4629 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.

A dynamic adaptive depth camera occlusion detection method, system and device, and a storage medium, the method comprising: step T1: constructing a three-frame time sequence analysis window, continuously obtaining infrared speckle image sequences at different moments and corresponding dense depth map sequences, and establishing a pixel-level space-time alignment mapping relationship; t2, calculating a motion vector between adjacent frames, and executing the step T3 when the average motion intensity exceeds a dynamic threshold value; wherein the dynamic threshold is determined by the dense depth map; t3, positioning a speckle feature point in the infrared speckle image, generating an analysis window by taking the speckle feature point as a center, calculating a gradient change rate, and when the gradient change rate is smaller than a gradient threshold value, marking the region as a primary candidate region; and T4, carrying out space-time consistency comparison on the primary candidate region and two adjacent frames, and reserving a shielding region of which the variation value is smaller than a variation threshold value in three continuous frames. The method is high in precision, good in real-time performance and high in stability.

The invention discloses a system for improving the applicability of an intelligent camera, and relates to the technical field of intelligent camera shooting. RGB, infrared and depth sensors are used for cooperative sensing, a dynamic inter-frame interpolation algorithm is used for complementing video data, target trajectory prediction is carried out based on a target motion vector analysis algorithm in combination with a recurrent neural network LSTM + Kalman filtering, and the applicability of the intelligent camera is improved. Meanwhile, three-dimensional scene modeling and shielding recovery are introduced, and environment information complementation of a dead angle area is optimized; through a cross-camera target identity re-identification ReID algorithm, seamless tracking is carried out among multiple cameras by utilizing appearance characteristics, gait characteristics and biological characteristics of a target, and meanwhile, PTZ camera control is optimized in combination with reinforcement learning, and the angles of the cameras are dynamically adjusted; a generative adversarial network GAN based on space-time consistency is adopted, historical video data and environment modeling are combined, missing pictures in visual dead angles are intelligently complemented, and the continuity and authenticity of monitoring pictures are improved.

The invention provides a lightweight AI personnel perception method and system based on a millimeter wave radar, and relates to the technical field of personnel perception, and the method comprises the steps: carrying out the phase compensation, amplitude calibration and bidirectional combined filtering processing of a millimeter wave echo signal, and constructing three-dimensional space point cloud data. Then, constructing a spatial distribution matrix based on the point cloud data, calculating the center position of a point cloud cluster, establishing an adaptive sampling radius to extract point cloud features, and calculating a motion vector and attitude parameters; and finally, inputting the motion vector and the attitude parameter into a double threshold judgment module, judging whether a person exists in the target area, generating a person position thermodynamic diagram based on the center position of the point cloud cluster, and outputting a person perception result. According to the method, the calculation complexity is reduced, the real-time performance and accuracy of personnel perception are improved, and the method is suitable for smart home, security monitoring and other scenes.

The embodiment of the invention provides a dynamic point cloud compression method and device based on time sequence perception, and the method comprises the steps: carrying out the sparse convolution processing of two continuous frames of input point cloud data, and obtaining point cloud features in a feature space, carrying out local and global feature extraction on representations of two continuous frames in the feature space, splicing and fusing to obtain a motion vector, and carrying out motion compensation on point cloud features of the previous frame by utilizing the accurate motion vector to obtain a prediction frame of the current frame; calculating a residual error between a prediction frame and an actual frame, carrying out compression coding to obtain a residual error tensor, adding the prediction frame and the residual error tensor to obtain a coarse-grained prediction feature, and carrying out time alignment and feature fusion on the coarse-grained prediction feature and the three-frame point cloud features stored in the buffer region to obtain a fused prediction feature; performing up-sampling reconstruction on the fused prediction features to obtain a final point cloud frame; according to the invention, the accuracy and efficiency of point cloud data compression can be effectively improved.

