three-dimensional attitude reconstruction technology, and an action three-dimensional time sequence data set is constructed. The method comprises the following steps: firstly, constructing an individual standard power generation characteristic model of a user, modeling a skeleton driving path of a main muscle group, and forming a personalized power generation reference structure; and then a standard rehabilitation action path is matched through a dynamic time warping algorithm, and the attitude deviation under the key frame is identified. And the system dynamically compares the identified non-standard motion mode with the individual model, judges whether abnormal force generation exists or not and outputs the type and the part of the muscle compensation behavior. And finally, multi-modal feedback information with highlighted graphs, voice prompts and character suggestions is generated in combination with an identification result, so that the identification precision and personalized guidance capability of rehabilitation training are remarkably improved.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based motion analysis system which comprises a multi-source data acquisition module, a space-time preprocessing module, a feature fusion module, a motion mode recognition module, an anomaly detection module and a feedback generation module. The multi-source data acquisition module synchronously acquires data at a multi-band sampling rate by means of an inertial measurement unit, an optical sensor and a depth camera array, generates an original motion data set and stores the original motion data set to the distributed edge nodes; the space-time preprocessing module uses a multi-scale dynamic time warping algorithm and adaptive Kalman filtering to calibrate noise reduction; the feature fusion module extracts and fuses key spatio-temporal features; the motion mode recognition module and the anomaly detection module accurately judge a motion mode according to the fused feature tensor and position anomaly; the feedback generation module generates a personalized executable instruction set according to the results, and provides powerful support for the athletes to dig potential and break through bottlenecks.

The invention relates to the technical field of computer vision data analysis, in particular to an agent training analysis algorithm based on a large model. The computer vision technology is used for extracting bone key points of officers and soldiers from videos, and accurate action analysis is carried out. And deep learning, especially a convolutional neural network CNN and a generative adversarial network GAN, is used for improving the accuracy of skeleton key point recognition and action recognition. The physical training technology comprises an officer and soldier training method and effect evaluation, a traditional method depends on observation of a coach, and a modern technology can improve the training efficiency and quality through video analysis, especially in a training scene needing accurate action analysis. The video analysis technology serves as a core link and is responsible for extracting action features from video data. Through advanced algorithms and models, the feature data are processed and analyzed, and detailed feedback and improvement suggestions are provided, so that coaches and officers and soldiers are helped to optimize training effects.

The invention relates to the technical field of image enhancement, in particular to an ultrasonic image enhancement and motion analysis system for real-time uterine contraction detection. The system comprises the steps of obtaining ultrasonic images of different frames of uterine contraction detection, determining uterine contraction evaluation characteristic values according to gradient directions and position distribution of image angular points, performing screening to obtain target characteristic points, performing similarity analysis according to motion vectors of the target characteristic points in a neighborhood, determining continuous coefficients, performing clustering according to the continuous coefficients, and performing clustering according to different clustering clusters. Analyzing the uterine contraction change track in the time sequence, obtaining a uterine contraction rule change value of each target feature point, and obtaining an enhancement adjustment coefficient of each target feature point according to the uterine contraction rule change value of the target feature point and the uterine contraction evaluation feature value; and performing image enhancement on the uterine contraction detection ultrasonic image based on the enhancement adjustment coefficient. According to the invention, adaptive image enhancement can be realized, the motion trail of uterine contraction can be displayed more clearly by the enhanced image, and the imaging effect is improved.

The invention discloses a 3D human body posture estimation method based on a diffusion model. According to the method, firstly, a continuous human body dynamic video is split into RGB images, two-dimensional coordinate information, embedded time steps and positions of all joint points of a human body are extracted from the RGB images through a 2D posture detector, then predicted 3D postures are output through a diffusion model, and low-frequency key information is reserved and high-frequency noise is filtered in combination with discrete cosine transform (DCT), so that the 3D postures are obtained. And the calculation complexity is greatly reduced. And based on a multi-hypothesis aggregation strategy of confidence and consistency evaluation, weighted fusion is performed on the predicted 3D postures, so that the precision and robustness of posture estimation are improved. The performance of the model in a dynamic scene is further enhanced by a time step embedding mechanism and time consistency scoring, so that the model adapts to a complex motion environment and a shielding condition. The method is low in computing resource demand, is suitable for being deployed on resource-constrained equipment in the fields of human-computer interaction, virtual reality, motion analysis, medical rehabilitation and the like, and has a wide application prospect.

