862 results about "Reference frame" patented technology

A method of programming an industrial robot (100), with a robot manipulator (110) and robot controller (120), comprises: recording movements of the robot manipulator during a demonstration-based robot programming session, for thereby obtaining a robot trajectory; acquiring a video of the robot programming session; and generating, on the basis of the robot trajectory and the video, a robot program (C) executable by the robot controller, wherein the robot program includes at least one motion command which is expressed in a first reference frame (0 1). The method further comprises capturing operator input data indicating a first object (151) in an image in the video. The generated robot program includes a command to identify a position of an object resembling the first object in a work area (150) of a robot which executes the robot program; and the first reference frame is defined with respect to the identified position of the object resembling the first object.

Methods and systems implement a pleno-generation face video compression framework with bandwidth intelligence for generative models and compression. Heterogeneous-granularity facial description regularizes long-term dependencies between video frames and compensates for motion estimation errors caused by compact representations of motion information. A generative decoder reconstructs heterogeneous-granularity visual representations, providing auxiliary visual signals for attention-based recalibration of a GFVC-reconstructed face signal. A coarse-to-fine generation strategy avoids error accumulation. High efficiency for heterogeneous-granularity signal compression is achieved by two different entropy-based signal compression methods: heterogeneous-granularities feature representation from the key-reference frame as hyperpriors to optimize the entropy model for compressing heterogeneous-granularity feature from subsequent inter frames, and a feature difference operation for heterogeneous-granularities feature representation between key-reference and subsequent inter frames, such that the entropy model only compresses heterogeneous-granularities feature residual for redundancy reduction. Mixed-model dataset generation and training and model-specific dataset generation and training are also provided.

The invention discloses an ERP (Enterprise Resource Planning) data intelligent supervision method based on the Internet of Things, which relates to the technical field of enterprise informatization, and comprises the following steps: constructing a time sequence integrity observation layer in an Internet of Things data acquisition link, embedding a mirror image time anchor in each sensor sampling point, recording electromagnetic interference, time service offset and queuing delay, generating a phase fingerprint time baseline, and obtaining a phase fingerprint time baseline; constructing a unified time reference framework; operating a causal coherent analysis mechanism based on a unified time reference frame, comparing instantaneous offsets of a mirror time anchor and a real-time timestamp, identifying a phase inversion region, extracting information of a corresponding sampling node, a production line position and an energy consumption unit, generating a high-risk time slice list, and determining a time correction boundary. According to the method, a time sequence observation layer and a time reference frame are constructed, phase abnormity is identified, a data trend is reconstructed, a regulation and control instruction draft is generated, sampling time sequence recovery and scheduling convergence are realized through time inversion control, data consistency and decision accuracy are improved, and a closed-loop control system is constructed.

The invention provides a method, a device, equipment and a medium for generating an engineering project management digital model, and the method comprises the steps: collecting original data related to engineering project features, carrying out the preprocessing, forming a standardized project data set, constructing a space-time reference frame based on the data set, fusing a multi-dimensional feature tensor of multiple engineering features, and carrying out the construction of a space-time reference frame. The method comprises the steps of generating a project progress node and an integrated feature tensor, fusing the project progress node and the integrated feature tensor to generate a multi-source association graph containing entity relationships and feature semantics, generating a verified engineering project management digital model according to the project progress node, the integrated feature tensor and the association graph, and dynamically updating and optimizing the model in response to change data in project execution. According to the method, the problems of data dispersion, lack of association and static stiffness of the model in a traditional method are solved, automatic construction and dynamic evolution from multi-source heterogeneous data to an intelligent decision-making model are realized, and the accuracy, real-time performance and intelligent level of project management are remarkably improved.

