5396 results about "Video processing" patented technology

A system and method for adaptive image and video processing using a Persistent Cognitive Machine (PCM) architecture with visual strategy caching. The system receives degraded input media and extracts degradation fingerprints to query a PCM-based visual strategy cache containing previously successful processing strategies. When matching cached strategies are found above a relevance threshold, they are retrieved and applied directly. When no match exists, the input is processed through transform-domain networks to generate new strategies. A pattern synthesizer combines multiple strategies for complex degradation types. The system evaluates processing effectiveness using a feedback controller and stores successful strategies in the hierarchical cache. This cognitive approach enables real-time processing with continuously improving performance as the cache learns from successful patterns. The adaptive architecture eliminates redundant processing while maintaining high-quality output, making it suitable for diverse imaging and video applications requiring efficient enhancement capabilities with superior performance over traditional methods.

The invention relates to the technical field of resource allocation, in particular to a multi-channel video stream cooperative transmission method based on dynamic priority, which comprises the following steps of: acquiring task context characteristic parameters, system resource states, user behavior responses and computing node load data in real time, and constructing a priority allocation model to carry out pattern recognition to generate real-time task priority. And establishing a task scheduling strategy generation model, and performing resource allocation by adopting a priority weighting efficiency evaluation function, a memory-computing unit occupancy rate prediction matrix and a gradient optimization target under an adjustment resource constraint condition to form an initial scheduling strategy. And performing multi-dimensional parameter fusion analysis on the data through an adaptive optimization decision model, generating a resource redistribution correction vector, dynamically adjusting an initial scheduling strategy by adopting an online iterative optimization mechanism, and outputting a final real-time optimization task scheduling strategy. The task priority dynamic evaluation and the closed-loop optimization of the resource allocation are realized, and the efficient cooperative execution of the multi-channel video processing task is ensured.

The embodiment of the invention relates to the technical field of video processing, and particularly discloses a video timestamp event identification and reasoning method based on a multi-modal large model. According to the embodiment of the invention, an input video is processed, and visual information and text information are extracted; performing interaction and fusion on the visual information and the text information to generate a video event semantic representation; and performing event identification on the semantic representation of the video event to obtain an event timestamp mark and event logical reasoning. According to the method, deep fusion and semantic association understanding of multi-modal information such as vision and texts can be realized, the problem that a traditional method is insufficient in semantic consistency and feature expression ability among modals is solved, and deep fusion of multi-modal features and accurate understanding of semantic association are realized; and the frame-level time information can be used as a visual code and a text feature to perform visual text feature alignment, and then video-level modeling is performed, so that the problem that a traditional model cannot directly capture an accurate time frame when a visual event occurs is solved.

An example operation may include at least one of receiving, via a user interface of a device, an activation input from a user to initiate a session, capturing, by a camera of the device, a scan of a body of the user, wherein the capturing comprises recording at least one image and / or at least one video of the user, processing the at least one image and / or video to generate a three- dimensional model of the user comprising measurements and contours of the body, retrieving, from a database, at least one clothing item associated with the user, the at least one clothing item comprising dimensional attributes and texture attributes, rendering, by a graphics processing unit, the at least one clothing item onto the three-dimensional model to generate a visual representation, wherein the rendering simulates draping behavior, movement, and light interaction of the at least one clothing item relative to the three-dimensional model, and displaying, on the user interface, an interactive visualization comprising the visual representation of the three-dimensional model with the at least one clothing item from multiple viewing angles.

Disclosed are systems and methods for a hybrid cloud-edge computing platform using decentralized networks for solving resource-intensive computation tasks such as machine learning and video processing tasks. In some respects, the cloud-edge computing platform comprises a coordinator cluster and worker nodes partitioned into shards, which include cloud-based networks and edge networks, and which cater to a wide range of computation requirements. The coordinator cluster manages workloads by selecting the appropriate shard and worker nodes to execute tasks based on computed efficiency scores. Worker nodes may be incentivized with rewards such as cryptocurrency tokens for successful completion of tasks. The disclosed architecture also supports the pipelining of multiple tasks in a workload, where the output of one task is used as input for another task. In some respects, the computing architecture includes a blockchain-supported edge network, which enables verifying compute tasks and managing reward distribution for worker nodes using smart contracts.