The invention relates to the technical field of panoramic video processing, in particular to a panoramic video stability enhancement method based on space-time consistency, which comprises the following steps: acquiring a panoramic video frame sequence; obtaining spherical projection coordinates of each frame in the panoramic video frame sequence; based on the spherical projection coordinates, a deep learning model is constructed, and the deep learning model comprises a feature extraction encoder and a motion prediction compensation decoder; performing feature extraction on the panoramic video frame sequence by using a feature extraction encoder; performing motion prediction and compensation by using a motion prediction compensation decoder based on a feature extraction result; constructing a space-time filter according to the motion prediction and compensation result, and smoothing the motion vector; based on the smoothed motion vector, performing multi-frame motion analysis by using a long time sequence network; according to a multi-frame motion analysis result, designing panoramic video optical flow motion estimation; performing stable enhancement processing on the panoramic video frame sequence based on an optical flow motion estimation result; and outputting the stably enhanced panoramic video.

The invention relates to the technical field of action analysis, in particular to a laser targeting training management system which comprises an immersive training environment module, a biological characteristic analysis module group, an intelligent trajectory management module group, a federal learning center module group, a tactical training management module group and a command and control system module group. Compared with the limitation of lack of battlefield dynamic complexity simulation by adopting a static preset scene in the prior art, the scheme deeply integrates multi-physical field environment control, intelligent target dynamic coordination and a cross-sensory feedback technology to construct an immersive battlefield space with temperature and humidity gradient adjustment, explosion shock wave simulation and geographic space accurate reconstruction linkage; therefore, multi-dimensional collaborative transition from single-dimensional visual simulation to tactile sense / auditory sense / olfactory sense / somatosensory is realized, so that soldiers can obtain subversive environment adaptability and tactical decision-making ability exercises in a comprehensive battlefield pressure environment close to reality.

The invention relates to the technical field of image recognition, in particular to a smart home scene interaction method based on an image recognition technology, which comprises the following steps of: capturing real-time image data of a resident based on a camera, and extracting pixel points and color depth information in an image to obtain initial posture data; and based on the initial attitude data, an OpenPose algorithm is applied to detect a joint position, the attitude of the resident is identified, and a joint position analysis result is generated. According to the invention, based on the motion trail analysis of the time sequence, the system can analyze the conversion process of different postures, and the stability of behavior classification is improved. And joint motion analysis is adopted, so that classification of behavior states is more detailed, different subtle motion changes under the same category can be recognized, and misjudgment is reduced. In combination with an environment parameter dynamic adjustment strategy, the system can perform personalized adjustment according to a user state instead of depending on a fixed rule to perform equipment control.

The invention relates to the technical field of video monitoring, in particular to a cloud-based video behavior monitoring system, which comprises a video bandwidth control module, a behavior calculation scheduling module, a monitoring area adjustment module, an environment task optimization module and a monitoring resource allocation module. According to the invention, through real-time bandwidth state monitoring and data analysis, dynamic matching of video data volume, improvement of stability and quality of video transmission, optimization of task scheduling accuracy based on pixel motion analysis and target activeness evaluation, reasonable distribution of monitoring resources, statistics of behavior density and judgment of trend are realized, and priority uploading of high-density area data is ensured. According to the method and the system, monitoring data imbalance is avoided, network flow detection and bandwidth recovery identification are combined, intelligent task sorting is realized, the resource utilization rate is improved, low-priority task allocation is optimized through calculation load detection and resource analysis, so that monitoring is stably operated in a high-load environment, and the real-time performance, the intelligent level and the accuracy of abnormal behavior detection are improved.