The invention belongs to the technical field of image processing, and particularly relates to a contact network defect identification method based on multiple modalities, which comprises the following steps of: 1, acquiring a visible light image sequence and an infrared thermal imaging image sequence of a target contact network, and carrying out time alignment and temperature calibration to obtain a unified reference frame sequence and a thermal vision consistency label set; 2, constructing a structure change graph and an overheat candidate graph in the obtained unified reference frame sequence, calling a cross-modal physical semantic autocatalysis recombination algorithm, and taking a generated thermal vision consistency label set as a constraint; and step 3, taking the output fusion evidence body as input, combining the thermal vision consistency label set and the reversible channel audit record to verify the candidate area, obtaining a defect target, and outputting a defect category and a defect position. According to the method, the precision and stability of defect detection are improved, the traceability of the result is also realized, and a reliable guarantee is provided for intelligent operation and maintenance of the electrified railway overhead line system.

The invention relates to an underwater single target tracking method based on a wavelet token and a space-time Transform. The method comprises the following steps: firstly, constructing a reference frame sequence, a search frame and a previous frame historical token into a space-time input sequence, and extracting cross-frame features through a Transform encoder; then, Haar wavelet decomposition is carried out on the historical token, and a low-frequency component representing a target structure and a high-frequency component capturing motion details are separated out; then, adaptively fusing the global features and the historical components of the current search frame by using a gating mechanism, and generating a wavelet token; and finally, inputting the wavelet token and the global feature into a prediction head, and outputting a target classification confidence map and a bounding box regression map to determine the position and the scale of the target. According to the technical scheme of the invention, the interference of underwater low-illumination noise can be effectively suppressed through the wavelet token, and the space-time continuity of target motion modeling is maintained in combination with a gating strategy, so that the tracking robustness of an underwater complex scene is effectively improved.

Encoding using adaptive transform type sets based on frame level statistics includes obtaining an encoded bitstream by encoding a current block of a current frame of a current sequence of frames of an input video stream using adaptive transform type sets based on frame level statistics and outputting the encoded bitstream. Encoding the current block includes obtaining transform type statistics for previously reconstructed reference frames from the current sequence of frames, the previously reconstructed reference frames including at least one previously reconstructed reference frame, determining, in accordance with the transform type statistics, a current subset of transform types from a set of available transform types, generating encoded block data for the current block using a current transform type from the current subset of transform types, and including the encoded block data in the encoded bitstream.

The invention provides a video enhancement method, related equipment and a computer program product, and relates to the technical field of image processing. The method comprises the following steps: acquiring a target frame and at least one reference frame of the target frame from a video to be enhanced; performing feature extraction on the target frame and the at least one reference frame through a feature extraction module to obtain a target frame feature corresponding to the target frame and a reference frame feature corresponding to each reference frame; performing multi-frame alignment processing on the target frame feature and each reference frame feature through a multi-frame alignment module to obtain a multi-frame alignment feature; performing image enhancement processing on the multi-frame alignment features through an image enhancement module to obtain image enhancement features; and adding the image enhancement feature and the target frame pixel by pixel to obtain an enhanced target frame. According to the embodiment of the invention, the video can be enhanced accurately and efficiently.

The invention relates to the field of digital image processing, in particular to a super-resolution imaging method based on focal plane splicing and adaptive fusion. According to the method, sub-pixel offset among nine CCDs is preset through hardware, and nine frames of low-resolution image sequences with accurate displacement are obtained in push-broom. A central image is taken as a reference frame, high-precision mapping is realized based on hardware offset, motion estimation errors are avoided, effective pixels are screened by calculating robustness weight, an anisotropic Gaussian kernel function with a self-adaptive local structure is constructed so as to maintain image edge and detail features, and each frame is accumulated to a high-resolution grid in a weighting mode, so that a high-resolution image is obtained. And a sample compensation mechanism based on cumulative robustness is introduced, a fusion strategy is adaptively adjusted in an information insufficient area, and finally a high-resolution image is generated through normalization. The method significantly improves the imaging quality, suppresses artifacts and noise, and is suitable for the field of satellite remote sensing.

The invention relates to a video coding method and device, a video decoding method and device, electronic equipment and a storage medium. The video coding method comprises the following steps: acquiring difference information between a plurality of reference frames of a current frame and the current frame; determining weights corresponding to the plurality of reference frames based on the difference information; fusing the coding information of each reference frame in the plurality of reference frames based on the weights corresponding to the plurality of reference frames to obtain fused coding information; and coding the current frame based on the fused coding information to obtain a coding frame of the current frame.

