690 results about "Motion estimate" patented technology

A method for simultaneous localization and mapping underwater. A robot is equipped with an IMU inertial unit and a sonar unit. When the robot submerges underwater, a buoy connected to the robot floats on the water surface, and the buoy moves in coordination with the movement of the robot. The position and observed velocity of the buoy are obtained by a shore-based lidar. During motion estimation in a SLAM algorithm, when an angular velocity is below a preset angular velocity threshold, the observed velocity of the buoy is decomposed into x-axis velocity and y-axis velocity, and updated as the two-dimensional operating velocity of the robot. The polar coordinates of an obstacle in a map under the coordinate system of the robot are associated with the polar coordinates of sonar data of a current frame by using the Mahalanobis distance. The polar coordinates of a new obstacle are converted to the world coordinate system and added to the map. The present invention has the advantages of not relying on underwater visibility, not requiring pre-installed devices, and possessing good generalization and stability.

The invention discloses a cradle head tracking method, system and device, a storage medium and a program product, and relates to the technical field of cradle heads, and the method comprises the steps: obtaining the target position information of a tracking target in continuous multi-frame image frames collected by an image sensor, and the size of a detection frame, determining a pan-tilt rotation angle of a target pan-tilt carrying an image sensor when multiple image frames are acquired; determining a target position offset between the target position and the center of the image based on the target position information, and mapping the size of the detection frame into a target distance between the tracking target and the image sensor; inputting the target position offset and the holder rotation angle corresponding to the multiple image frames into a motion estimation model to obtain a predicted position; and determining a pan-tilt angle value of the target pan-tilt based on the predicted position and the target distance, and driving the target pan-tilt to rotate based on the pan-tilt angle value. According to the invention, the cradle head keeps stable tracking while quickly responding to target movement, so that the stability and reliability of cradle head tracking control are improved.

Disclosed in the present invention is a LiDAR odometer method using a piecewise linear continuous time trajectory. The present invention comprises the following steps: first, using a time window to divide an initial LiDAR point cloud time sequence so as to obtain LiDAR point cloud sub-sequences under a plurality of time windows, and constructing an initial local point cloud map; and then, on the basis of the initial local point cloud map, optimizing LiDAR poses of the LiDAR point cloud sub-sequences under the time windows to obtain an overall continuous time trajectory and a final local point cloud map. The present invention achieves high-precision motion estimation for LiDARs, has high robustness and real-time performance, is applicable to the fields such as robot navigation, simultaneous localization and mapping, automated driving, and provides a relatively simple and efficient solution that can effectively handle the characteristics of LiDARs as streaming sensors.

The invention discloses a structure large displacement estimation method based on Canny-Hough transformation and KLT optical flow, and relates to the related field of signal processing and vision measurement, and the method comprises the steps: collecting and preprocessing structure vibration data; carrying out integer pixel displacement estimation on the basis of Canny-Hough transformation; reconstructing the video; performing sub-pixel displacement estimation based on the KLT optical flow; and physical displacement. According to the method, the limitation of a single identification method in identifying different pixel levels (pixel level and sub-pixel level) is overcome, the measurement precision of the large displacement of the structure is remarkably improved, and an estimation error caused when an image pyramid is used for large displacement tracking is avoided. The research provides a new target-free large-displacement motion estimation scheme for structural vibration measurement based on computer vision, and has important reference value.

Methods and systems implement a pleno-generation face video compression framework with bandwidth intelligence for generative models and compression. Heterogeneous-granularity facial description regularizes long-term dependencies between video frames and compensates for motion estimation errors caused by compact representations of motion information. A generative decoder reconstructs heterogeneous-granularity visual representations, providing auxiliary visual signals for attention-based recalibration of a GFVC-reconstructed face signal. A coarse-to-fine generation strategy avoids error accumulation. High efficiency for heterogeneous-granularity signal compression is achieved by two different entropy-based signal compression methods: heterogeneous-granularities feature representation from the key-reference frame as hyperpriors to optimize the entropy model for compressing heterogeneous-granularity feature from subsequent inter frames, and a feature difference operation for heterogeneous-granularities feature representation between key-reference and subsequent inter frames, such that the entropy model only compresses heterogeneous-granularities feature residual for redundancy reduction. Mixed-model dataset generation and training and model-specific dataset generation and training are also provided.

