2026 results about "Motion vector" patented technology

The invention discloses an artificial intelligence machine vision image acquisition system, and the system comprises a multi-mode perception layer which integrates a self-adaptive optical module, inhibits metal reflection, captures a visible light to short wave infrared image, and captures a motion edge; the dynamic adaptive layer adopts an illumination compensation and motion compensation module to dynamically adjust camera parameters and micro displacement compensation, feeds back an illumination trend, outputs a motion vector to the cognitive layer, generates a confrontation sample through a GAN, simulates virtual defects in combination with a physical engine, and expands training data; the cognitive reasoning layer is used for deploying a dynamic routing network, distributing computing resources according to image complexity and optimizing feature extraction efficiency; reducing data deviation through anti-fact analysis, and generating a thermodynamic diagram to explain a detection basis; and the collaborative decision-making layer is used for rapidly screening samples by edge nodes, training a global model by cloud aggregated data, automatically triggering manual rechecking when the confidence coefficient of the model is insufficient, synchronously optimizing a training set and a causal reasoning module by a rechecking result, and improving the labeling efficiency through AR assistance.

The invention relates to the technical field of intelligent driving, in particular to a method for generating an obstacle avoidance and planning cooperative path in an urban complex environment, which comprises the following steps: acquiring environment real-time sensing data through a vehicle-mounted multi-sensor, and constructing a dynamic semantic traffic matrix in combination with high-precision map static semantic information; based on the matrix, a multi-objective optimization algorithm is adopted to calculate the security cost, the efficiency cost and the rule conformity cost of the path, and a global optimization path is generated; inputting the global optimization path and the dynamic obstacle motion vector into an intention prediction model, generating dynamic obstacle future trajectory probability distribution and interactive intention classification, and further generating an avoidance strategy and adjusting a local path in real time; and inputting the adjusted local path into a kinematics model to carry out kinematics feasibility verification, and outputting an executable track or path re-planning. According to the method, the integration of dynamic environment understanding, path planning and obstacle avoidance strategies is realized, and the method is suitable for the path generation task of an automatic driving system in an urban complex traffic scene.

The invention provides an AI-based nuclear power station high-risk operation compliance real-time monitoring method and system, and the method comprises the steps: processing human body joint point coordinate data of an operator through employing an AI dynamic contour modeling algorithm, and generating a limb movement track sequence; scanning the preset operation area to generate a relative motion vector between the special operation tool for the nuclear power station and the operator; detecting an action deviation mode of the operator based on the limb movement track sequence, and generating a detection result; fusing the relative motion vector and multi-channel video stream frame synchronization data obtained by applying an AI video analysis algorithm to generate a space intrusion relation between the nuclear power station special operation tool and the nuclear power station high-risk equipment; and when the action deviation mode exists in the detection result and the space intrusion relation exceeds a preset safety threshold, generating an operation compliance signal. According to the invention, real-time dynamic evaluation of human motion compliance and tool equipment safety distance in the high-risk operation process of the nuclear power station is realized, and the safety monitoring precision and real-time early warning capability of high-risk operation are improved.

The invention relates to the technical field of liquid crystal display, in particular to a liquid crystal display driving control method and system. Comprising the following steps: SS01, multi-modal sensing and state triggering: fusing a user distance, a fixation point coordinate and ambient light illumination in real time by using an integrated sensor, and triggering an eye protection mode or a self-adaptive sleep mechanism according to the user distance, the fixation point coordinate and the ambient light illumination, SS02, image partitioning and strategy generation: dividing a screen into N * M partitions, and SS03, distinguishing a dynamic region from a static region according to the brightness histogram and the motion vector of each partition, and outputting differentiated refresh rates and backlight strategies, and SS03, performing big data learning and model optimization, and collecting user behaviors. The system has the advantages that the infrared proximity sensor, the eyeball tracking sensor, the distance sensor and the ambient light sensor are integrated through the multi-mode sensing and state triggering module, multi-dimensional data such as user distance, fixation point coordinates and ambient light illuminance are fused in real time, and active intelligent sensing of the user state and the environment is achieved.

The invention belongs to the technical field of signal synchronization, and discloses a multi-camera anti-shake time sequence synchronization control system and method based on an FPGA. The method comprises the following steps: taking an FPGA as a core, generating an FSYNC signal as a camera exposure reference, and obtaining a delay value through a timestamp and differential measurement; acquiring I MU data and obtaining a global motion vector through a gradient descent algorithm; analyzing by combining the two to obtain phase deviation and feeding back the phase deviation to the FPGA, and adjusting the FSYNC signal by combining the global motion vector by the FPGA according to the phase deviation; according to the method, nanosecond timestamp recording is realized through a homologous clock reference and hardware-level signal binding mechanism, and the limitation of dependence on static time difference compensation in the prior art is broken through in combination with global motion vector analysis and dynamic phase offset adjustment.