A G-PCC coder may determine an occupancy of a reference child node in a reference node, wherein the reference node is in a reference frame of point cloud data used for inter prediction of a current node in a current frame of the point cloud data. The G-PCC coder may further determine a context for decoding a current occupancy bit of a current child node of the current node based on the occupancy of the reference child node, and arithmetic decode the current occupancy bit using the context.

The invention relates to the technical field of video compression, in particular to a narrow-bandwidth-based video monitoring data transmission and storage method, which comprises the following steps of: for an input video monitoring data frame, calculating a difference absolute value sum between a current frame and a reference frame; according to the method, the pixel difference of the current frame and the reference frame is calculated, and the average amplitude of the motion vector field is fused to form a comprehensive index of the dynamic degree of the quantized content, so that the image group structure is not fixed or periodic any more, and the dynamic degree of the quantized content can be calculated according to the score of scene activity and the change intensity. When a monitoring picture is static, a super-long image group is established to limit a compression code rate, when the picture is suddenly changed, the super-long image group is quickly switched to a short image group to ensure instant refreshing and definition of key information, meanwhile, non-uniform redistribution is performed on limited total code rate budget, bit resources can be intelligently inclined to frames with violent content change and large information amount, and the real-time refreshing and definition of the key information are ensured. Therefore, the subjective visual quality of the key dynamic moments is improved under the narrow bandwidth.

The invention discloses an adaptive video restoration method based on a digital video technology, and relates to the technical field of image communication. The method comprises the following steps: preprocessing an original video containing hard subtitles and automatically removing the hard subtitles to obtain a video to be repaired; screening a reference frame and a to-be-repaired frame by comparing the original video with the to-be-repaired video; determining a to-be-repaired area according to the pixel gray scale difference; extracting matched feature points, calculating a reference motion vector, estimating possible positions and motion differences of points in the to-be-repaired region in combination with object structure segmentation and region prediction, and generating a corrected motion vector; performing sub-pixel-level interpolation by using the vector, and constructing a corrected reference image; and finally, fusing the corrected image and the to-be-repaired area to generate a repaired video frame. The method can effectively improve the restoration quality of the subtitle shielded area.

The embodiments of the present disclosure provide a video encoding method and apparatus, and a device and a storage medium. The video encoding method comprises: in response to current network latency being less than or equal to preset latency, on the basis of a current reference frame queue and frame reception acknowledgment information that is fed back by a receiving end, determining a received reference frame in the current reference frame queue; determining the current encoding complexity between a current video frame to be encoded and the received reference frame; on the basis of the current encoding complexity and a target complexity threshold, determining a target encoded frame type corresponding to the current video frame; and on the basis of the target encoded frame type, performing encoding processing on the current video frame, and on the basis of the current video frame, updating the current reference frame queue. The technical solution in the embodiments of the present disclosure enables dynamic determination of an encoded frame type, so as to realize dynamic video encoding, thereby reducing end-to-end latency and stuttering, and also improving the video clarity.

The invention relates to the technical field of video super-resolution reconstruction, in particular to an unmanned aerial vehicle video super-resolution reconstruction method and system based on an implicit diffusion model, and the method comprises the steps: encoding a low-resolution frame into a low-dimensional latent feature through an implicit neural expression auto-encoder; denoising the latent features of the reference frame through an implicit neural U-shaped diffusion network to obtain clean latent features, generating a high-resolution reference image through an implicit neural decoder, and performing optical flow alignment to obtain a time consistency control signal; and finally, inputting the current frame latent features and the control signal into an implicit neural U-shaped diffusion network with a historical sampling correction module, and outputting a super-resolution image of any scale by an implicit neural decoder after iterative denoising. According to the method, in the unmanned aerial vehicle video super-resolution reconstruction task, any-scale reconstruction is realized, the problems of space-time inconsistency and sampling error accumulation are effectively relieved, and the method has better performance in the aspects of perception quality, time sequence consistency and the like.