The provided is a LiDAR odometry using a piecewise linear continuous-time trajectory. The LiDAR odometry includes the following steps: dividing an initial LiDAR point cloud time sequence through time windows to obtain LiDAR point cloud subsequences in the multiple time windows, and constructing an initial local point cloud map; and optimizing, based on the initial local point cloud map, a LiDAR pose for the LiDAR point cloud subsequence in each time window to obtain an overall continuous-time trajectory and a final local point cloud map. The provided achieves high-precision motion estimation of the LiDAR, with high robustness and real-time performance, and is suitable for fields such as robot navigation, simultaneous localization and mapping, and autonomous driving. The provided gives a simple and efficient solution that effectively utilizes the characteristics of the LiDAR as a streaming sensor.

The invention discloses a non-vision field imaging detection method and system fused with an event camera, and the method comprises the following steps: enabling the event camera and a standard optical camera on a detection prototype to be aligned with a ground region of a corner, forming a video file based on a tiny pixel color difference amplification technology, and carrying out the calculation and analysis of the video file; processing the video file by using an image matching algorithm, and eliminating noise by using Kalman filtering; establishing a linear system, and then obtaining a reconstructed image by using a forward transmission light intensity function; performing Euler video amplification processing on the reconstructed image, wherein the Euler video amplification comprises motion estimation, spatial decomposition, time filtering and signal enhancement; detecting the detection area, and mapping the motion track of the target hidden behind the corner to a two-dimensional plane; the system comprises a simulation scene construction module, a three-dimensional point cloud set construction module and an information integration module. According to the invention, a micro motion algorithm and optical flow calculation are popularized to non-vision field imaging, so that the optical imaging technology can act on a vision field range and also can act on a non-vision field range.

A method for determining a location of a vehicle includes obtaining, by a processor, a plurality of aerial image segment signatures of segments of a region including the vehicle, and a plurality of sensed image signatures associated with the region including the vehicle. The method matches a selected aerial image segment signature segment signatures to a selected sensed image signature. Based on the matching step, the processor generates probabilistic location information regarding the location of the vehicle. The method also includes generating a movement estimate of the vehicles. The movement estimate is generated based on a vehicle location comparison across a plurality of vehicle sensed images, and the plurality of vehicle sensed images are captured at a plurality of time intervals. The method also includes determining the location of the vehicle by combining the movement estimate of the vehicle and the probabilistic location information.

Disclosed in the embodiments of the present application are a dense optical flow generation method and apparatus, and a device and a storage medium. The method comprises: performing block matching on the current image frame and the next image frame, in order to obtain a first motion estimation vector corresponding to each pixel point to be processed in the current image frame, wherein the first motion estimation vector corresponding to said pixel point is determined on the basis of a block matching result corresponding to a unit image block in which said pixel point is located; on the basis of the matching relationship between a local image in which each said pixel point is located in the current image frame and a local image in which a corresponding matching pixel point is located in the next image frame, fitting a second motion estimation vector corresponding to each said pixel point, wherein the matching pixel point is determined on the basis of said pixel point and the corresponding first motion estimation vector; and on the basis of the first motion estimation vector and the second motion estimation vector corresponding to each said pixel point, generating dense optical flow information.

The invention relates to the technical field of map construction, and provides a mapping method and device based on fusion of a laser radar and an inertial measurement unit. According to the scheme, motion distortion compensation is carried out on original point cloud data through motion estimation based on inertial data, the compensated point cloud is matched with a local map, and the local map is obtained through a filter; carrying out tight coupling fusion on the inertial data and the point cloud matching result, and outputting the pose information of the sensor at the current moment; according to the pose information of the sensor, the compensated point cloud is added into the dynamically managed local map, and the updated local map is periodically published, so that a complete mapping function from data acquisition, processing and fusion to map construction is realized. And through motion distortion compensation, the inertial data and the point cloud matching result are tightly coupled and fused, and the updated local map is periodically published, so that high mapping precision can still be kept when the mapping area is large.

The invention provides an image motion estimation method and a related device, and an electronic device can intercept a rendering instruction for an image frame, obtains the related resources of the image frame based on the rendering instruction, carries out the motion estimation of two adjacent image frames through the fusion of an optical flow motion estimation method and a 3D motion field motion estimation method, and achieves the motion estimation of the two adjacent image frames. And determining motion vector information between two adjacent image frames. In this way, the motion estimation effect between two adjacent image frames can be improved.