The invention provides a motion blurred bar code identification method and system based on multi-frame image fusion, and the method comprises the steps: obtaining a continuous image sequence containing a motion blurred bar code, carrying out the motion track feature extraction of the continuous image sequence, and obtaining a motion vector field and a pixel displacement track set of a bar code region in each frame image unit; performing multi-frame image fusion on the continuous image sequence based on the motion vector field and the pixel displacement track set to generate a candidate bar code image set; performing deblurring enhancement processing on the candidate bar code image set to obtain a clear bar code image unit after deblurring processing; and performing bar code area positioning and distortion correction processing on the clear bar code image unit to generate a standardized bar code image, and performing identification to obtain an identification result. According to the invention, the accuracy and reliability of bar code identification in a motion blurred scene can be obviously improved.

A video encoding or decoding apparatus and method perform existing inter prediction on a current block to generate a motion vector and predicted samples. The video encoding or decoding apparatus and method generate enhanced predicted samples for the current block using a deep learning-based video prediction network (VPN) on the basis of the motion vector, the reference samples, the predicted samples, and the like to improve encoding efficiency.

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.

To provide a video encoding / decoding method and apparatus for performing intra-prediction based on the availability of a reference pixel so as to improve encoding performance.SOLUTION: An intra-prediction method for a color copy mode checks a reference pixel region designated to obtain correlation information, determines a reference pixel processing setting based on a determination of availability of the reference pixel region, and performs intra-prediction according to the determined reference pixel processing. In addition, a candidate list for motion information prediction of the current block is generated, a control point vector of the current block is derived based on the candidate list and a candidate index, a motion vector of the current block is derived based on the control point vector of the current block, and inter prediction is performed on the current block using the motion vector.SELECTED DRAWING: Figure 14

To provide a video encoding / decoding method and apparatus for performing intra-prediction based on the availability of a reference pixel, which can improve encoding performance.SOLUTION: An intra-prediction method for a color copy mode checks a reference pixel region designated to obtain correlation information, determines a reference pixel processing setting based on a determination of availability of the reference pixel region, and performs intra-prediction according to the determined reference pixel processing. In addition, a candidate list for motion information prediction of the current block is generated, a control point vector of the current block is derived based on the candidate list and a candidate index, a motion vector of the current block is derived based on the control point vector of the current block, and inter prediction is performed on the current block using the motion vector.SELECTED DRAWING: Figure 14

The invention provides a bridge structure damage detection method based on image processing and CNN-LSTM, relates to the field of graphic image processing, and solves the problems that bridge vibration monitoring in the prior art is time-consuming, labor-consuming, high in cost and limited in precision, and adopts the technical scheme that the method comprises the steps of obtaining a recorded vibration video of a to-be-detected bridge structure; extracting the vibration video frame by frame to obtain bridge image data; detecting angular points by using a Harris algorithm, screening image data, and calculating vertical displacement of each feature point one by one; using a Lucas-Kanade optical flow algorithm to calculate the motion vectors of the feature angular points of the adjacent frame images; calculating the actual vibration displacement of the bridge; and carrying out standardization processing on the actual vibration displacement to obtain a two-dimensional matrix, inputting the two-dimensional matrix into the trained CNN-LSTM model, and outputting a final identification result of each type of damage. According to the scheme of the invention, high-precision bridge vibration displacement can be calculated, and efficient, accurate and high-precision bridge damage detection can be realized through the CNN-LSTM damage identification model.

The invention relates to the technical field of traffic monitoring, in particular to a traffic monitoring method and system for vehicle tracking and early warning, and the method comprises the following steps: obtaining the real-time position, speed and acceleration data of all vehicles in a monitored road area, and setting according to the spatial distance between the vehicles and a time window; adjacent vehicles are aggregated into a dynamic traffic microscopic cluster, and a regional motion state reference is established; according to the method, the spatial distance and the time window between the vehicles are set, the dynamic traffic microscopic clusters are divided, refinement and dynamics of vehicle clustering are achieved, and the sensitivity and the response speed of road microscopic traffic environment changes are improved; and deviation calculation and weighted summation operation are carried out on a single vehicle motion vector by using a regional motion state reference, so that accurate quantification of individual driving behavior instability is realized, the abnormal driving state of the vehicle is captured, and the abnormal behavior recognition accuracy is improved.