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 a weak network environment video compression method based on deep learning, and relates to the technical field of video compression, and the method comprises the following steps: 1, carrying out the feature extraction of video content through a deep learning model; 2, dynamically adjusting video compression parameters according to the extracted features and real-time network conditions; step 3, compressing the video by using a self-adaptive coding strategy; according to the video compression method based on deep learning, the video compression efficiency and adaptability in a weak network environment are remarkably improved, the video content is subjected to feature extraction by using the deep learning model, key information in the video can be intelligently identified, such as a motion vector, texture complexity and scene change, and the video compression efficiency and adaptability are improved. Therefore, a more accurate compression strategy is realized, the compression ratio of the video data is optimized, and the smoothness and the stability of video playing are ensured to be kept under variable network conditions.

The invention discloses a security and protection monitoring video real-time transmission method based on the Internet of Things, and relates to the technical field of security and protection monitoring video transmission, and the method specifically comprises the following steps: when a texture repetition region exists in an image frame, determining all macro blocks forming the texture repetition region in the image frame, and marking the macro blocks as texture repetition blocks; performing comprehensive analysis on each texture repetition block, and evaluating a candidate motion vector direction dispersion degree when an image frame in the security and protection monitoring video has a texture repetition area; based on the evaluation result, determining whether to classify each texture repetition block and whether to respectively match a corresponding motion vector selection strategy; and according to the matched motion vector selection strategy, respectively executing corresponding dynamic regulation and control operations. According to the method, the problem that the direction of the motion vector is discrete and uncontrollable in a texture repetition area is solved, dynamic regulation and control of a coding strategy and synchronous optimization of a decoding end are realized, and the image reconstruction precision and the intelligent analysis stability are improved.

Systems, apparatuses, methods, and computer-readable media are described for determining and / or coding geometry and attribute information of a point cloud frame associated with content. The attribute information associated with a reconstructed geometry of a point cloud frame may be coded (e.g., encoded and / or decoded) based on attribute motion vectors associated with the point cloud frame. The attribute motion vectors may be used to determine an attribute motion-compensated point cloud frame.

Disclosed in the present invention are a UWB-based positioning apparatus and positioning method for a point target of a pointing apparatus. The positioning apparatus comprises a pointing apparatus and a receiving apparatus, wherein UWB antennas are respectively arranged on the pointing apparatus and the receiving apparatus, and an accelerometer and a gyroscope are further arranged on the pointing apparatus. By means of the pointing apparatus and the receiving apparatus, UWB-based positioning is performed to obtain coordinates of the pointing apparatus in a coordinate system in which the receiving apparatus is located; then by means of the accelerometer, a motion vector of the pointing apparatus is obtained, such that a linear equation for a corresponding pointing direction of the pointing apparatus is obtained; and by means of solving an intersection point of the linear equation and a pointing area, coordinates of a pointing point are determined, thereby achieving a design purpose.

The invention relates to the technical field of network video monitoring, in particular to a terminal equipment collaborative video monitoring method and system, which comprises the following steps of: monitoring video data based on terminal equipment, extracting a motion vector, a pixel intensity value and a color histogram of each frame, carrying out space-time alignment, calculating an average value and a variance of each parameter, and calculating the average value and the variance of each parameter; time sequence characteristics are analyzed, parameter changes of key frames are extracted, and an initial behavior characteristic set is established; according to the method, the motion vector, the pixel intensity and the color histogram of each frame of video are analyzed, and the data are subjected to space-time alignment and statistical analysis, so that the understanding of a behavior mode is improved, and clustering analysis helps a system to efficiently identify abnormities, monitor in real time and calculate parameter deviation to ensure that abnormal behaviors are found in time; the adaptability and efficiency of the system to the monitoring environment are further improved through data exchange and collaborative analysis between the terminal devices, and the coverage range and the response speed of the monitoring system are remarkably enhanced through the highly collaborative processing mode.

Methods, apparatuses, and non-transitory computer-readable storage media are provided for video coding. In one method, a decoder derives a first and a second reference pictures for a current coding block; the decoder derives a first and a second predictor samples based on a motion vector associated with the first reference picture using a motion compensation process from the first reference picture; the decoder determines whether the first and the second predictor samples are out-of-boundary (OOB), and in response to determining the first or the second predictor sample is OOB, the decoder replaces the first or the second predictor sample that is OOB with a first or a second alternative predictor sample; and the decoder generates the final prediction sample by averaging sample values of the first predictor sample and the second predictor sample.