The invention belongs to the technical field of multi-camera cooperative imaging, and discloses a multi-camera cooperative anti-shake control system and method. The method comprises the following steps: generating a global hardware trigger signal by using an FPGA (Field Programmable Gate Array), synchronizing IMU and multi-camera acquisition, and combining motion parameters to obtain a space-time aligned multi-view image; obtaining a global jitter component through feature extraction, motion vector field generation, depth map analysis and the like; fusing the data with IMU data to generate a trajectory, and inputting the trajectory into LSTM to obtain future compensation amount; and cutting based on deep layered transformation to obtain an EIS optimized frame, dynamically electing a main camera for super-resolution reconstruction, and outputting a final anti-shake video stream. Through multi-source information fusion, layered precise compensation and dynamic optimization, the accuracy and stability of motion estimation are remarkably improved, the jitter influence of the finally output video stream is effectively weakened, and the reliability and visual coherence of the image stabilization effect in a complex scene are ensured.

The invention discloses a heart motion feature extraction method based on optical flow estimation, and relates to medical image processing. Preprocessing the input four-dimensional space-time cardiac magnetic resonance imaging data, namely, scaling pixel values; inputting two frames of images which are continuous in time into a feature encoder and a context encoder for feature extraction, wherein the two frames of images are divided into a reference frame and a moving frame; calculating the correlation between the feature maps through a correlation volume calculation module, constructing a correlation pyramid, and extracting the feature maps to provide matching information for subsequent optical flow estimation; the motion feature iteration enhancement module iteratively and continuously refines an optical flow estimation result through a deformable convolution and global motion aggregation (GMA) module; model parameters are optimized based on a weighted sum of luminosity consistency loss, smoothness loss, and gradient consistency loss. Edge features are adaptively captured through deformable convolution, global and local features are fused by using a GMA module, optical flow prediction errors are effectively reduced, motion estimation quality is improved, and key features of a heart edge region are maintained.

The invention discloses a motion estimation method and device based on multi-modal perception and electronic equipment, and relates to the technical field of intelligent technology, in particular to the technical field of motion estimation based on multi-modal perception. According to the specific implementation scheme, motion evaluation related data, including I MU sensor data and flexible deformation sensor data, of a target part area of a target object are obtained, and pose information of a part configured with an I MU sensor can be determined based on the I MU sensor data; and according to the position relationship between the configured I MU sensor and the flexible deformation sensor and the acquired data of the flexible deformation sensor, the pose information of the position where the flexible deformation sensor is configured is calculated, so that the pose information of the position where the I MU sensor is not configured is obtained under the condition that the configuration of the I MU sensor is limited.

The invention relates to the technical field of fire safety inspection, in particular to a fire safety inspection method and system based on machine vision recognition. The method comprises the following steps: carrying out inspection equipment deployment on an inspection area to obtain a camera deployment scheme and a sensor network deployment scheme; performing multispectral dynamic sensing according to the camera deployment scheme to obtain a time sequence spectral flow; performing multispectral channel motion estimation on the time sequence spectral flow, and performing motion vector noise suppression to obtain a filtered motion vector diagram; performing spectral characteristic-based motion enhancement on the filtered motion vector diagram to obtain a spectral enhanced motion vector; and performing motion amplification algorithm application on the time sequence spectrum flow according to the spectrum enhanced motion vector, and constructing an enhanced motion vector field to obtain the enhanced motion vector field. According to the invention, through multi-modal information fusion, context awareness and capturing of early fine features, early and accurate fire hazard identification and early warning are realized.

The invention provides a medical image denoising and segmentation integrated model trained based on a conductible diffusion method, and belongs to the field of medical image processing, and the model comprises an improved denoising and diffusion module which is used for receiving an initial medical image, predicting an original clean signal of the initial medical image based on an improved denoising and diffusion probability model, and obtaining a final denoised medical image; the joint motion and segmentation module is used for receiving the initial medical image, extracting features through a shared encoder, and performing joint learning through a motion estimation branch and a segmentation branch to obtain segmentation data; the cascade training mechanism carries out end-to-end training on the improved de-noising diffusion module and the joint motion and segmentation module through a derivable connection, gradient back propagation is realized by using a joint loss function, and cascade training is completed. According to the method, the problem that the quality of segmented medical images is reduced due to the fact that denoising and segmentation tasks cannot be collaboratively optimized due to the fact that a traditional denoising method cannot be guided in sampling and is difficult to carry out cascade training with a segmentation model is solved.

