46 results about "Motion flow" patented technology

The invention discloses a real-time dynamic rendering method and system for holographic interaction display, and relates to the technical field of holographic interaction display and real-time light field rendering, and the method comprises the steps: collecting a multi-modal interaction signal in real time, and dynamically generating an attention thermodynamic diagram through a space-time alignment algorithm; compressible motion primitives in the scene are extracted based on Lie group SE (3) motion manifold analysis; constructing a sparse sampling light field calculation model according to the attention thermodynamic diagram and the motion primitives, and dynamically adjusting the compression ratio; and performing non-rigid deformation correction on a phase error caused by interaction by adopting a dual-channel feedback compensation mechanism. Holographic display is driven based on a multi-mode interaction signal, the motion trajectory prediction precision is improved through Lie group SE (3) motion manifold analysis, and motion errors are reduced; in combination with an attention thermodynamic diagram and a light field compression strategy, optimal allocation of computing resources is realized, and rendering time delay is reduced; the phase error and non-rigid deformation are suppressed through a dual-channel feedback compensation mechanism, the holographic image quality is improved, and the interaction experience is enhanced.

The invention relates to the technical field of computer vision and satellite video processing, in particular to a satellite video dense vehicle tracking method and system. The method comprises the following steps: constructing a satellite video dense vehicle data set VDD-VEH containing a motion vector label, and providing supervision information for space-time modeling; designing a motion position map (MPG), mapping a target space position and a motion flow into a three-dimensional space-time diagram structure, fusing space-time consistency, appearance features and detection confidence by using a multi-feature edge weight (MFEW) strategy, and quantifying node association strength; global optimal trajectory association is realized by adopting integral linear programming (ILP), abnormal trajectories are eliminated by combining a trajectory optimization module (TRM), trajectory fractures are repaired, and long-time-sequence tracking stability is enhanced. The method is remarkably superior to the prior art in indexes such as MOTA and IDF1, the identity switching frequency is reduced by 55%, and the method is suitable for intelligent traffic monitoring and remote sensing video analysis and has high precision and cross-scene generalization ability.

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product. The image gaze correction method includes: acquiring a to-be-corrected eye image from a to-be-corrected image, generating, based on the to-be-corrected eye image, an eye motion flow field and an eye contour mask, the eye motion flow field being used for adjusting a pixel position in the to-be-corrected eye image, and the eye contour mask being used for indicating a probability that the pixel position in the to-be-corrected eye image belongs to an eye region, performing, based on the eye motion flow field and the eye contour mask, gaze correction processing on the to-be-corrected eye image to obtain a corrected eye image, and generating a gaze corrected image based on the corrected eye image.

The invention provides a speaker video generation method jointly driven by key points and expression action units, which relates to the field of computer vision and comprises a key point and expression action unit jointly driven generation stage, a video quality enhancement stage driven by time consistency and a video super-resolution stage driven by prior. Key points and expression action unit information in the driving frames are extracted, the original frames are driven to generate target frames of postures and expressions through motion flow field estimation and dynamic feature transformation, global enhancement and key area enhancement are carried out on the initially generated frames, and multi-frame joint adversarial training and background fusion are carried out to obtain the target frames of the postures and the expressions. Performing quality correction and enhancement on a result generated frame by frame to obtain an enhanced video, generating a preliminary video frame in a low-resolution space, performing super-resolution processing on the enhanced video, and improving the definition of the video and the high-frequency part of human face details by utilizing face priori information, so as to improve the resolution of the video. The problem of accurate and rapid drive generation of the speaker video can be solved.