The invention provides a textile pattern dislocation identification method, which comprises the following steps of: performing geometric transformation analysis on extracted pattern contour information, calculating a relative position relationship of each part of a pattern, establishing a pattern moisture deformation model, and judging whether the pattern is dislocated or deformed by comparing the difference between the current pattern and a reference model; on the basis of the corrected pattern, adding a special waterproof mark on the surface of the wet textile, manufacturing a microscopic mark which does not influence the fabric performance, and capturing the position of the waterproof mark through infrared imaging equipment to serve as an auxiliary reference point for pattern positioning; and based on the waterproof mark position of the pattern, tracking the motion trail of the waterproof fabric, calculating the image distortion degree according to the motion vector field, correcting the moving waterproof mark fabric image in real time, and eliminating the influence of motion blur on waterproof mark identification.

Aspects of the disclosure provide a method and an apparatus for video decoding. The apparatus includes processing circuitry that determines motion information of a current block. The motion information indicates one or more reference blocks of the current block. If a motion information constraint indicates that the one or more reference blocks are within picture boundaries of respective one or more reference pictures, the processing circuitry reconstructs the current block based on the one or more reference blocks. If a first motion vector (MV) points from a region in the current block to a first reference region of a first reference block that is outside a picture boundary of a first reference picture, the processing circuitry determines a first clipped MV pointing from the region to an updated first reference region by clipping the first MV where the updated first reference region is within the picture boundary.

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.

An image encoder or decoder includes circuitry and a memory coupled to the circuitry. The circuitry, in operation, predicts a first set of samples for a first partition of a current picture with one or more motion vectors including a first motion vector and predicts a second set of samples for a first portion of the first partition with one or more motion vectors from a second partition different from the first partition. The samples of the first set of samples of the first portion of the first partition and of the second set of samples of the first portion of the first partition are weighted. A motion vector for the first portion of the first partition is stored which is based on one or both of the first motion vector and the second motion vector. The first partition is encoded or decoded using at least the weighted samples of the first portion of the first partition.

The invention relates to the technical field of image optimization recognition, in particular to a deep foundation pit crack detection method and device for deep foundation pit tunneling. According to the method, a deep foundation pit image and a motion vector of an image acquisition device are obtained; and blurred images in the deep foundation pit images are screened out, and rigid edges are identified. And screening out the smear edge by using the position relationship between other edges and the rigid edge, the pixel value change stability on the path and the correlation degree between the edge vector and the motion vector. And constructing a two-dimensional fuzzy kernel, and adaptively setting a fuzzy kernel value by using gradient information and position information of pixel values in the two-dimensional fuzzy kernel. And performing deblurring on the blurred image through the two-dimensional blurring kernel, and performing crack identification. The feature information in the blurred image is extracted and analyzed, the smear effect in the image is quantified, the blurred kernel in the image is determined in a self-adaptive mode, deblurring is carried out, the quality of the deep foundation pit image is improved, and the accuracy of a crack recognition result is improved.

The invention provides an image quality enhancement method and system based on a cloud game, and the method comprises the following steps: firstly, a cloud end renders a game picture and generates an initial video stream; then, performing preliminary optimization on the video stream, and reducing jumping and frame loss through motion compensation of an inter-frame motion vector; the optimized video stream is transmitted to a user side, after the user side receives the video stream, image quality enhancement processing is carried out, specifically, denoising processing is carried out, and random noise and compression noise are eliminated by combining an inter-frame difference method and spatial domain bilateral filtering; sharpening enhancement: enhancing image edges and texture details by using a Laplacian operator; artifact removal: removing compression artifacts through frequency domain filtering; the dynamic contrast is enhanced, and gray level distribution is adjusted through histogram equalization; finally, the video stream after enhancement processing is presented to a user in real time, the image quality of the cloud game can be remarkably improved by gradually updating the processing result of each pixel point, the video stream is presented in real time in a high-quality mode, and the user experience is optimized.

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.