The invention discloses a video style migration attitude detection method based on a graph neural network, which belongs to the technical field of video processing, and specifically comprises the following steps: preprocessing a video data stream; constructing a dynamic space-time association network, mapping pixels of the video frame into space-time diagram nodes, and converting the original video frame into a target artistic style by aggregating the space-time features of the nodes layer by layer; in the style migration process, a dynamic mask is generated for the area where the human body joint points are located, and the geometric consistency of the joint structures in stylized output is restrained; extracting joint point coordinates of each frame from the stylized video, constructing a space-time trajectory sequence, and generating a feature vector containing space-time dependence; performing multi-dimensional comparison on the feature vector and a preset action mode library, calculating a track offset and a mode matching degree, judging that the action is abnormal according to a threshold value, and outputting a detection result; according to the invention, the cooperative performance of style migration and anomaly detection is improved.

The invention discloses a power distribution fusion terminal real-time video reasoning method based on an SDNN, relates to the technical field of intelligent power grid monitoring, and aims to solve the problems of high delay, high bandwidth consumption, low power consumption and the like caused by insufficient utilization of computing power resources of an existing power distribution terminal and dependence on a cloud on video processing in the prior art. And the problem of poor adaptability of the model in a complex power grid scene is solved. Firstly, a power grid special sample library is constructed, and data quality is optimized through time domain key frame extraction and a stratified sampling mechanism; carrying out enhanced training on the lightweight model by adopting transfer learning and multi-modal confrontation; designing an intermediate representation conversion and hierarchical caching mechanism to realize efficient model compiling and heterogeneous hardware deployment; edge-side real-time reasoning is realized based on dynamic frame sampling and statistical threshold detection; finally, a self-adaptive feedback closed loop is driven through reinforcement learning, and incremental updating of the model and dynamic allocation of resources are achieved. The real-time performance of power distribution equipment anomaly detection is remarkably improved, and meanwhile the model memory occupancy rate is reduced.

Embodiments of the present invention provide a target multi-modal model system and construction method, a video processing model training method, and a video processing method. The video processing model training method comprises: inputting a video sample and each initial text sample into a video processing model, wherein the initial text sample is a text for performing category description on video content of the video sample; using the video processing model to perform feature extraction on the video sample to obtain a temporal motion feature and a fused image feature; using the video processing model to perform feature extraction on the initial text sample to obtain a dynamic text feature and a fused text feature; and training the video processing model on the basis of the temporal motion feature and the dynamic text feature, and the fused image feature and the fused text feature.

The invention provides an emergency lane dynamic management method and system, and the method comprises the steps: obtaining historical monitoring videos of all monitoring points, and carrying out the video processing of the historical monitoring videos; extracting vehicle information in the processed video, and redefining and calculating traffic flow parameters according to the extracted vehicle information; according to the traffic flow parameters, constructing a traffic flow interruption probability model, and determining a final threshold value of emergency lane opening; based on the traffic flow parameters and the final threshold, training the multiple classification models; training a traffic jam early warning model according to the traffic flow parameters; a monitoring video of a target monitoring point in a target time period is obtained, video processing and data extraction are carried out, based on the calculated real-time traffic flow parameters, a traffic jam early warning model is used for processing, and traffic flow density and an early warning point are predicted; and according to the prediction result, using multiple classification models to decide whether to open the emergency lane, and fusing all decision results. According to the invention, the use of the emergency lane is optimized.

The invention discloses a video semantic segmentation method based on a time sequence cross attention mechanism, and belongs to the field of computer vision and the field of material detection.The video semantic segmentation method comprises the steps that firstly, a video used for training is preprocessed, a frame sequence is extracted, and then a coding-decoding network for multi-level feature extraction and fusion is constructed; according to the method, feature extraction is enhanced through a time sequence cross attention module, network parameters are optimized through weighted IoU loss and binary cross entropy BCE loss, then frame-by-frame prediction segmentation is carried out on a target video by using a trained model, and a multi-classification segmentation result is exported. According to the method, a time sequence cross attention mechanism is integrated into the SAMUNet network, the segmentation precision is effectively improved for image data with time sequences, the time cost and the labor cost of material video processing are greatly reduced, the method can be widely applied to the field of industrial detection, and the product quality and the production efficiency are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a video stream processing method which comprises the steps of collecting current environment parameters, generating a mode switching instruction and determining a target processing mode; obtaining multi-dimensional context awareness data, and selecting a target detection model; key area coordinate parameters in the video frame sequence are extracted, and grading resolution parameters are determined; and based on the target processing mode, the target detection model and the grading resolution parameter, constructing a video processing strategy matrix, executing the video processing strategy matrix to perform coding processing on the video stream, and generating a target coding video stream. According to the invention, through intelligent mode switching based on the current environment parameters, dynamic adjustment of the video processing mode is realized, and the adaptive capacity of the system in a complex environment is improved; through target detection model selection in combination with multi-dimensional context awareness data, the detection precision is optimized, and the reliability of visual analysis is improved.