The invention discloses a method for detecting the position of a chairlift of a roundabout station of a skiing chairlift, and relates to the field of roundabout safe operation control, and the method comprises the steps: carrying out the nonlinear chairlift dynamics modeling of the chairlift, obtaining a correction envelope, carrying out the topological feature analysis of the chairlift based on the correction envelope, and obtaining a topological detection result. The method comprises the following steps: acquiring a geometric relationship of a hanging chair, laser installation parameters and a laser detection signal to carry out tensor risk fusion decision making to obtain a safety control instruction, carrying out fractional order optimal braking analysis based on the safety control instruction to obtain a structured data frame, acquiring hanging chair displacement, hanging chair motion manifold and laser signal parameters of the hanging chair, and carrying out conformal geometric maintenance to obtain the safety control instruction. Maintenance operation guidance is obtained, wind vibration dynamics, laser detection and safety control parameter space-time coupling modeling and detection precision guarantee under wind and snow interference can be achieved, the offside risk can be perceived in the early stage, the equipment state is judged based on a dynamic threshold value, the trend of dynamic degradation is considered, and maintenance decision response is timely.

The invention discloses a game object state synchronization method and system, a storage medium and equipment. The method comprises the following steps: determining to-be-synchronized target time of a game object; acquiring state data of adjacent time before and after the target time, and performing interpolation processing on the state data of adjacent time before and after the target time based on the target time to obtain first target time state data corresponding to the target time; and synchronizing the game state of the game object at the target time according to the first target time state data. Visual jitter is eliminated through virtual state filling, the motion fluency is improved, the client is released through state calculation, and the load of the server is reduced.

The present application provides a particle size detection method, which relates to the technical field of defect detection. This particle size detection method uses the same reflecting moving mirror to simultaneously participate in the detection of the first surface and the second surface of the workpiece to be detected. By switching the working angle of the reflecting moving mirror, only one camera is needed to complete the shooting of the first surface and the second surface of the workpiece to be detected, effectively simplifying the detection optical path and reducing the cost. Moreover, when the workpiece to be detected enters and exits the plate, there will be processes of deceleration, stop, and acceleration. As long as the rotation timing of the reflecting moving mirror is set when the workpiece to be detected stops, the rotation of the reflecting moving mirror can be skillfully integrated into the movement process of the workpiece to be detected, thus not increasing the overall time consumption of the detection process.

The invention discloses an image registration method based on grouped motion estimation and neighborhood refining sampling. The method comprises the following steps: constructing an image registration network comprising a shared feature extraction module, a sparse motion module and a dense motion module; a source image and a target image are obtained to train the image registration network, and shared feature extraction, grouping motion estimation and neighborhood refining sampling are sequentially carried out until a loss function converges to complete training; a to-be-registered source image and a target image thereof are processed through a network, and finally a registration motion stream is obtained to register the target image to the source image. According to the method, sparse modeling is carried out on input features by introducing grouping motion estimation, the local deformation expression ability is enhanced in combination with an adaptive fusion mechanism, meanwhile, a neighborhood refining sampling method is designed, a learnable local weighted correction mechanism is introduced in the motion flow sampling process, and the local deformation expression ability is improved. The boundary definition and detail precision of the dense motion field are effectively improved, and the precision and robustness of image registration are remarkably improved.

We provide the system. [Solution] A means for acquiring video data and analyzing the location of workers within the office, A means for designing the optimal traffic flow based on the above analysis results, A means of outputting the designed movement flow information, A system that includes this.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: generating, for a conversion between a current block of a video and a bitstream of the video, a prediction block of the current block or at least one picture for the current block based on one or more coded pictures and auxiliary information corresponding to the one or more coded pictures, the one or more coded pictures being coded before the current block, and the auxiliary information comprising at least one of motion flow information or depth information; and performing the conversion based on the prediction block or the at least one picture.

The application relates to the technical field of computers and discloses a tracking method and system applied to orthopedic patient rehabilitation progress, which comprises the following steps: synchronously collecting image sequences and echo signals under patient free activity through high-frame-rate optical imaging and millimeter wave radar; performing three-dimensional reconstruction of human key points and muscle micro-vibration micro-Doppler feature extraction; inputting joint trajectories and muscle activation signals into a biomechanics constraint timing analysis model to output standardized joint angles, muscle strengths and motion fluency sequences; constructing a multi-dimensional rehabilitation progress trajectory graph and generating a structured evaluation report. The system comprises a multi-modal perception layer, a data preprocessing layer, a biomechanics analysis layer and a rehabilitation evaluation layer. The application realizes inductive, continuous and fine-grained rehabilitation tracking, and significantly improves the comprehensiveness, timeliness and individualization level of evaluation.