An example device for decoding video data includes a memory configured to store video data; and one or more processors configured to: decode data representing an initial motion vector for a current block of the video data, the initial motion vector having integer-motion vector difference (MVD) precision; determine a search range around a reference area identified by the initial motion vector in a reference picture; perform a template matching search process in the search range to identify a best matching region; determine error values for neighboring pixels to the best matching region; use the error values for the neighboring pixels to perform a model-based fractional-pixel motion vector refinement to derive motion vector difference values; apply at least one of the motion vector difference values to the initial motion vector to determine a refined motion vector for the current block; and decode the current block using the refined motion vector.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, for a conversion between a current video block of a video and a bitstream of the video, at least one history-based motion vector prediction (HMVP) table of the current video block based on at least one of the following: a motion vector (MV) difference threshold for comparing at least two MVs of at least two HMVP candidates, a coding mode of the current video block, or a reference picture list index associated with the current video block; and performing the conversion based on the at least one HMVP table.

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.

In a method of video decoding performed by a decoder, a current picture is obtained from a coded video bitstream. The current picture is divided into a plurality of units and divided into a plurality of tiles. Each tile includes at least one unit of the plurality of units. A first current unit in a first tile of the plurality of tiles is decoded. A first HMVP buffer is updated with a motion vector of the first current unit that has been decoded. A position of the first current unit in the first tile of the plurality of tiles is determined. The first HMVP buffer is reset when the first current unit is located in a first column of the first tile.

Disclosed herein is a method of transmitting point cloud data. The method may include encoding geometry data of the point cloud data, encoding attribute data of the point cloud data based on the geometry data, and transmitting the encoded geometry data, the encoded attribute data and signaling data, the geometry encoding includes splitting the geometry data into one or more prediction units, and inter-prediction encoding the geometry data by selectively applying a motion vector to each of the split prediction units, and the signaling data includes information for identifying whether the motion vector is applied for each prediction unit.

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 discloses a dynamic object suppression video image stabilization method based on depth information and global consistency, and the method comprises the steps: extracting key points in a video, carrying out the optical flow estimation, and obtaining the depth information of the key points; a static weight and a motion vector residual error are calculated based on the motion information, the depth information and the global homography matrix of each key point, a dynamic weight is obtained based on an attention mechanism, the dynamic weight and the static weight are fused to obtain a comprehensive weight, and a weighted motion residual error is calculated based on the comprehensive weight and the motion vector residual error; spreading the motion information to grid vertexes, obtaining dense residual motion based on weighted motion residual prediction, and accumulating the dense residual motion to obtain a track; and carrying out smoothing and motion compensation on the track and outputting a stable video sequence. The problem that in a complex scene, motion of a dynamic object causes distortion and inaccuracy of a stabilization result is solved, interference of the dynamic object can be effectively suppressed, and the effect and robustness of video image stabilization are improved.

The invention discloses a multistage video watermark embedding and extracting method and system, and the method comprises the steps: carrying out the perception analysis of an original video through a space-time attention neural network, and dynamically dividing a time domain stable region and a space domain sensitive region; a three-level watermark system is constructed, the three-level watermark system comprises a basic-level watermark, a first-level enhanced watermark and a second-level enhanced watermark, the basic-level watermark adopts an anti-rotation binary matrix generated by a Fourier descriptor, the first-level enhanced watermark is a DCT frequency domain Hash sequence, and the second-level enhanced watermark is a semantic fingerprint generated based on a motion vector. According to the method, through multi-dimensional collaborative optimization of the anti-attack capability, the dynamic security mechanism and the verification efficiency, the core defects that erasing is easy due to insufficient reversibility in a traditional video watermarking technology and counterfeit affiliation chaos is caused by a static key are overcome, and reliable technical guarantee is provided for digital content copyright protection.

This disclosure relates to method and system for inspection of rotational components based on video frames having different camera views of defects on rotational components. Using optical flow to ascertain motion vectors of pixels of the video frames, the video frames are partitioned based on motion vectors. The partitioned regions are paired with corresponding regions in a subsequent video frame, and their features are matched. For video frames having at least camera motion, a transformation matrix is ascertained which is applied to map defect trajectories of each camera view to a subsequent camera view.