The invention belongs to the technical field of speech translation, and particularly relates to a video speech automatic translation method based on a pre-training model, and the method comprises the following steps: 1, preprocessing video and audio data; step 2, voice recognition and language detection; step 3, machine translation and text post-processing; step 4, speech synthesis and audio mixing; step 5, synchronizing video processing and subtitles; step 6, quality control and multi-dimensional evaluation; 7, carrying out model iteration and data closed loop; and step 8, system deployment and engineering implementation. Through deep fusion of efficient transfer learning of the pre-training model and the multi-modal technology, a high-precision, low-cost and easy-to-expand video speech translation solution is constructed, the time and labor cost of globalized content production is greatly reduced, the cross-language communication efficiency is improved, immersive multi-language experience is provided, and the method is suitable for popularization and application. And a data-driven continuous optimization mechanism is established, so that the system performance is improved along with the increase of the use scale.

The invention provides a rail transit video intelligent analysis method, medium and system, and belongs to the technical field of rail transit, and the method comprises the steps: constructing a self-adaptive resolution image hierarchical structure, and dynamically determining an optimal resolution hierarchy based on a minimum spanning tree algorithm; constructing an abnormal target movement matrix by combining an improved frame difference method with an optical flow estimation technology; applying a Hungary algorithm to realize target tracking; constructing an abnormal target change index to evaluate a behavior abnormal degree; dividing monitoring stages according to the running state of the vehicle to realize scene self-adaption; an orbit scene attention network model is introduced to identify abnormal behaviors; quantifying a risk level through an abnormal behavior risk assessment function; implementing a hierarchical processing strategy to enable a low-resolution hierarchy to be responsible for global rapid screening and a high-resolution hierarchy to be responsible for fine analysis of key areas; the risk trend is estimated in combination with the rail transit abnormal event prediction model, and the technical problems of large calculation amount and low efficiency of high-definition video processing are effectively solved.

The invention belongs to the technical field of video processing, and particularly relates to a monitoring video enhancement method for a farm, which aims to solve the technical problem of low quality of an enhanced video in the prior art, and comprises the following steps: S1, processing each frame of image in a monitoring video sequence frame by frame; s2, distinguishing a target animal area from a background area, and identifying and generating an artifact mask; s3, aiming at the background area, carrying out key smoothing processing on the artifact position to inhibit the artifact; s4, for the image of the target animal area, performing adaptive nonlinear enhancement on the brightness component, and performing color correction on the chrominance component; s5, performing pixel-level fusion on the enhanced target animal area and the background area; and S6, spreading the information of the previous frame to the current frame by using the forward optical flow field, and carrying out weighted fusion on the information of the previous frame and the current frame. According to the method, the target bred animals, the background areas and the artifacts are accurately distinguished, so that refined and differentiated processing of pictures is realized.

The invention provides a video production method and device for automatically explaining static PPT by a digital human, computer equipment and a computer storage medium, and relates to the technical field of video processing. Comprising the following steps: extracting first target text information in each sub-image from a sequence image set of static PPT; converting the first target text information in each subimage into voice, and synthesizing the obtained initial audio files corresponding to each subimage to obtain a target audio file for the static PPT; wherein a preset pause duration is added between the initial audio files in the target audio file, and a corresponding relation between the audio time of each initial audio file and the display time of each sub-image is constructed; adding a PPT transition animation with a preset pause duration between the sub-images to obtain an initial PPT video for the static PPT; and based on the target audio file, controlling the virtual digital human to explain first target text information in the initial PPT video to obtain a target PPT video. According to the method, the video playing fluency can be improved.

The invention belongs to the technical field of video processing methods, and discloses a high-performance video reasoning segmentation method based on time sequence marking. The method comprises the following specific steps: carrying out time sequence mark coding by using a multi-mode large language model, carrying out time sequence dynamic aggregation on a coded special mark, and carrying out mask decoding and propagation by using an SAM2 based on a key frame selection strategy of the time sequence mark. According to the method, multiple module designs are combined, and the time sequence marks generated by the multi-modal large model are utilized, so that the perception capability of the model on spatial features and time dynamics is effectively improved.