The invention discloses a video anomaly detection method and system based on a global local feature double-flow network. The method comprises the following steps: 1.1, loading an original video training and test data set; 1.2, dividing a video frame sequence and constructing a corresponding optical flow frame sequence; 2.1, constructing a video anomaly detection model based on global and local feature fusion; 2.2, the model takes a video frame sequence and an optical flow frame sequence as input of an appearance branch encoder and an input of a motion branch encoder respectively, and appearance feature codes and motion feature codes are extracted respectively; 2.3, restoring the image by the appearance and motion decoder, and generating a prediction frame image matched with the corresponding mode; 2.4, calculating an appearance and motion joint loss function, performing back propagation, and optimizing a codec of a branch; 3.1, dividing the test video into a video frame sequence and an optical flow frame sequence; 3.2, inputting into a trained model to generate a prediction frame image at the next moment; 3.3, calculating abnormal scores of the appearance stream and the motion stream of each test video frame; and 3.4, when the abnormal score exceeds a threshold value, judging the current frame as an abnormal frame.

The application discloses a kind of real-time data-driven crowd behavior modeling simulation methods for digital twinning, comprising: constructing three-dimensional twin static scene structure and two-dimensional simulation scene structure;Real-time acquisition of the dynamic changes of the crowd in dynamic scene structure in multiple time windows Video data;Obtain the macroscopic crowd movement flow and crowd density distribution of each time window;Macroscopic crowd movement flow and crowd density distribution are distributed to each unit area, obtain the movement flow set and cumulative density error of each unit area;Obtain the migration adjustment demand and migration adjustment amount of the agent in each unit area;The preferred speed of the agent in each unit area is obtained, the obstacle avoidance speed is obtained, and the three-dimensional dynamic crowd corresponding to the dynamic scene structure time is generated in three-dimensional twin static scene structure.The application can fully meet the application demand of digital twinning scene, with high fidelity, high restoration characteristics.

A knowledge transfer method allows for knowledge transfer based on images acquired from sensors with different views. The knowledge transfer method allows for knowledge learned from an infrastructure device including a top-view sensor to be transmitted to a vehicle including a perspective-view sensor, and the vehicle may predict a path for autonomous driving using the transferred knowledge. Rasterized semantic maps generated based on data acquired from each of the top-view sensor and the perspective-view sensor may be used to generate a motion flow result based on map-matching. The motion-flow result may be transferred to the vehicle for path prediction.

The application discloses a dynamic human body falling detection method based on future human body time sequence posture prediction, and the method comprises the following steps: automatically calibrating the ground in a monitoring scene to obtain ground plane parameters; detecting, cropping and tracking the human body target in a video image to obtain a human body image sequence; performing single-person 3D posture estimation on the human body image sequence frame by frame to obtain a historical 3D skeleton sequence; constructing a skeleton motion flow feature according to the historical 3D skeleton sequence, and extracting a current human body state feature by using a time sequence feature coding model; predicting a future H-frame human body 3D skeleton sequence based on the current human body state feature; determining whether the human body has a risk of falling soon according to the included angle between the human body trunk vector in the future 3D skeleton and the ground plane normal vector, and combining the distance information of the skeleton key point to the ground plane; and outputting a warning signal when a preset condition is met, and the method is suitable for security monitoring, old-age care, public safety and the like.

The application relates to a skeleton action recognition method based on region perception motion contrast learning, and relates to the field of computer vision. The method solves the problems that existing contrast learning skeleton action recognition methods focus on global feature contrast learning, ignore the importance of local action patterns for semantic discrimination, and a large amount of redundancy exists in the time and space dimensions of a skeleton sequence, and the method comprises the following steps: skeleton data preprocessing and standardization; multi-stream data conversion, generating three kinds of representations of joint flow, motion flow and skeleton flow; motion perception time sequence enhancement, adaptively retaining key frames based on interframe motion intensity; spatial multi-level data enhancement, implementing progressive three-level data enhancement operation; spatiotemporal feature extraction and region perception mining, extracting features through an encoder and unsupervisedly identifying significant motion regions by using a contrast motion perception region mining method; contrast learning optimization, constructing a dynamic negative sample queue to learn features; and multi-stream model fusion, weightedly integrating recognition results of the data streams.

