4241 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.

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.

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 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 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 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 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 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 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.

The invention discloses an artificial intelligence video processing method based on digital twinning, and relates to the technical field of intelligent video analysis. According to the method, the problems of time inconsistency and frame missing of the multi-source video data are solved through a timestamp alignment mechanism and a time interpolation method, and the integrity and synchronism of the data are ensured; a multi-source integration technology and a three-dimensional convolutional network are used for fusing multi-view video data, a high-precision virtual model is constructed, dynamic scene mapping is optimized through real-time parameter updating and Kalman filtering, and the precision and the real-time performance of the model are improved; furthermore, by means of a long-short term memory network and an attention mechanism, a target track is accurately predicted, behavior pattern classification is achieved, reliable support is provided for intelligent decision making in a complex dynamic environment, the efficiency and accuracy of multi-source video data processing are effectively improved, and the adaptability and the intelligent level of the system in a dynamic scene are enhanced.

The invention discloses a video training data generation method based on multi-modal semantic alignment, and relates to the technical field of audio and video processing. The method specifically comprises the following steps: (1) carrying out multi-modal time alignment on audio, image frames and text information in a video, and establishing a cross-modal time sequence mapping relation; (2) semantic enhancement processing is carried out based on the time alignment result, and the identification accuracy of the terminology is improved; (3) dynamically grading the training samples according to the semantic density and the confidence coefficient; and (4) outputting the graded structured training data to adapt to different training stages. The method aims at improving the quality of training data from a video data source and avoiding occurrence of a large amount of redundant data and missing of key nodes.

The invention relates to the field of video processing, in particular to a video data processing method and device, equipment and a medium. In a background replacement link, based on precise operation of video processing requirements, an adaptive algorithm can be selected according to scene characteristics, and errors are preliminarily reduced. And subsequently, error compensation is carried out on the generated intermediate video data, the error region is corrected in a targeted manner, the edge is filled and optimized by utilizing the edge pixel characteristics of the foreground region, and iteration processing is carried out until the error is lower than a preset value, so that the accuracy of background replacement is greatly improved. And finally, the target video data is injected into the virtual camera of the cloud mobile phone, the method is applied to a mobile terminal scene, the background and the foreground are naturally fused under high-precision scenes such as live broadcast and virtual conferences, the image flaws are remarkably reduced, the strict requirements of a user on the video image quality are met with a high-precision background replacement effect, and the overall user experience is improved.

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.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: selecting, for a conversion between a current video block of a video and a bitstream of the video, a target blending scheme from a plurality of candidate blending schemes for blending samples in a plurality of partitions associated with the current video block; and performing the conversion based on the target blending scheme.

The invention relates to the technical field of audio and video processing, and provides an audio and video object intelligent tracking optimization method and system combined with deep learning. The method comprises the following steps: performing cross-modal feature collaborative extraction on an audio stream and a video frame sequence by acquiring a synchronous audio and video data group, and generating a multi-modal feature set containing audio time domain dynamic features and video space structure features; inputting the multi-modal feature set into a pre-trained association enhancement network to generate a cross-modal semantic aligned association feature sequence; constructing a tracking stability evaluation model based on the associated feature sequence, and outputting a stability index; tracking parameters are dynamically adjusted according to the stability index, an initial tracking result is calibrated, and an optimized tracking trajectory is output. Therefore, the precision and stability of object tracking in a complex scene are improved by deeply fusing the dual-mode characteristics of the audio and the video, mining the internal association between the modes and combining a dynamic evaluation and calibration mechanism.

The invention discloses a real-time video processing system based on an ampere-path FPGA. The real-time video processing system comprises an SD card, an ampere-path FPGA board card and an HDMI displayer. The SD card is used for storing original video data in a Bayer format; the A-path FPGA board card comprises an FPGA end and an ARM end, the FPGA end comprises an SD card reading control module, an image cache FIFO module, a Bayer-to-RGB module, a seven-in-one image processing module, an SDRAM read-write control module and an HDMI display control module, the first four modules are connected in sequence, and the SDRAM read-write control module is connected with the Bayer-to-RGB module, the seven-in-one image processing module and the HDMI display control module; wherein the ARM end comprises a Cortex-M0 processor core, an AHBLiite-Interconnect data bus and all peripheral interfaces, and all the modules are connected in sequence; the ARM end comprises a Cortex-M0 processor core, an AHBLiite-Interconnect data bus and all the peripheral interfaces; the ampere-path FPGA board card is used for completing all video processing functions; and the HDMI display outputs the processed video. The method is good in video processing effect, low in resource consumption, high in flexibility, high in stability and low in hardware cost, and the hardware is 100% localized.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. In the method, for a conversion between a current video block of a video and a bitstream of the video, a process is applied to the current video block based on template matching. At least one reference sample of a current template of the current video block is determined based on a block vector (BV) of the current video block during the process. The conversion is performed based on the applying.