The invention relates to the technical field of computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

The invention relates to the technical field of video processing, and relates to a video generation method and device, a computer program product and electronic equipment. The video generation method comprises the steps that an event description text sequence is received, a reference frame feature vector of a video to be generated is determined according to the event description text sequence, and the event description text sequence comprises text description information of multiple events and corresponding starting and ending time information; performing time sequence position coding processing on the reference frame feature vector according to the starting and ending time information to obtain a video frame time sequence fusion feature, and performing time sequence position coding processing on the text description information to obtain a text time sequence fusion feature; based on a time sequence cross attention mechanism, fusing the video frame time sequence fusion feature and the text time sequence fusion feature to obtain a target fusion feature; and generating a dynamic image according to the target fusion feature corresponding to each reference frame feature vector to obtain a target video. The quality of the generated multi-event video can be improved.

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, a sign prediction of a block vector difference (BVD) of the current video block; determining the BVD at least based on the sign prediction of the BVD; and performing the conversion based on the BVD.

Provided are a video generation method, an electronic device, and a computer readable storage medium, relating to the technical fields of computers and video processing. The video generation method comprises: acquiring a target text, wherein the target text is used for describing video content to be generated (S21); and using a target video generation model to perform video generation processing on the target text to obtain a target video, wherein the target video generation model is a model obtained by performing model alignment on an initial video generation model and a preset reward model in a fine-tuning manner (S22). The present invention solves the technical problems in the related art that a video generated by a video generation model obtained by training based on network data has poor quality and does not meet user expectations.

The invention provides an intelligent self-adaptive video editing method and system based on deep learning, and relates to the technical field of video processing.The method comprises the steps that firstly, a semantic mapping relation between a to-be-edited video material and a preset editing requirement is established, and an editing requirement mapping result is generated, the preset editing demand comprises a content style and a rhythm control demand, and then semantic feature association processing is carried out based on the mapping result to obtain a semantic association feature set comprising lens unit content semantic features and rhythm association features; then calling a pre-trained editing decision model (including a semantic matching module and a rhythm adjusting module) to carry out editing strategy matching on the set, generating a preliminary editing strategy set, generating an initial video editing scheme according to the preliminary editing strategy set, carrying out parameter adjustment on the initial scheme according to a strategy optimization suggestion output by the model, and carrying out video editing on the initial scheme; and a final video editing scheme is obtained, and intelligent self-adaptive editing of the video is realized.

The invention discloses a home decoration construction site monitoring system and method based on artificial intelligence, and relates to the technical field of video processing. A video data set, an environment parameter set and an equipment state set in a historical construction process are collected in advance, an image semantic segmentation model is constructed based on the video data set, and construction scene division is performed; constructing a target detection model based on the video data set, generating a target spatial-temporal trajectory based on a detection result of the target detection model by using a multi-target tracking algorithm, and constructing a construction behavior recognition model based on the video data set and the target spatial-temporal trajectory; based on the recognition result of the construction behavior recognition model, the environmental parameter set and the equipment state set, constructing a risk early warning model; the safety condition of a construction site is evaluated in real time, and dynamic risk early warning is provided.

The invention discloses a universal scene retrieval analysis method and system based on multi-modal feature fusion, the method comprises a video analysis step and an application service step, and the application service step comprises the steps of receiving a user input request, describing a multi-dimensional standardized video tag based on a video summary, and obtaining a multi-dimensional standardized video tag; the steps of cross-modal video retrieval, dynamic knowledge enhancement question answering and interactive enhancement analysis can be executed, efficient video preprocessing is achieved by constructing an offline feature library, the retrieval precision is improved by adopting a cross-modal feature fusion technology, the analysis authority is enhanced in combination with a dynamic knowledge base, and the interactive enhancement analysis is supported to achieve abnormal early warning. The method has the advantages that the offline video processing efficiency is improved, cross-modal feature fusion retrieval is realized, and the authority of an analysis result is enhanced.

The invention provides a consistent video generation method and device based on wavelet transform and frequency decomposition, and relates to the technical field of image processing. Through a wavelet multi-stage frequency band decoupling-fusion framework and a dynamic cross-modal attention mechanism, a dynamic alignment mechanism of a distributed sensing adapter and an HJB optimization strategy of hierarchical diffusion are combined, so that the consistency video generation efficiency is improved. Identity representation fusion across space-time dimensions is achieved while parameter efficiency is guaranteed, the problems of high-frequency detail drift, dynamic scene identity distortion and poor long sequence generation stability in a traditional video generation method are solved, the quality and stability of a generated video are remarkably improved, and the video generation efficiency is improved. And more advanced technical support is provided for video processing and related applications.