The invention relates to a sports injury real-time early warning and health optimization system based on multi-mode infrared thermal imaging and three-dimensional skeleton posture fusion, and belongs to the field of sports health management. The problems of motion analysis lagging, damage identification missing and cross-scene data splitting of a traditional monitoring means are solved. According to the technical scheme, the system comprises a multi-modal motion data sensing module which synchronously collects human thermodynamic distribution, three-dimensional skeleton postures and surface electromyogram signals through an infrared thermal imaging camera, an RGB-D camera and a bioelectric sensor; an AI driving training optimization engine is used for generating an adjustment instruction in real time based on a muscle thermal gradient and joint trajectory deviation degree dual-threshold model; and the full-scene intelligent management platform executes millisecond damage early warning, metabolic optimization and school-family health data closed-loop management. The technical effects are that the exercise safety guarantee is fundamentally improved, the training is scientifically optimized, the metabolism management accuracy is broken through, and the teaching resource allocation is intensive.

The invention discloses a Pick ball athlete action analysis method based on computer vision, and belongs to the technical field of data processing. Standard action videos corresponding to action types of Pick ball athletes are collected by arranging a camera, and the standard action videos are analyzed and learned by utilizing a computer vision technology; according to the technical scheme, the motion analysis method and the motion analysis device, the model capable of analyzing the motion of the Pick ball athlete is formed, then the to-be-analyzed video data of the to-be-analyzed Pick ball athlete is collected, the to-be-analyzed video data is analyzed through the obtained model, and finally the motion analysis result is obtained, so that the normalization of the technical motion can be effectively evaluated; scientific training suggestions are provided for athletes, and athletic performance is improved.

The invention relates to the technical field of image motion analysis, in particular to a rectal cancer radiotherapy effect dynamic monitoring system combined with iconography analysis. Determining a reference recovery index representing the recovery effect from the dimensions of edge change and area change according to the characteristic that the lesion area becomes smaller and smaller along with the better and better treatment effect; then, based on the reference recovery index and in combination with the characteristic that rectum tissue circularity can cause gradual inward contraction of a lesion area and the edge is smoother and smoother, determining a more accurate shape recovery degree representing the reference recovery index; therefore, the corrected radiotherapy recovery curve is corrected more accurately according to the shape recovery degree, and dynamic monitoring of the rectal cancer radiotherapy effect is more visual and accurate.

The invention discloses a hand function evaluation method, device and equipment and a computer readable storage medium. The method comprises the steps that at least one frame of hand motion image of an evaluated person is acquired through a first image acquisition module; processing the hand motion image through a posture estimation algorithm model carried by the motion analysis model to obtain a hand key point coordinate, and recognizing a gesture in the hand motion image based on the hand key point coordinate through a classification model to obtain a first evaluation result; acquiring a hand grip image of the evaluated person under the counter-acting force of the gesture interaction equipment through a second image acquisition module; detecting the hand grip strength image of the evaluated person through a target detection algorithm model carried by the motion analysis model so as to judge whether the evaluated person can resist the counter-acting force of the interaction equipment or not, and obtaining a second evaluation result; the hand function of the evaluated person is evaluated based on the first evaluation result and the second evaluation result, and the motion function of the evaluated person can be scientifically evaluated.

The invention provides virtual reality-based dance teaching equipment and a teaching method thereof, and the equipment comprises a VR module which is used for providing multi-view immersive visual experience; the motion capture module is used for capturing the joint angle of the dancer in real time and the limb track feedback module and is used for simulating the ground counter-acting force in dancing through vibration or pressure, enhancing motion perception and providing a touch prompt for motion correction; the virtual scene module is used for creating a dance virtual scene; and the action analysis module analyzes the joint action deviation based on multiple view angles, generates an instant score according to the joint angle deviation and the rhythm synchronization rate, and records historical data to generate a progress curve. According to the method, the problems of resource shortage, feedback lag and other pain points in traditional dance teaching are solved, efficient, personalized and interesting dance learning experience is achieved through multi-modal interaction and a dynamic adjustment mechanism, and the application of the method is expected to promote dance education to develop in the intelligent and universal direction.

The invention discloses a rapid sparse map reconstruction method and system based on a video image, belongs to the technical field of video image processing, and realizes rapid sparse map reconstruction in a dynamic environment through motion analysis, key frame extraction, space-time diagram convolutional network feature extraction, image segmentation and multi-view fusion. Technicians can correct the map in real time through the interactive interface, automatically adjust the weight according to feedback, and provide personalized correction suggestions. According to the method, the map reconstruction speed and accuracy are optimized, the dynamic environment adaptability is enhanced, the practicability and individuation of the map are improved through user interaction, and the method has remarkable technical progress and practical value.