The invention relates to a multi-mode real-time compliance auditing method and system for an investment adviser live broadcast industry. According to the system, audio and video frames and texts in a live broadcast stream are analyzed in real time by fusing artificial intelligence technologies such as audio and video processing, voice recognition, natural language processing and image recognition, and potential illegal contents are recognized. The system adopts a sliding window audio slicing mechanism, so that the accuracy of speech recognition and the continuity of semantic analysis are improved; hidden violation contents can be recognized by combining a semantic recognition technology of BERT model fine tuning. In addition, the system supports automatic marking and backtracking of violation contents, and meets the compliance mark leaving requirements in the financial field. According to the method, the compliance auditing efficiency and accuracy of the live broadcast of the investment adviser are remarkably improved, the deployment cost is reduced, the method is suitable for investment adviser scenes such as securities, funds and insurance, and powerful support is provided for real-time risk control and compliance management.

The invention provides a video dubbing method and device, electronic equipment and a storage medium, and belongs to the technical field of video processing, and the method comprises the steps: separating a first audio in a to-be-dubbed video, and carrying out the sentence-by-sentence text conversion, and obtaining a first subtitle text with a time code; translating the first subtitle text into a second subtitle text with the same time code; normalizing the second subtitle text to obtain a third subtitle text according to the difference between the dubbing duration of the second subtitle text and the subtitle display duration; generating a third audio corresponding to the third subtitle text; and finally, synthesizing all the third audios, the background audio of the video to be dubbed and the silent video to obtain a final target video. According to the invention, under the condition that the audio duration and the subtitle display duration are different in the video dubbing process, the subtitle text is normalized, so that the damage to the original video file is avoided, and the high quality of video dubbing is ensured.

The invention discloses a large-scale video access and AI reasoning enhancement system and method, belongs to the technical field of video processing, and aims to solve the technical problem of how to realize large-scale video stream efficient access, resource elastic scheduling and abnormity self-healing in a complex environment. Comprising a resource dynamic scheduling module, a decoding and reasoning control module, a resolution dynamic processing module, a multi-stage shared memory transmission module, an exception self-healing and fault-tolerant module and a task parallel execution module, and resource allocation is optimized and calculated by dynamically binding a CPU core and GPU hardware unit isolation; the resolution is dynamically adjusted based on the scene algorithm precision requirement, and invalid calculation is reduced; a stream pushing and frame rate decoding strategy is controlled by using a Redis mark, and memory occupation is reduced by combining long and short queues; transmission coding and decoding are reduced by adopting a shared memory, and the cross-process interaction efficiency is improved; and task self-healing is realized through dual anomaly detection and process-level heartbeat monitoring.

The invention relates to the technical field of video processing, and discloses a video generation and screening method and device, equipment and a medium, and the method comprises the steps: obtaining text description information and reference image information, carrying out the semantic analysis of the text description information through a large model, and obtaining a multi-modal semantic representation; generating a cue word set according to the multi-modal semantic representation, and modeling the cue word set and the reference image information to obtain image sequence information; carrying out model matching on the image sequence information by utilizing a large model, generating an optimized cue word, and generating a video material based on the optimized cue word through a video generation model; and determining motion smoothness and picture consistency between adjacent frames in the video materials, and determining the video materials of which the motion smoothness is higher than a first preset threshold value and the picture consistency is higher than a second preset threshold value as target videos. The video generation method and device can be applied to financial science and technology and medical care service program systems, and can improve the smoothness and consistency of the video while ensuring the video generation efficiency.

