2485 results about "Motion vector" patented technology

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

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 discloses an eVTOL multi-camera cooperative video compression coding method based on multi-source perception and intelligent partitioning. The method comprises the following steps: constructing a scene three-dimensional perception model by fusing multi-source data of visible light, infrared and depth sensors; the method comprises the following steps: realizing dynamic video partitioning based on motion vectors and semantic analysis, and dividing a picture into a core region, a secondary region and a background region; establishing a parallax compensation motion prediction model by adopting a cross-camera reference frame sharing mechanism; and high-fidelity compression of the key area is realized through layered entropy coding and a dynamic quantization parameter distribution strategy. And the decoding end reversely executes multi-source data fusion and partition reconstruction according to the coding metadata. The method is suitable for eVTOL multi-camera video real-time transmission scenes such as polling, surveying and mapping.

The invention relates to the technical field of solar cell processing, in particular to a laser light-transmitting scribing control method and system for a solar thin-film cell. The control method comprises the steps that a large-size substrate is divided into a plurality of galvanometer view fields through a dynamic flight continuous machining mode, a continuous snake-shaped machining path is generated, the laser machining platform is driven to continuously move at the constant speed, and the galvanometer scanning system is synchronously triggered to start lineation in a splicing area in advance. According to the method, a time domain coupling equation of galvanometer scanning frequency-platform movement speed-laser pulse sequence is established, and dynamic compensation of an optical path and self-adaptive adjustment of energy density are achieved in the continuous machining process. Based on a view field expansion algorithm of a nonlinear kinematics model, the phase deviation of a scanning vector and a motion vector is effectively inhibited, and the geometric continuity of a splicing interface in ultra-long stroke machining is guaranteed.

The invention relates to the technical field of image target tracking, and discloses a video image target tracking method and processing device based on multi-feature fusion, and the method comprises the steps: obtaining an initial position parameter and a current frame detection parameter of a target; calculating according to the initial position parameter to obtain a pixel-level motion vector field; performing prediction according to the pixel-level motion vector field to obtain a prediction position parameter of the target; comparing the predicted position parameter with the current frame detection parameter, and when the difference between the predicted position parameter and the current frame detection parameter is smaller than a preset threshold value, judging that the target is not shielded; and when the difference between the predicted position parameter and the current frame detection parameter is greater than a preset threshold value, tracking error accumulation may be caused by too early view angle switching in the prior art, and in a resource-limited scene, the calculation overhead of a deep learning model is generally large, resulting in poor tracking accuracy and real-time performance.

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 discloses an extreme compressed video coding method based on an intelligent reference frame, comprising the following steps: S1, acquiring an original video stream, dividing the original video stream into frame groups according to a time sequence, each group comprising a current frame and a candidate reference frame; s2, carrying out region positioning on the candidate reference frame, extracting motion, edge and background regions, and generating a mapping graph; s3, calculating a quality score based on the mapping graph according to a motion vector, gradient change and a region overlapping rate; s4, selecting three frames with highest scores to form a reference set, and establishing an index structure; s5, predicting the current frame by using the reference set to generate a predicted frame and a residual error; s6, multi-path coding cost is calculated, and a path with the minimum cost is selected; and S7, entropy coding is carried out on the reference index, the motion vector, the residual error and the control parameter, a code stream is output, and calling information is recorded. According to the method, the compression ratio and prediction precision of video coding are improved, the image quality is maintained while the code rate is reduced, and the method is suitable for efficient transmission and storage of high-resolution videos.

The invention relates to an unmanned aerial vehicle detection countering method, which comprises the following steps of: deploying a multi-dimensional sensing network to carry out full-band instantaneous scanning, synchronously acquiring noise characteristics of radio signals, material characteristics of a terahertz wave band and Doppler effect characteristics, mapping to a virtual twinborn body, reconstructing physical parameters, a communication protocol and a behavior mode of a target unmanned aerial vehicle, performing depth comparison by combining a disguise sample library generated by the antagonism generation network, and generating a holographic target file containing a three-dimensional motion vector, an energy feature and a potential threat intention; a target quantum encryption communication link is interfered through a quantum entanglement signal, a controllable plasma cloud cluster is generated, navigation and image transmission signals of the controllable plasma cloud cluster are selectively attenuated, the controllability of the target is judged, and an uncontrollable target is repelled through coded sound waves; and sending a control instruction containing a biological heuristic obstacle avoidance algorithm in stages, and guiding the control instruction to an intelligent recovery cabin. According to the invention, accurate detection, intelligent identification, safe countering and reliable takeover of the unmanned aerial vehicle are realized, and the airspace safety guarantee capability is improved.

The invention discloses a millimeter wave radar meteorological target detection method based on deep learning, and relates to the technical field of meteorological radar processing. The method comprises the following steps: acquiring a millimeter wave radar original signal by using a transmission control protocol for preprocessing; the distance and the angle between an object and an antenna are measured through a millimeter wave radar, so that three-dimensional space coordinates of the measured object are obtained; converting the three-dimensional space coordinates through time alignment and a space coordinate system, and projecting the three-dimensional space coordinates into a visual coordinate system; pre-training a feature extractor, analyzing a data label by using ECMWF, and then performing end-to-end fine tuning; and automatically optimizing the detection threshold according to the signal-to-noise ratio, and outputting target meteorological classification, meteorological intensity and meteorological motion prediction. According to the method, the detection threshold is automatically optimized according to the signal-to-noise ratio, target classification, intensity estimation and motion vector prediction are output, weather weak signal leak detection is avoided, and the target tracking capability and the extreme weather generalization capability 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 relates to the technical field of image recognition, in particular to an AI-based park intrusion detection and response system, which comprises a target trajectory acquisition module, an event density analysis module, a disturbance behavior recognition module and a response joint control execution module. According to the invention, through fusion of the spatial-temporal characteristics of the target trajectory and motion vector pattern recognition, intrusion behavior dynamic risk assessment and early warning collaborative optimization is realized, and through combination of multi-dimensional data association calculation and event density increment monitoring, abnormal situation sensing sensitivity and discrimination accuracy in a complex scene are improved. Motion vector time sequence modeling and disturbance feature extraction are utilized, the capacity of capturing camouflage behaviors and irregular motion is enhanced, an environment perception and response execution closed-loop control link is constructed by means of intelligent matching and parameter self-adaptive adjustment of multi-source sensing equipment, the dependency degree of manual review is remarkably reduced, the delay from recognition to treatment response is shortened, and the recognition efficiency is improved. And an early warning research and judgment and linkage disposal full-process automation system is formed.