The invention discloses an immersive video enhancement method and device based on frequency domain boundary collaborative optimization, and relates to the field of video processing, and the method comprises the steps: obtaining a to-be-reconstructed compressed multi-view texture and depth video sequence, and inputting the sequence into a trained immersive video enhancement model; a current to-be-enhanced video frame passes through a feature extraction module, and high-frequency features and low-frequency features are extracted respectively; the high-frequency features and the low-frequency features pass through a frequency domain enhancement module to obtain a frequency domain enhanced image; inputting the frequency domain enhancement image and the current video frame to be enhanced into a boundary enhancement module to obtain a fused image; and inputting the fused image and an adjacent video frame of the current to-be-enhanced video frame into a space-time deformable convolution module to obtain an aligned fused image, and performing prediction on the aligned fused image through a quality enhancement module to obtain an enhanced residual error and generate a corresponding reconstructed video. According to the method, the problems of compression artifacts, boundary artifacts, low quality of immersive videos and the like are solved.

The invention discloses an acousto-optic fusion bird repelling efficiency evaluation method and system, and the method comprises the steps: enabling a bird repelling device to recognize a target through an AI image, and enabling the bird repelling device to repel the target through the sound and light capability; collecting data and synchronously transmitting the data to a data analysis module; the behavior state of the birds is automatically analyzed based on image recognition and video processing technologies; comprehensively evaluating the bird repelling effect by using the efficiency evaluation index; according to historical data, a bird repelling strategy is optimized through an efficiency evaluation result; intelligent learning is introduced, and a bird repelling strategy is automatically adjusted; the system generates a regular efficiency evaluation report and feeds back data to the user; the system comprises a data acquisition module, a behavior identification and analysis module, an efficiency evaluation module and a strategy optimization module. According to the method, quantitative evaluation is carried out on the bird repelling effect, and the efficiency performance of bird repelling equipment is clarified; the effectiveness of a bird repelling strategy can be analyzed according to behaviors of birds; the bird repelling strategy is adjusted and optimized according to the data, and the bird repelling efficiency is greatly improved.

The invention provides an intelligent identification and real-time alarm system for video monitoring. The intelligent identification and real-time alarm system comprises a video sensor, a real-time preprocessing module, a deep learning module, a risk assessment module and an early warning module, the video sensor collects video information in an environment and sends the video information to the real-time preprocessing module, and the video sensor adopts one of a network camera IPC, an analog camera AVC and an NVR video processor; the real-time preprocessing module preprocesses the video data, the preprocessing comprises clipping, size adjustment, enhancement and denoising, standard video image formation, resolution and frame rate unification and video noise reduction, and the standard video image obtained after preprocessing is sent to the deep learning module; and the deep learning module performs target identification, target detection and abnormal behavior detection on the standard video image through a deep learning model, and analyzes the video according to the output detection target and the detection abnormal result.

The invention relates to the technical field of video processing, and discloses a panoramic monitoring camera video processing method and device based on cloud storage, and the method comprises the steps: carrying out the coding compression and layering processing of original video data collected by a panoramic monitoring camera, obtaining an initial data set, and executing the calibration frame parameter optimization calculation, camera calibration parameters are obtained; generating hierarchically stored processing data according to the initial data set and the camera calibration parameters; performing multiband fusion and triangulation calculation on the calibrated panoramic video data to obtain a three-dimensional scene digital model; analyzing the hierarchically stored processing data and the three-dimensional scene digital model to obtain a monitoring object behavior prediction result; according to the method, differential modeling and behavior prediction of panoramic monitoring on different areas are realized, smooth user experience is provided, and video access delay and operation complexity are reduced.

A method and apparatus comprising computer code for video processing, the method including receiving a text prompt from a user device, the text prompt being used for instructing an artificial intelligence (AI) generative process to generate one or more images; encoding a video in a bitstream, the video comprising the one or more images generated using the AI generative process and the text prompt; signaling the text prompt in the bitstream in a supplemental enhancement information (SEI) message; and signaling the encoded video comprising the one or more images.

The invention discloses a traffic monitoring video rapid target extraction method for edge equipment, and relates to the technical field of intelligent traffic video processing and edge calculation target detection, and the method comprises the steps: carrying out the adaptive downsampling processing of original video frame data, and generating downsampling video frame data; extracting a foreground target candidate region, and constructing a traffic region-of-interest mask in combination with a lane line detection result; carrying out pixel AND operation on the traffic region-of-interest mask and the foreground target candidate region to generate accurate candidate target region data, and extracting a target feature vector; carrying out weighted fusion on the target feature vector through a lightweight attention mechanism, generating a fusion feature descriptor, and calculating a target confidence score; and carrying out screening and duplicate removal processing on the accurate candidate target area data, and outputting traffic target extraction result data. According to the invention, the target in the traffic video can be rapidly and accurately extracted and processed in a low-delay manner on the edge equipment with limited computing resources.