To provide a video encoding / decoding method and apparatus for performing intra-prediction based on the availability of a reference pixel so as to improve encoding performance.SOLUTION: An intra-prediction method for a color copy mode checks a reference pixel region designated to obtain correlation information, determines a reference pixel processing setting based on a determination of availability of the reference pixel region, and performs intra-prediction according to the determined reference pixel processing. In addition, a candidate list for motion information prediction of the current block is generated, a control point vector of the current block is derived based on the candidate list and a candidate index, a motion vector of the current block is derived based on the control point vector of the current block, and inter prediction is performed on the current block using the motion vector.SELECTED DRAWING: Figure 14

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 relates to the technical field of image processing, and further relates to a video decoding and unreal engine rendering method based on GPU full-link zero copy, which comprises the following steps of: 1, performing hardware decoding on an input video code stream on a GPU, and constructing a motion vector description buffer region for storing compressed domain motion vector information according to a macro block sequence; step 2, reading the motion vector description buffer area on the GPU through a calculation shader, and generating dynamic special effect control buffer areas in one-to-one correspondence with the macro blocks; and step 3, in a post-processing material of the unreal engine rendering module, taking the shared video texture resource as an input texture, and outputting a video picture. On the premise of not depending on a host processor and not excessively occupying video memory bandwidth, unified management of pixel data and motion data is achieved, delay is remarkably reduced, and special effect stability and direction consistency are improved.

To provide a video encoding / decoding method for performing intra-prediction on the basis of availability of a reference pixel so as to improve encoding performance.SOLUTION: An intra-prediction method for a color copy mode checks a reference pixel region designated to obtain correlation information, determines a reference pixel processing setting based on a determination of availability of the reference pixel region, and performs intra-prediction according to the determined reference pixel processing. In addition, a candidate list for motion information prediction of the current block is generated, a control point vector of the current block is derived based on the candidate list and a candidate index, a motion vector of the current block is derived based on the control point vector of the current block, and inter prediction is performed on the current block.SELECTED DRAWING: Figure 14

The invention discloses a power grid intelligent inspection method and system based on an unmanned aerial vehicle, and the method comprises the following steps: collecting image flow data of a target region, constructing a three-dimensional point cloud model, and generating an initial inspection path based on a fast marching tree method in combination with spatial position information; controlling the unmanned aerial vehicle to fly according to a path and collect image frames in real time, and executing an optical flow estimation algorithm through an edge calculation chip to obtain a pixel motion vector; associating the motion vector with a space coordinate corresponding to each inspection point in the inspection path, dividing an optical flow detection area and distributing an initial detection weight; recognizing a dynamic abnormal area according to the motion features, extracting image data and space coordinates, and driving an acousto-optic load assembly carried by the unmanned aerial vehicle to respond; and based on the space coordinates of the abnormal region, re-executing the fast marching tree method to generate a local update path, and adjusting the detection weight of the related region. According to the invention, dynamic sensing and path updating linkage in the unmanned aerial vehicle inspection process can be realized.

The invention relates to the technical field of computer vision, in particular to an image target detection system and method based on deep learning, and the system comprises a dynamic feature alignment unit, a motion blur compensation unit and a feature fusion control unit. A dynamic feature alignment unit generates spatial deformation parameters through a deformable convolutional layer and an offset prediction sub-network, resamples a shallow high-resolution feature map, and realizes deep and shallow feature space alignment, and a motion blur compensation unit generates a motion vector based on brightness gradient field difference, constructs a mask and weights a suppression blur region, so as to realize deep and shallow feature space alignment. The feature fusion control unit analyzes local entropy and target size distribution, dynamically distributes feature weights and feeds back and optimizes offset parameters, a closed-loop learning loop is formed by the method, and the problems of inaccurate feature alignment, fuzzy interference and poor scene adaptation are solved.

A method for coding a block of video data using affine mode includes determining a refined affine model for the current block of video data from a linear regression process using a base motion vector field and a guidance motion vector field as inputs to the linear regression process. The method further includes determining affine merge candidates for the current block using the refined affine model, and coding the current block of video data using the affine merge candidates.

The invention discloses a multi-scale linear array camera splicing method and system based on point cloud. The method comprises the following steps: completing acquisition and preprocessing of point cloud data and image data of a target area; determining an overlapping region range between adjacent images; extracting spatial structure characteristics in the point cloud data, and performing multi-scale hierarchical decomposition on the point cloud through a multi-scale segmentation method; meanwhile, multi-scale image feature extraction is carried out on the images of the linear array camera; solving gradients in X and Y directions by adopting an optical flow method aiming at any pixel in the overlapping region, and calculating a motion vector between the pixels; fusing the optical flow information obtained under each scale, and constructing a globally consistent optical flow vector field; according to the fused optical flow vector, calculating to obtain a geometric transformation matrix of the whole overlapping region; and after image transformation and alignment are completed through the transformation matrix, fusion processing is carried out on overlapped areas. And the unification of the visual effect and the spatial integrity of the spliced image is ensured.