The invention discloses a skeleton action recognition method based on regional perception motion contrast learning, and relates to the field of computer vision. The problems that an existing comparative learning skeleton action recognition method mainly focuses on comparative learning of global features and neglects the importance of a local action mode to semantic discrimination, and a skeleton sequence has a large amount of redundancy in time and space dimensions are solved. The method comprises the steps that skeleton data are preprocessed and standardized; converting multi-stream data to generate three representations of joint stream, motion stream and skeleton stream; enhancing a motion perception time sequence, and adaptively reserving a key frame based on inter-frame motion intensity; performing spatial multi-level data enhancement, and implementing progressive three-level data enhancement operation; spatial-temporal feature extraction and region perception mining: extracting features through an encoder, and identifying a significant motion region in an unsupervised manner by using a comparison motion perception region mining method; performing contrast learning optimization, and constructing a dynamic negative sample queue to perform feature learning; and fusing the multi-stream model, and weighting and integrating the identification result of each data stream.

The embodiment of the application discloses a video fluency evaluation method and device and computer equipment. Specifically, first, a target video is acquired, the target video includes multiple video frames, feature extraction is performed on the video frames to obtain multiple features of the video frames and motion state information of the features, a matching relationship of the features in adjacent video frames is calculated to obtain a motion state information set of each feature, the motion state information set includes motion state information of the features in different video frames, fluency evaluation is performed on the features according to the motion state information set to obtain motion fluency of each feature, and finally, multiple motion fluencies are aggregated to obtain video fluency of the target video. The embodiment of the application can significantly improve the efficiency and accuracy of video fluency evaluation.

Estimating a dynamic object velocity includes warping a first voxel grid generated from camera output images of a scene captured at a first time to a third voxel grid representing the scene at a second time; generating a voxel flow from the first time to the second time based at least in part on a second voxel grid generated from camera output images of the scene captured at the second time and the third voxel flow; determining a dynamic voxel flow based at least in part on the voxel flow and an ego motion flow; clustering the dynamic voxel flow to identify one or more object instances; and determining a velocity estimate for a dynamic object of the scene from motion of the one or more object instances in the dynamic voxel flow.

The application discloses an image registration method based on grouping motion estimation and neighborhood refinement sampling. The method comprises the following steps: constructing an image registration network comprising a shared feature extraction module, a sparse motion module and a dense motion module; obtaining a source image and a target image, and training the image registration network, extracting shared features, grouping motion estimation and neighborhood refinement sampling are sequentially performed until the loss function converges to complete the training; and finally obtaining a registration motion flow by processing the source image and the target image to be registered through the network, and registering the target image to the source image. The method introduces grouping motion estimation to perform sparse modeling on input features, combines an adaptive fusion mechanism to enhance local deformation expression capability, designs a neighborhood refinement sampling method, introduces a learnable local weighting correction mechanism in the motion flow sampling process, effectively improves the boundary definition and detail accuracy of the dense motion field, and significantly improves the accuracy and robustness of image registration.

The embodiment of the invention provides an animation generation method and device, electronic equipment and a storage medium, relates to the technical field of image processing, and is suitable for the fields of financial science and technology and medical health. The method comprises the following steps: acquiring an original animation frame, and extracting the original animation frame to obtain original animation features; obtaining movement track data, and extracting the movement track data to obtain movement track features; obtaining animation style constraint information, and extracting animation style constraint features from the animation style constraint information; performing feature transformation on the noise vector to obtain image spatial features; adjusting the animation style constraint features according to the image spatial features to obtain image style features; fusing the image style features, the motion track features and the original animation features to obtain target image features; and decoding the target image features to obtain target animation frames, and merging the target animation frames of the time steps to obtain a target animation. According to the embodiment of the invention, the style consistency and motion fluency of the animation can be considered.