Systems, methods, and frameworks are provided for privacy-aware weapon anomaly detection via integrated object recognition and skeletal motion analysis. This framework integrates real-time object detection and motion analysis to identify weapon anomalies in video surveillance while preserving privacy. The framework combines a fine-tuned object detection model, a head-segmentation module for anonymizing unarmed individuals, and a skeleton-based motion analysis module to detect threatening behaviors. By refining and fusing detection and motion analysis outputs, the framework enhances detection accuracy and reduces false positives, thereby providing a reliable solution for intelligent, privacy-preserving surveillance applications.

The present invention relates to an intelligent wooden man robot and motion analysis system capable of measuring skill. The system analyzes the weights of the hitting points in each area of ​​the wooden man according to the distance from the key hitting point; uses a pressure sensor to obtain the force data of hitting the wooden man; monitors the hitting points and force of the wooden man, and calculates the hitting skill value in combination with the weights of the hitting points in each area of ​​the wooden man; uses a K-means cluster analysis method to obtain the skill level of different hitting skill values, and emits lights of different brightness at the hitting points according to different skill levels; extracts the image feature data of the hitting action video stream, compares it with the standard action image feature data, and evaluates the trainee's action in combination with the corresponding skill level; and emits lights of different colors according to the evaluation results of the hitting action. The present invention solves the problem that it is difficult to interact with lights according to the accuracy of different hitting parts of the wooden man in the prior art to accurately evaluate the trainee's skill and action.

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 generally relates to motion analysis systems and methods of use thereof. In certain aspects, the system includes an image capture device, at least one accelerometer, and a central processing unit (CPU) with storage coupled thereto for storing instructions that when executed by the CPU cause the CPU to receive a first set of motion data from the image capture device related to at least one joint of a subject while the subject is performing a task and receive a second set of motion data from the accelerometer related to the at least one joint of the subject while the subject is performing the task. The CPU also calculates kinematic and / or kinetic information about the at least one joint of a subject from a combination of the first and second sets of motion data, and outputs the kinematic and / or kinetic information for purposes of assessing a movement disorder.

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 flexible riser motion analysis, in particular to a marine dynamic flexible riser motion response reconstruction method based on monitoring data, which comprises the following steps of: arranging sensors in a flexible riser system by adopting a structure embedded type arrangement method or an external attachment type arrangement method, and optimizing a sensor arrangement mode; a data acquisition and transmission system is arranged on an ocean platform to obtain flexible riser motion response data, the flexible riser motion response data is converted into flexible riser displacement data based on a Timoshenko beam model and Tikhonov regularization, the flexible riser displacement data is subjected to noise reduction processing based on a wavelet threshold noise reduction method to obtain flexible riser displacement noise reduction data, and the flexible riser displacement noise reduction data is subjected to flexible riser displacement noise reduction. And motion response reconstruction is performed on the flexible riser displacement noise reduction data based on variational mode decomposition (VMD). According to the method, the data interpretability, the data reconstruction precision and the real-time performance of motion reconstruction of the flexible riser can be effectively 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 power line fog penetration monitoring method, system and device based on polarization-infrared multi-mode fusion and a medium. An atomic clock is tamed to trigger infrared pulse and polarization exposure, and pixel offset caused by jitter of an unmanned aerial vehicle is compensated through an image motion analysis method; constructing a polarization physical model, and establishing a relationship between the fogdrop particle size and the polarization degree; performing multi-modal feature fusion, constructing a double-branch deep learning network, and performing output judgment on the image according to a dynamic fusion strategy; and optimizing conductor features and insulator features in the power line through a special convolution kernel, and accurately extracting target features.

The invention discloses a monitoring video transmission method for downhole fracture acidification field command, particularly relates to the field of video coding adaptive adjustment, and is used for solving the problem of weak intelligent response capability of monitoring video transmission under the existing remote well site condition. The method comprises the following steps: acquiring the position of a monitoring device and establishing a binding relationship between a monitoring video channel and an operation well; a fracturing accident identification model is trained based on the historical pumping pressure curve, and the accident risk of the operation well is monitored in real time; selecting a corresponding video channel and initiating link bandwidth tilt scheduling for the identified accident risk well; acquiring a video frame sequence of the channel, and judging a video coding guide type corresponding to the accident type through inter-frame motion analysis; a matched code reconstruction instruction is generated according to the guiding type and is issued to a corresponding channel; and uploading the reconstructed video to a remote command end, and recovering the bandwidth configuration and coding parameters of the video channel after the accident risk is removed.