The invention provides a multi-head gun-type camera multi-angle intelligent tracking method and system, and the method comprises the steps: obtaining image flow data through a multi-head gun-type camera, building a D-NeRF model through combining with an improved multi-layer perception mechanism, outputting a virtual viewpoint image, a scene depth image, a shielding relation image and a motion vector field according to the D-NeRF model, obtaining a volume density field, and carrying out the multi-head gun-type camera multi-angle intelligent tracking. Analyzing the volume density field, acquiring a continuous aggregation area and performing cutting operation, generating a target instance, acquiring a bounding box and a centroid coordinate in combination with a scene depth map, acquiring appearance characteristics and morphological characteristics and performing matching, generating an identity recognition result, performing trajectory prediction through physical constraint, generating a probability space-time trajectory and performing evaluation, and performing identification on the probability space-time trajectory. According to the method, the uncertain area is obtained, the motion of the camera is subjected to joint optimization in combination with the probability space-time trajectory, the intelligent control instruction is generated, and multi-angle intelligent tracking is performed according to the intelligent control instruction, so that the intelligent level of multi-camera monitoring and tracking in a complex scene is improved.

A method of encoding information about a motion information predictor, comprising: selecting one of a plurality of motion information predictor candidates; and encoding information for identifying the selected motion information predictor candidate using CABAC coding, wherein the CABAC coding comprises, for at least one bit of said information, using the same context variable used for another inter prediction mode when one or both of a Triangle Merge mode or a Merge with Motion Vector Difference (MMVD) Merge mode is used.

Implementations are described for determining, for a block being encoded in a picture, at least one predictor candidate, determining for the at least one predictor candidate, one or more corresponding control point generator motion vectors, based on motion information associated to the at least one predictor candidate, determining for the block being encoded, one or more corresponding control point motion vectors, based on the one or more corresponding control point generator motion vectors determined for the at least one predictor candidate, determining, based on the one or more corresponding control point motion vectors determined for the block, a corresponding motion field, and encoding the block based on the corresponding motion field.

The invention discloses a five-dimensional motion vector real-time construction and calibration method based on multi-sensor fusion, and relates to the technical field of multi-sensor information fusion and dynamic state estimation, and the method comprises the steps: collecting and preprocessing motion carrier data, obtaining a preprocessing data set and a feature data set, inputting the feature data set into a long short-term memory network model, and obtaining a multi-sensor fusion model; and outputting a sensor error offset prediction vector to the extended Kalman filtering model, outputting a preliminary five-dimensional motion vector, and performing consistency verification and correction on the preliminary five-dimensional motion vector through a kinematics constraint equation to obtain a corrected five-dimensional motion vector so as to drive a virtual model corresponding to a motion carrier to perform synchronous position and attitude updating. According to the method, the sensor error is predicted through the long-short-term memory network model, and the dynamic motion model constraint module is additionally arranged to perform physical constraint correction, so that the problems of inaccurate error compensation and lack of physical authenticity of the calculation result under the dynamic working condition are solved, and the construction precision and reliability of the five-dimensional motion vector are improved.

The invention relates to the technical field of rehabilitation robot control, and particularly discloses a gait prediction compensation admittance control method and system for lower limb rehabilitation exoskeleton, and the method comprises the steps: inputting a multi-dimensional motion vector into a time sequence prediction model, and outputting a prediction result of a gait phase at a future moment; if the maximum probability output by the time sequence prediction model is lower than a preset threshold value, automatically switching to a pre-constructed rule criterion to carry out gait phase recognition; an admittance control parameter matched with the gait phase prediction result is called from an admittance parameter mapping table, and a main control torque is calculated based on the called admittance control parameter; calculating a compensation torque based on the deviation between the actual human-computer interaction force and the expected interaction force; and the main control torque and the compensation torque are superposed to serve as a final torque driving instruction of the exoskeleton motor. Real-time gait prediction, admittance parameter self-adaptive adjustment and interaction force error dynamic compensation can be fused, and high-flexibility and high-safety man-machine collaborative rehabilitation training is achieved.