To achieve both accurate recognition and quick recognition of a mobile object included in images of a plurality of frames.SOLUTION: A mobile object recognition device 1 detects a mobile object on the basis of images of a plurality of frames captured in time series by reference and comparison cameras 3 and 4. The mobile object recognition device 1 comprises a feature point tracking unit 21, a variable time width speed estimation unit 24, and a high-precision movement flow determination unit 25. The feature point tracking unit 21 tracks a feature point in the images of the plurality of frames. The variable time width speed estimation unit 24 estimates a relative speed to a vehicle of an object corresponding to the feature point from a difference value between coordinates of the feature point in an image of a frame at a first time and coordinates of the feature point in an image of a frame at a second time preceding the first time by a prescribed time width. The high-precision movement flow determination unit 25 converts the relative speed of the object to an absolute speed. The prescribed time width is determined on the basis of a distance from the reference camera 3 to the object. The mobile object is detected on the basis of the absolute speed of the object.SELECTED DRAWING: Figure 1

The application discloses a kind of meta-universe digital twin construction method and system supporting real-time interaction, belong to computer technology field.The method includes: through the original data stream of multiple sensors deployed in physical entity acquisition;In edge side, semantic event is identified based on kinematics parameter calculation and multi-condition joint determination, and key parameter is extracted to generate lightweight semantic instruction package;After instruction package is transmitted to cloud, corresponding virtual model in parameterization model library is executed according to its driving parameter, and the hierarchical relationship of associated object is updated to realize visual follow-up;Motion interpolation and smoothing processing are carried out between instructions based on physical constraint prediction algorithm, and forward prediction is carried out using timestamp to compensate network delay.The system includes multi-source data acquisition, edge semantic extraction, instruction transmission, virtual reconstruction and physical calibration and other modules.The application reduces data transmission load, improves the real-time performance and motion fluency of virtual-real synchronization, and enhances the adaptive ability and interaction security of system.

The application provides a training method, a soft tissue deformation estimation method, a device, equipment and a storage medium. The training method comprises: obtaining a training sample set, different samples in the training sample set comprising point clouds at different time points and velocity vectors of three-dimensional points in the point clouds; wherein, any sample is obtained based on depth estimation and optical flow estimation of four soft tissue images collected from two different directions at adjacent time points; and a preset neural network model is self-supervised trained by using the samples in the training sample set, to obtain a soft tissue deformation estimation model. In the embodiment, the soft tissue deformation estimation model aggregates semantic information and motion flow information, and improves the accuracy of soft tissue deformation estimation.

The present application relates to the technical field of computer vision, and more particularly to an AI three-dimensional reconstruction method and system for multi-view athlete training video. The method comprises: acquiring a multi-view video sequence and extracting image frames corresponding to time stamps, constructing a multi-scale spatiotemporal feature representation based on the image frames, and establishing a view semantic correspondence relationship by hierarchically fusing local poses and global motion contexts to guide cross-view three-dimensional coordinate inference to generate an initial three-dimensional skeleton; introducing a motion manifold constraint to the initial skeleton to make the skeleton at adjacent time points smoothly transition in the manifold tangent space, and modeling and propagating the uncertainty distribution to obtain an optimized three-dimensional skeleton sequence with confidence estimation; generating a three-dimensional mesh using the optimized skeleton to drive a parameterized human body model and combining texture rendering; performing multi-view projection consistency verification on the rendering result and the original multi-view image frames, and adaptively adjusting the hierarchical fusion weight and the regularization strength of the motion manifold constraint using the verification result.

The invention belongs to the technical field of text-to-video generation, relates to a text-to-video generation method, system and equipment based on time sequence enhanced hidden space diffusion and a medium, and aims to solve the problem of time sequence inconsistency in high-frequency artistic style video generation. The method comprises the following steps: receiving a text prompt and a noise potential sequence; generating a diffusion model control condition based on the text prompt; in the multi-step diffusion denoising process, executing time sequence enhancement processing on each frame except the first frame, including predicting a hidden space motion stream according to the potential state of the adjacent frame, performing motion compensation on the potential state of the previous frame, removing artifacts through a refinement network, and adaptively fusing the refinement potential state and the potential state of the current frame according to a fusion weight; and inputting the fused potential sequence into the denoising backbone network to complete updating, repeating iteration until a final potential sequence is obtained, and decoding to generate a video. According to the method, the time sequence consistency and the structural stability of the generated video are improved through an explicit potential motion alignment and fusion mechanism.