The invention relates to the field of industrial robot visual servo control, in particular to a visual servo grabbing dynamic compensation system. The system comprises an image acquisition module, a motion analysis module, a local modeling module, a prediction module and a compensation execution module. A target image sequence is continuously captured, a motion track is extracted, a non-parametric local motion model is established based on recent data, the position of a target at the future grabbing moment is predicted, and a compensation signal is generated according to a prediction result to adjust the grabbing track of a robot. According to the method, the motion model is constructed by adopting a local weighted regression method, accurate description and rapid adaptation of various motion modes are realized through adaptive bandwidth adjustment and an online updating mechanism, and the grabbing success rate of a moving target is effectively improved.

Face recognition systems are vulnerable to the presentation of spoofed faces, which may be presented to face recognition systems, for example, by an unauthorized user seeking to gain access to a protected resource. A face liveness detection method that addresses this vulnerability utilizes motion analysis to compare the relative movement among three regions of interest in a facial image and based upon that comparison to make a face liveness determination.

The invention relates to the technical field of flow measurement, in particular to a sewage pipeline flow measurement device and method based on dynamic image analysis, and the device comprises a rod body, a control system, a particle release module, a track acquisition module and an image and motion analysis module; the control system is arranged at the top end of the rod body; the track acquisition modules are arranged at the bottom of the rod body; the particle release module is used for storing fluorescent particles and releasing the fluorescent particles into the sewage pipeline to be detected under the control of the control system; the track acquisition module is used for shooting the interior of the sewage pipeline to be detected to obtain an image containing fluorescent particles; the image and motion analysis module is used for analyzing the motion characteristics of the fluorescent particles released in the sewage pipeline to be detected so as to determine the flow of the sewage pipeline to be detected. Thus, the bottom of the device is placed in the sewage pipeline to be detected, the motion characteristics of the fluorescent particles are determined by shooting the tracks of the fluorescent particles, the flow of the sewage pipeline is determined, and the device is efficient, accurate and low in operation difficulty.

The invention discloses a lag elimination method and device for a vehicle anti-dazzling system, equipment and a medium, and relates to the technical field of vehicle anti-dazzling systems, a trained target object detection model is utilized to detect a shot image at the current moment, and a target object detection result at the current moment is obtained. Further combining with a target object detection result at the previous moment, matching the target object at the current moment with the target object at the previous moment, determining a matched target object at the current moment, performing motion analysis on the matched target object at the current moment, determining the speed of the matched target object at the current moment, and when lagging elimination needs to be performed, determining the speed of the matched target object at the current moment. And determining the final shielding position of the matched target object based on the position, the speed and the corresponding shielding position of the matched target object, the reasonable shielding position is determined by combining target object detection, target object tracking and target object motion analysis, the problem of light beam adjustment lag caused by insufficient dynamic response speed can be solved through lag elimination, and the accuracy of light beam adjustment is improved. The driving safety is improved.

The invention discloses an anti-intrusion alarm system based on a laser ranging technology, and relates to the technical field of laser ranging, a data acquisition module of the system collects a motion track, a dynamic video and environment information of a target, constructs a target motion model by using a two-dimensional coordinate system, processes a video frame image, and obtains target motion data and a target frame picture; after feature extraction, obtaining target feature data, and preprocessing the environment data, the target trend data and the target feature data to obtain a target risk data set; and carrying out feature analysis, motion analysis and environmental influence analysis according to the target risk data set, calculating an intrusion judgment evaluation index fin to carry out preliminary intrusion behavior evaluation, executing target trend evolution analysis when an evaluation result is consistent with an intrusion feature, and carrying out deep intrusion behavior evaluation by calculating a comprehensive behavior evaluation index zxp. The system has the advantages of high precision, high intelligence and low false alarm rate, and can significantly improve the effect and response capability of anti-intrusion monitoring.