The invention discloses an airborne low-detectability photoelectric long-distance stable tracking and aiming method based on a time-sensitive opposite target, and belongs to the technical field of airborne photoelectric detection and target tracking. The method comprises the following steps: step 1, target detection and miss distance calculation: in a level flight section of an airplane, imaging a target in a field of view, carrying out target detection by adopting an optical flow method, and detecting a moving target by analyzing a motion vector of pixel points in an image sequence; and resolving the deviation amount of the target image relative to the visual axis in real time, namely the target pixel miss distance. 2, designing a stable tracking loop: designing a two-layer control structure for the stable tracking loop, stably inhibiting high-frequency disturbance by a gyroscope of an inner loop, performing tracking correction on low-frequency deviation by an outer loop based on pixel miss distance, and finally realizing high-precision tracking. On the basis of real-time pixel miss distance feedback, a two-layer control structure is designed, the miss distance fed back by an outer ring tracking loop is converted into an inner ring control instruction, and the tracking precision and stability are improved.

The invention relates to the technical field of wind power generation, in particular to a wind turbine generator deformation vibration monitoring device based on millimeter wave radar. The core function module comprises a benchmark sensing radar, a blade monitoring radar, a reference beacon, an inertial measurement unit, a data synchronization and acquisition unit and a signal processing unit; a reference beacon fixed on a substrate is detected through a reference sensing radar, an absolute motion vector A of the reference sensing radar is solved, then the absolute motion vector A is converted into an absolute motion vector B of a blade monitoring radar, then data C measured by an IMU (Inertial Measurement Unit) is converted into data D of the blade monitoring radar in a coordinate system of the blade monitoring radar, and the blade monitoring radar can be used for monitoring the blade. The absolute motion vector E of the blade monitoring radar is fused and extracted through an extended Kalman filter, and is finally used for compensating original data of blade measurement by the blade monitoring radar, so that the influence of cabin motion on deformation vibration monitoring is eliminated.

The invention provides a micro-expression recognition method based on brain-like vision. The method comprises the following steps of: dividing an event flow output by an event camera according to fixed time steps, constructing a multi-time-step event frame sequence, and synchronously acquiring an initial grayscale image as static texture prior; extracting high-dimensional features of each time step event frame through an event encoder sharing parameters, and generating a corresponding pixel-level motion vector field; performing deformation sampling on the initial grayscale image by using the motion vector field to generate a reconstructed image of the target time step; and splicing the initial grayscale image and the deformed image in a channel dimension, inputting the spliced image into an image refining network for fusion optimization, and outputting a continuous grayscale image sequence. According to the method, the asynchronous event data output by the event camera can be effectively converted into a high-quality continuous image sequence, and the time sequence modeling precision and robustness of micro-expression recognition are improved.

The invention relates to the technical field of VR panoramic road safety illegal behavior snapshot scheme design, in particular to a VR panoramic road safety illegal behavior snapshot method and system. The method comprises the following steps: acquiring an intersection panoramic video stream through a single panoramic device; illegal evidence obtaining is triggered based on space overlapping of the vehicle track and the electronic fence; synchronously judging a red light running behavior by combining a signal lamp state; identifying retrograde motion by comparing the motion vector with the lane direction vector; all-weather overspeed monitoring is realized by using a millimeter wave radar; and generating an evidence packet containing the two-dimensional panorama and the three-dimensional reconstruction model. The system is composed of a panoramic acquisition module, an electronic fence module, a behavior analysis module, an illegal evidence obtaining module and a data communication module. The method has the core advantages that all-intersection coverage is achieved through single equipment, the illegal recognition accuracy is improved through multi-source data fusion, the evidence effectiveness is enhanced through the three-dimensional reconstruction technology, and the law enforcement cost and the dispute risk are remarkably reduced.

The invention discloses a monitoring camera image anomaly detection method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: obtaining an original video stream, extracting a pixel matrix of a current frame, carrying out the joint coding of the pixel matrix and a motion vector field of an adjacent frame into a quantum state bit vector, inputting the quantum state bit vector into a pre-constructed anomaly detection operator, and carrying out the detection of the anomaly. Executing quantum state evolution calculation in the Hilbert space, and outputting a two-dimensional distribution matrix of each pixel region; integrating the marked binary image and the physical verification conclusion, and when the quantum anomaly probability value reaches the alarm standard and passes the physical verification, outputting an anomaly alarm signal; and the abnormal alarm signal is converted into a confrontation training sample, a cross-scene virtual sample is generated through quantum noise injection, and an abnormal detection operator is dynamically updated. According to the method, the Hilbert space quantum state evolution step is combined, unitary transformation is applied to the quantum state bit vector, the orthogonal projection operation is executed, the accurately quantified abnormal probability distribution matrix is generated, and accurate assessment of the abnormal risk is achieved.