An apparatus for estimating a motion of a vehicle occupant, and a method therefor, can store motion estimating models of a vehicle occupant, collect behavior data of a vehicle, determine a traveling mode of the vehicle based on a longitudinal acceleration among the behavior data of the vehicle, and estimate a motion of the vehicle occupant using at least one of the motion estimating models corresponding to the traveling mode of the vehicle. The motion estimating models can include a first model corresponding to a dynamic mode of the vehicle and a second model corresponding to a steady state mode of the vehicle.

The invention relates to the technical field of magnetic resonance imaging, and discloses a motion estimation guided magnetic resonance four-dimensional dynamic imaging method and device, and the method comprises the steps: obtaining a three-dimensional single-frame static image of magnetic resonance and an under-acquired low-resolution four-dimensional dynamic image sequence, the low-resolution four-dimensional dynamic image sequence is a multi-frame low-resolution four-dimensional dynamic image obtained by carrying out undersampling on K space acquisition data; utilizing a motion encoder in the image sequence prediction model to extract motion estimation information in the low-resolution four-dimensional dynamic image sequence; inputting the three-dimensional single-frame static image into a structure encoder in the image sequence prediction model to obtain static anatomical structure information; and fusing and decoding the motion estimation information and the anatomical structure information through a decoder to obtain a high-resolution four-dimensional dynamic image sequence. The problems that an existing magnetic resonance four-dimensional imaging technology is long in scanning time, complex organ movement is difficult to capture, and time-space consistency is poor are solved.

The invention provides an imaging spectrometer motion compensation imaging model construction method, an imaging model and a device, and relates to the technical field of space remote sensing. The method comprises the following steps: establishing a satellite orbit model, and defining a plurality of coordinate systems such as a detector coordinate system and a spectrometer imaging coordinate system and a conversion relation thereof; constructing a detector-scanning mirror-lunar surface ideal geometric mapping model based on the load optical parameters and a ray tracing method; determining the relation between the rotation angle of the scanning mirror and imaging spectrum view field positioning by using a reflecting prism conjugation theory; a quantitative relation is established by combining parameters such as satellite orbits and position postures, and finally a motion compensation imaging model is established. According to the method, the influence of the on-orbit error on the compensation imaging model is analyzed, and the error is used as an uncertainty component to be coupled into the model. According to the method, the problems of low model precision, neglect of influence of orbit observation parameters on imaging and the like in the prior art are solved, high-precision motion estimation and compensation can be realized, and the quality of data acquired by an imaging spectrometer is remarkably improved.

There is provided a computer-implemented method for learned video compression, which includes processing a current frame (xt) and previously decoded frame ({circumflex over (x)}t-1) of a video data using a motion estimation model to estimate a motion vector (vt) for every pixel, compressing the motion vector (vt) and reconstructing the motion vector (vt) to a reconstructed motion vector ({circumflex over (v)}t), applying an enhanced context mining (ECM) model to obtain enhanced context ({umlaut over (C)}E) from the reconstructed motion vector ({circumflex over (v)}t) and previously decoded frame feature (x̆t-1), compressing the current frame (xt) with the assistance of the enhanced context ({umlaut over (C)}E) to obtain a reconstructed frame ({circumflex over (x)}′t), and providing the reconstructed frame ({circumflex over (x)}′t) to a post-enhancement backend network to obtain a high-resolution frame ({circumflex over (x)}t).

The invention relates to the technical field of image recognition, in particular to a method and system for early warning below a portal crane, and the method comprises the steps: obtaining site environment sensing data, constructing a space map, and recognizing the position and motion state of equipment in real time; predicting an equipment motion track and a load swing range, and generating a predicted dangerous area; image acquisition devices are installed at a plurality of fixed positions, field images are acquired, an image recognition model is used for detecting a target, and the spatial position and the posture state of the target are obtained; tracking a target trajectory based on a motion estimation model and predicting a future position of the target trajectory; dynamically switching the image acquisition devices according to the motion state of the equipment; and comparing the future position of the target with the predicted dangerous area in real time, when the future position and the predicted dangerous area coincide, carrying out potential interference prediction, calculating predicted interference time, and carrying out grading processing in combination with the attitude state of the target. According to the invention, equipment dynamic perception and personnel behavior prediction are realized, and the intelligent protection capability of the operation area is improved.