The invention belongs to the technical field of video material processing, and discloses a video material intelligent processing method and device based on dynamic semantic driving, and the method comprises the steps: carrying out the frame-by-frame analysis of an input video through a pre-trained semantic feature extraction model, and extracting a deep semantic vector containing an object attribute, a scene context and a multi-modal emotion feature; performing dynamic analysis on the deep semantic vector based on a time sequence attention mechanism, and generating a semantic state change map reflecting a video content evolution rule in real time; generating an adaptive processing strategy through a reinforcement learning strategy network based on a real-time matching result of a personalized processing demand input by a user and the semantic state change graph; performing dynamic processing operation on the input video according to a self-adaptive processing strategy to generate a target video material; according to the invention, the problems of shallow semantic understanding and fixed processing mode in the prior art are effectively solved, the video processing effect and the user experience are improved, and the requirements of diversification, timeliness and individuation are met.

Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method includes: determining, for a conversion between a video unit of a video and a bitstream of the video, a neural-network post-filter (NNPF) is activated for a set of pictures; apply the NNPF to one or more pictures in the set of pictures according to an order; and performing the conversion based on the NNPF.

The invention relates to the technical field of data processing, and discloses a video understanding method and device based on dynamic sparseness, equipment and a medium, according to the scheme, spatiotemporal feature extraction and conversion are performed on a video frame sequence through a spatiotemporal feature encoder, spatiotemporal information of a video can be fully reserved, and video features with rich semantics can be output. A dynamic sparse attention mechanism is utilized to perform sparse attention calculation on video semantic features, and attention distribution is dynamically adjusted according to time-space characteristics of video contents, so that important context information in a video is accurately captured, redundant calculation is reduced, calculation complexity during video processing is effectively reduced, and video understanding efficiency is improved. The context feature vectors are analyzed and calculated through the text generation encoder, efficient and accurate video semantic understanding and text description generation are achieved, and therefore the video understanding efficiency in the application scene of processing mass transaction data in the financial field and processing high-resolution medical images in the medical field is improved.

The invention discloses a railway freight car part replacement operation video detection system, solves the problem of how to improve the railway freight car maintenance operation detection efficiency, and belongs to the technical field of railway freight car maintenance. The method comprises the following steps: a video processing layer processes a vehicle depot operation video stream collected in real time to obtain a dynamic frame image; the multi-target detection and tracking module detects the dynamic frame image to obtain a state label of the target, determines the first occurrence time and the final disappearance time of the target and the position coordinate of the target, and forms time-space sequence data; the action time sequence analysis module is used for analyzing the state label and the space-time sequence data of the target, filtering out abnormal action nodes and extracting action nodes and corresponding time of a state conversion scene; the component state judgment module verifies the final setting state and the operation process compliance of the component according to the state label and the space-time sequence data of the target; and the decision output layer generates a structured detection report according to the analysis result.

Devices, systems and methods for digital video coding, which includes matrix-based intra prediction methods for video coding, are described. In a representative aspect, a method for video processing includes performing a conversion between a current video block of a video and a bitstream representation of the current video block according to a rule, where the rule specifies a relationship between applicability of a matrix based intra prediction (MIP) mode or a transform mode during the conversion, where the MIP mode includes determining a prediction block of the current video block by performing, on previously coded samples of the video, a boundary downsampling operation, followed by a matrix vector multiplication operation, and selectively followed by an upsampling operation, and where the transform mode specifies use of a transform operation for the determining the prediction block for the current video block.

The invention provides a high-density video processing and computing resource dynamic allocation method, which comprises the following steps of: acquiring video stream data, monitoring load indexes of computing units, combining states of the computing units into a matrix, obtaining a historical state sequence, inputting the historical state sequence into a long short-term memory network to predict future load states of the computing units, obtaining a load prediction result, generating a candidate migration path, calculating the migration cost of a single path, screening an optimal path for channel migration, updating a channel binding relationship, and transmitting a video stream to a video processing task queue according to the binding relationship; calling a hardware decoder cluster to decode a video stream, running a target detection model and a target tracking model in parallel by a heterogeneous array, performing target detection on decoded frame buffer, superposing a tracking algorithm on a detection result to obtain an analysis result, dynamically selecting a coding mode according to the analysis result, executing hardware coding, and packaging an output stream.

A video processing method includes: obtaining a plurality of image groups on the basis of a video frame sequence of an initial video; performing motion blur processing on the basis of each frame of image in a target image group, and fusing images which are obtained by performing motion blur processing on each frame of image, so as to obtain a motion-blurred image corresponding to the target image group; on the basis of a specified frame of image in the target image group, determining a main body object area and a background area, which correspond to the target image group; fusing the motion-blurred image with the specified frame of image according to the main body object area and the background area, so as to obtain a target fused image; and generating a target video on the basis of target fused images respectively corresponding to the plurality of image groups.