The invention discloses a pipeline construction monitoring method and system based on image recognition, and belongs to the technical field of pipeline construction, and the method comprises the steps: projecting a light spot array to a monitoring scene containing a welding seam, analyzing the displacement of the light spot array in an image sequence to analyze affine transformation parameters, and generating a global motion flow field; performing reverse compensation on the image sequence by using the global motion flow field to generate a physical calibration sequence; performing signal analysis of each pixel on a time dimension on the physical calibration sequence, generating a time domain modulation intensity graph, and performing morphological expansion and connected domain analysis on the time domain modulation intensity graph; dual interference of high-concentration metal dust and equipment high-frequency vibration in underground shield tunnel construction can be suppressed, motion parameters are analyzed through a light spot array to achieve jitter reverse compensation, the dust shielding influence is eliminated in combination with the time domain analysis and texture reconstruction technology, and the problem that defect features are mistakenly erased in the denoising process in a traditional method is solved.

The present application relates to the technical field of defect detection. Provided are a granularity detection method and apparatus. In the granularity detection method, the same rotatable reflecting mirror is used to simultaneously participate in the detection of a first surface and a second surface of a member to be subjected to detection, and by means of switching the operating angle of the rotatable reflecting mirror, only one camera is required to complete the work of photographing the first surface and second surface of said member, thereby effectively simplifying a detection optical path and reducing the costs. Moreover, since said member has deceleration, stop and acceleration processes during loading and unloading, as long as the rotation timing of the rotatable reflecting mirror is set to be a time at which said member stops, the rotation of the rotatable reflecting mirror can be cleverly integrated into the motion process of said member, and thus the overall time of the detection process is not increased.

The invention belongs to the technical field of intelligent operation and maintenance of nuclear power station equipment, and discloses a digital twinborn visualization method and system for a video inspection device of a nuclear power station evaporator. Importing a three-dimensional model of the evaporator and the detection equipment, adding a constraint relation between each joint and a mechanism, and resolving and driving the three-dimensional model through data fusion mapping; acquisition frequency is determined to ensure smooth motion of a twin model, digital twin visualization of entity equipment is realized, real-time monitoring of the equipment state from entity to twin is ensured, a collision detection function can be added on the basis, equipment interference and damage to an evaporator due to collision can be prevented, and the equipment reliability is improved. Precise control of the detection device and rapid off-line planning of the motion trail can be realized through visual feedback from twinning to entity, and the problems that the closed-loop control capability from virtuality to entity is lacked, deep integration with real-time collision early warning and teaching-free trail planning cannot be realized, and the requirements of safety and reliability of the nuclear power station cannot be met can be effectively solved.

The present application belongs to the technical field of deep learning and computer vision, and proposes a human action recognition method based on multi-stream skeleton spatio-temporal feature fusion enhancement, which is used to solve the problems of low recognition accuracy and insufficient model stability of human action recognition in complex dynamic environment. The method extracts skeleton key points from human action video, constructs four parallel data streams of joint stream, joint motion stream, skeleton stream and skeleton motion stream, and establishes a multi-stream spatio-temporal feature modeling network (MSFD-Net) to jointly model the skeleton topology structure and dynamic characteristics in space and time dimensions. A multi-scale context awareness architecture (MSCA) is proposed to capture short-time burst actions and long-time continuous actions, and a spatio-temporal information enhancement module (STIE) is used for adaptive weighting of key joints and key time periods, realizing the cooperative enhancement of global and local spatio-temporal features. The experimental results show that the method has high recognition accuracy and stability, and can be applied to the fields of security monitoring, human-computer interaction and behavior analysis.