The invention relates to an image noise reduction method and system and a readable storage medium, and the method comprises the steps: carrying out the Gaussian filtering of a current frame image, carrying out the down-sampling, obtaining a first down-sampling image, and carrying out the Gaussian pyramid decomposition, and obtaining a current frame image group; performing up-sampling to obtain an up-sampling image, and subtracting the up-sampling image from the current frame image to obtain high-frequency information; motion estimation and texture estimation under different scales are carried out on the current frame image group and the reference frame image group, and guided filtering correction is carried out; performing time-space domain filtering on the first down-sampling image and the reference frame image based on the motion estimation weight; performing up-sampling on the down-sampling image after noise reduction to obtain an up-sampling image after noise reduction, and performing weighted fusion on the up-sampling image after noise reduction and the high-frequency information according to the texture estimation weight to obtain a final image; and performing down-sampling on the final image and performing Gaussian pyramid decomposition to obtain a reference frame image group for noise reduction of the next frame image. According to the method, more texture details can be reserved while the signal-to-noise ratio of the image is increased.

The invention discloses an echocardiogram myocardial motion estimation method and system based on a structure perception diffusion model, and relates to the technical field of ultrasonic video data processing. The invention aims to solve the problem of myocardial motion estimation in an ultrasonic video. According to the technical key points, the method comprises the following steps: (1) preprocessing collected echocardiogram video data; (2) establishing an echocardiogram myocardial motion estimation model; (3) constructing a diffusion model motion estimation module of structure perception; (4) constructing a spatial adaptive up-sampling module; (5) training the network by using the existing data to obtain a network model; and (6) estimating the myocardial movement of the echocardiogram video by using the trained model. According to the method, the cardiac structure information and the multi-scale correlation contained in the echocardiogram can be fully utilized to automatically estimate the cardiac muscle movement of the echocardiogram, and the accuracy and the robustness of the algorithm are effectively improved.

A computer-implemented method includes converting by a pose tokenizer, based on a learned codebook, pose parameters of a body into a sequence of discrete pose tokens; randomly masking a portion of the sequence of discrete pose tokens; predicting the randomly masked sequence of discrete pose tokens based on multi-scale features extracted from a monocular image by an image conditioned masked transformer; optimizing the sequence of discrete pose tokens by aligning a re-projected three-dimensional (3D) pose with an estimated two-dimensional (2D) pose; directly regressing, from the multi-scale features, a shape parameter of the body and a weak perspective camera parameter; and generating a 3D mesh reconstruction of the body based on the shape parameter and the weak perspective camera parameter.

The invention provides a coding and decoding method and electronic equipment. The method comprises the following steps: performing motion estimation on a current frame to obtain a first optical flow; obtaining first optical flow super-prior information based on the feature information of the first optical flow and the prior information of the first optical flow; encoding the first optical flow hyper-prior information to obtain a first code stream; probability estimation is carried out based on the second optical flow hyper-prior information and the prior information of the first optical flow, and optical flow probability distribution is obtained; entropy coding is carried out on the feature information of the first optical flow based on the optical flow probability distribution to obtain a second code stream; performing inter-frame prediction on the current frame to obtain prediction information; determining residual information based on the prediction information and the original image; and encoding the residual information to obtain a third code stream. Compared with the prior art that the determined first optical flow hyper-prior information is used for determining the optical flow probability distribution of all the pixel points in the current frame, the first optical flow hyper-prior information is used for determining the optical flow probability distribution of part of the pixel points in the current frame, and the code rate overhead can be reduced.

