267 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 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 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 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 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.

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 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 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 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.

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.

Systems, methods, and computer-readable storage devices are disclosed for improving markerless motion analysis of an object in contact with a user. One method including: receiving position data of an object in motion captured by at least one camera; enhancing, using model equations, three-dimensional (3D) angular kinematic data of the position data of the object, wherein the enhanced 3D angular kinematic data includes increased measurement accuracy of the position data of the object; and providing the enhanced 3d angular kinematic data for display to evaluate motion performance of the object.

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 computer vision and neuromorphic calculation, in particular to a memristor-based motion information extraction and fast optical flow calculation system, which comprises a front-end imaging module, a rear-end imaging module, a memory module and a signal processing module, and is characterized in that the front-end imaging module is used for acquiring a visual scene and converting the visual scene into an analog voltage signal; the voltage conversion module is used for extracting a light intensity change condition based on the analog voltage signal; the neuromorphic module is used for coding time dynamic information of a visual scene according to the light intensity change condition; and the signal processing module is used for extracting a time motion clue based on the time dynamic information and carrying out optical flow calculation on a corresponding region of the time motion clue. According to the invention, efficient motion analysis can be realized, and the optical flow calculation speed is 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 provides an intelligent image stabilization method and system for an unmanned intelligent turntable. According to the method, an interference library is updated by acquiring electromagnetic interference data, and a signal-to-noise ratio is monitored to generate notch filtering parameters. Based on the parameters, a wave trap is adjusted, non-linear compensation and frequency domain equalization are synchronously triggered, image key frame abrupt change signals are identified at the same time, compensation intensity is adjusted, and closed-loop suppression is formed. In a closed loop, an inner loop extracts high-frequency jitter, an outer loop fuses interference prediction to generate primary parameters, and a cross-domain suppression instruction is output after motion analysis. And instruction-driven anti-interference coding: processing rotation suppression by an attitude coding scheme, processing electromagnetic suppression by a hierarchical error correction protocol, and performing closed-loop verification through link feedback. A verification result is fed back to update the interference library and the compensation intensity, cooperative control of electromagnetic interference and mechanical jitter is achieved, and a stable image is output. By constructing a dynamic closed-loop suppression mechanism, electromagnetic interference and mechanical jitter of the rotary table are cooperatively suppressed, and stable image output of the unmanned intelligent rotary table is realized.

The present invention provides an affine motion estimation method for a dynamic point cloud. The method comprises the following steps: (1) point cloud preorder motion analysis: reading a point cloud sequence to be processed, estimating an affine motion field of the current point cloud starting from a third frame, and performing, on a point cloud having undergone preorder decoding, inter-frame geometric motion analysis to obtain motion prior information; (2) point cloud subset division based on the motion prior information; and (3) affine motion estimation of point cloud subsets: establishing a geometric matching relationship between a reference point cloud subset and the current frame, obtaining an affine transformation matrix between a reference frame subset and the current frame on the basis of singular value decomposition, and iterating all the point cloud subsets to complete affine motion estimation from reference frames to the current frame. The method of the present invention further improves the compression performance of a dynamic point cloud.