The invention provides a writing standard monitoring method and electronic equipment. The method comprises the steps that a dynamic contact state diagram changing along with a time sequence is generated; acquiring a continuous image sequence in the writing process by using a camera device at the upper end of the electronic equipment, and extracting pen point movement track characteristics and finger joint movement vectors; performing space-time alignment on the stroke sequence features decomposed from the continuous image sequence and the dynamic contact state diagram, and establishing a dynamic trajectory comparison model based on bimodal data fusion; performing multi-dimensional fusion on a space-time matching degree parameter output by the dynamic track comparison model and a pressure distribution parameter in the dynamic contact state diagram, and generating a continuously changing dynamic standard degree parameter through weighting; and when the local stroke segment deviates from the standard threshold value, activating the corresponding spectrum feedback signal according to the deviation type. According to the method, the robustness of track detection is improved through tactile-visual sensor fusion, and correlation analysis of writing elements is realized by means of space-time modeling.

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.

The method includes receiving an image block unit of a frame, the image block unit including samples in a first chrominance channel, a second chrominance channel, and one luminance channel; defining a co-located reference area across a luminance component of the luminance channel, a chrominance component of the first chrominance channel, and a chrominance component of the second chrominance channel; performing motion compensation prediction of the luminance component, the first chrominance component, and the second chrominance component using the reference frame and a motion vector; obtaining a first mapping function that maps the luminance component to the first chrominance component and a second mapping function that maps the luminance component to the second chrominance component using the co-located luminance prediction and chrominance prediction; reconstructing a luminance residual; and obtaining a first chrominance prediction by using the first function and the luminance residual and obtaining a second chrominance prediction by using the second function and the luminance residual.

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 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.

A method includes determining, for a conversion between a video block of a video and a bitstream of the video, a size of prediction block corresponding to the video block according to a rule. The method also includes performing the conversion based on the determining. The rule specifies that a first size of the prediction block is determined responsive to whether a prediction refinement using optical flow technique is used for coding the video block. The video block has a second size and is coded using an affine merge mode or an affine advanced motion vector prediction mode.

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 discloses a motion vector refinement for oversampling of time domain spread. A residual network is used to predict a set of residual motion vectors that provides additional motion data for portions of frames for which motion vectors are not provided, such as animation textures, mirrored / reflective objects, and / or moving objects without motion information.

To provide a method and apparatus for performing intra-frame prediction based on the usability of reference pixels to improve encoding performance.SOLUTION: An intra-frame prediction method in color copy mode includes confirming a specified reference pixel area for acquiring correlation information, determining reference pixel processing settings on the basis of the usability determination of the reference pixel area, and performing intra-frame prediction using the determined reference pixel processing. In addition, the intra-frame prediction method includes generating a candidate list for predicting motion information of the current block, deriving a control point vector of the current block on the basis of the candidate list and candidate index, deriving a motion vector of the current block on the basis of the control point vector of the current block, and performing inter-frame prediction for the current block using the motion vector.SELECTED DRAWING: Figure 14

The invention relates to the technical field of laser radar meteorological detection, in particular to a wind field feature detection method and device based on a laser radar, and the method comprises the following steps: S1, controlling the laser radar to execute dynamic hierarchical scanning, and generating three-dimensional laser scanning data; s2, carrying out aerosol backscattering signal preprocessing on the three-dimensional laser scanning data; s3, extracting an aerosol motion vector of each range gate, and decomposing the aerosol motion vector into a horizontal wind field component and a vertical wind field component; s4, performing spatial gradient calculation to generate a spatial gradient distribution map; s5, respectively calculating a horizontal energy spectrum and a vertical energy spectrum; and S6, outputting wind field characteristic parameters. According to the invention, through fusion of the dynamic features of the laser radar point cloud, the wind shear space gradient and the energy spectrum analysis method, precise extraction of the turbulence intensity, the main energy scale and the three-dimensional vortex features is realized, and the accuracy and the spatial resolution of wind field structure identification are improved.

The invention relates to the field of hydrological monitoring, in particular to a hydrological monitoring early warning method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a panoramic continuous aerial image of a to-be-monitored area; adaptive histogram equalization and image time delay interpolation optimization are carried out on the panoramic continuous aerial image of the to-be-monitored area to construct a time delay optimized continuous image; performing water body landform semantic analysis on the time delay optimized continuous image, performing real-time three-dimensional landform modeling, and constructing a real-time three-dimensional landform model; performing continuous frame optical flow tracking on the time delay optimized continuous image, performing inter-frame optical flow motion vector calculation, and generating a water level fluctuation curve and surface ponding area change characteristics; and carrying out instant hydrological state rendering on the real-time three-dimensional terrain model according to the water level fluctuation curve and the surface ponding area change characteristics so as to construct a dynamic hydrological state evolution model. According to the invention, accurate and efficient hydrological monitoring and early warning are realized, and the safety of the hydrological basin is improved.