The invention discloses a motion carrier self-motion estimation method based on multi-radar fusion, a carrier and a medium, and relates to the technical field of motion carrier positioning. The method comprises the following steps: performing consistency check among radars based on self-motion estimation results of the radars, and judging whether the radars with abnormal self-motion estimation results exist or not according to check results; if not, the self-motion estimation results of all radars are fused, and a final self-motion estimation result is output; if so, identifying an abnormal radar from the radars according to at least one quality index; and discharging the self-motion estimation results of the radars identified as abnormal, and based on the self-motion estimation results of the remaining radars not identified as abnormal, performing fusion or directly outputting the self-motion estimation results as the final self-motion estimation result. According to the method, the abnormal source can be automatically identified and isolated, and the robustness and the function security level of the system in a real complex environment are remarkably improved.

The invention discloses an unmanned aerial vehicle detection and tracking method based on a multi-modal image, and belongs to the field of target detection and tracking. The unmanned aerial vehicle in the air is detected through the combined action of multiple modes, so that the reliability of unmanned aerial vehicle detection in various environments is realized, and identification and classification of the unmanned aerial vehicle are realized; in the tracking stage, a prediction structure in which LSTM and a Kalman filter are fused is introduced, so that the state modeling capability under the conditions of nonlinear motion and shielding of a target is enhanced, and the stability and continuity of tracking are improved; a fusion multi-stage matching strategy is adopted to effectively improve the matching accuracy and robustness in a complex environment; meanwhile, a confidence shunt strategy and a trajectory management mechanism are combined, so that the response speed to a new target and the fault-tolerant capability to a lost target are improved; through combination of global motion estimation and height information judgment, accurate correction and target distinguishing of the track of the unmanned aerial vehicle are realized, aliasing and misjudgment are avoided, and the detection and tracking capability of the unmanned aerial vehicle is remarkably improved.

The invention relates to the field of digital image processing, in particular to a super-resolution imaging method based on focal plane splicing and adaptive fusion. According to the method, sub-pixel offset among nine CCDs is preset through hardware, and nine frames of low-resolution image sequences with accurate displacement are obtained in push-broom. A central image is taken as a reference frame, high-precision mapping is realized based on hardware offset, motion estimation errors are avoided, effective pixels are screened by calculating robustness weight, an anisotropic Gaussian kernel function with a self-adaptive local structure is constructed so as to maintain image edge and detail features, and each frame is accumulated to a high-resolution grid in a weighting mode, so that a high-resolution image is obtained. And a sample compensation mechanism based on cumulative robustness is introduced, a fusion strategy is adaptively adjusted in an information insufficient area, and finally a high-resolution image is generated through normalization. The method significantly improves the imaging quality, suppresses artifacts and noise, and is suitable for the field of satellite remote sensing.

The invention provides a multi-robot 3D vision cooperation space attitude sensing calibration method and system, and relates to the technical field of multi-robot cooperation work. And collecting three-dimensional visual data in real time in the cooperative working area by using at least three robots. Setting a calibration precision factor to quantify an attitude estimation error, and preprocessing the collected data, including noise reduction and normalization; the weight of each data source is calculated to reflect the importance of the data source in the fusion process, a fused attitude estimation model is generated through dynamic Kalman filtering, and historical information is introduced. A motion estimation error is set and monitored in real time, when the error exceeds a threshold value, a calibration re-correction mechanism is triggered, the robot is reconfigured for data collection and weight updating, and it is ensured that the calibration precision continuously meets the requirement. According to the method, the attitude sensing precision of the multiple robots in a complex environment is improved, the data processing complexity is reduced, the real-time performance of the system is enhanced, and a foundation is laid for wide application of cooperative work of the multiple robots.

Provided are a beauty instrument control method, a skin data processing method, a control system, and a device. The present application relates to the field of biological medical treatment. The method comprises: by means of a touch chip in a beauty instrument, determining whether an end surface of the beauty instrument fits the skin; if the end surface of the beauty instrument fits the skin, starting a beauty instrument load, and on the basis of motion estimation technology and by means of a camera of the beauty instrument, detecting whether a relative movement occurs between the skin and the end surface of the beauty instrument; if no relative movement occurs between the skin and the end surface of the beauty instrument, reducing the power of the beauty instrument load until the beauty instrument is in a standby state, and re-executing the step of determining, by means of the touch chip in the beauty instrument, whether the end surface of the beauty instrument fits the skin; and if a relative movement occurs between the skin and the end surface of the beauty instrument, controlling the beauty instrument to work normally. Further disclosed in the present application are the corresponding skin data processing method, the control system and the device.