A method, an apparatus, and a non-transitory computer-readable storage medium for video compression using a generative adversarial network (GAN) are provided. The method includes obtaining, by a generator of the GAN, a reconstructed target frame based on a reference frame and a raw target frame to be reconstructed; concatenating, by a transformer-based discriminator of the GAN, the reference frame, the raw target frame and the reconstructed target frame to obtain a paired data; determining, by the transformer-based discriminator of the GAN, whether the paired data is real or fake to guide reconstruction of the raw target frame; and determining a generator loss and a transformer-based discriminator loss, and performing gradient back propagation and updating network parameters of the GAN based on the generator loss and the transformer-based discriminator loss.

Disclosed are an inter-frame prediction method, device and system, relating to the field of video image coding and decoding, the method comprising: obtaining a mapping relationship between a current frame and a reference frame of the current frame, and determining initial motion information according to a current image block in the current frame and the mapping relationship; the initial motion information is used for indicating motion information between the current image block and a mapping image block having a mapping relation with the current image block in the reference frame, then determining target motion information of the current image block according to the initial motion information, and then determining predicted pixels of the current image block according to the target motion information. Therefore, the mode of determining the target motion information is higher in efficiency and lower in complexity, the obtained motion information is more accurate, the prediction accuracy is higher, the coding / decoding efficiency is higher, and the coding data volume and the transmission data volume are smaller.

The invention belongs to the technical field of computer vision and target detection, and discloses an anti-unmanned aerial vehicle low-altitude small target detection method based on RTDETR, and the method comprises the steps: extracting the features of an input image through employing a PVM-based backbone network, and obtaining the multi-scale features; processing the highest level features in the multi-scale features by using a self-attention-based space gating unit in the hybrid encoder to obtain gating features; other features except the highest-level feature in the multi-scale features are taken and combined with the gating features to be input into an attention fusion module based on content guidance, and fusion features are output; extracting a candidate frame with the minimum uncertainty of the fusion features as an initial reference frame, and taking a corresponding feature vector as an initial object for query; and taking the initial object query and the initial reference frame as a decoder based on the Transform, and enabling the output of the decoder based on the Transform to pass through a prediction head to obtain a final target detection result. According to the method, the small target detection performance can be remarkably improved while the real-time performance is kept.

The invention provides a long-time reference frame selection and updating method, device and equipment based on rendering scene state driving and a storage medium. The implementation method comprises the following steps: acquiring rendering scene state information from a game rendering engine; inputting the rendering scene state information into a rendering scene information processing module; the rendering scene information processing module generates a long-time reference frame control command matched with the format of the video encoder; and the video encoder executes the long-time reference frame control command for encoding. Through a long-time reference frame selection mechanism based on semantic driving, an encoder can receive a long-time reference frame decision command made based on game engine scene state information in real time, long-time reference frame judgment based on semantics is achieved, long-time reference frame judgment of the encoder based on image pixels is avoided, and the accuracy of the long-time reference frame judgment is improved. And the long-time reference frame selection speed and accuracy are improved. When it is detected that the scene is unloaded by the game engine, the system can automatically clear the corresponding LTR reference frame set, and wrong reference to old frames is avoided.

The invention relates to the technical field of monitoring video processing. The invention provides a monitoring playback picture processing method and device based on a conditional generative adversarial network. The method comprises the following steps: based on a monitoring service scene, acquiring degraded video frame samples under multiple degradation conditions and reference frame samples corresponding to the service scene, and establishing a training data set with degradation types and clear pairing; constructing a video processing model based on a conditional generative adversarial network, and training the video processing model by using the constructed training data set to obtain a trained video processing model; inputting a to-be-processed video into the trained video processing model to obtain enhanced and optimized video data; and executing index query through a retrieval mechanism to obtain a playback video corresponding to the user request. According to the invention, the quality of the monitoring playback picture is effectively improved.

The invention discloses an edge flame and smoke intelligent identification method based on deep learning, and the method comprises the steps: collecting continuous video frames, carrying out the normalization, correction and noise reduction, and generating a preprocessing video sequence; constructing a background static reference frame, and carrying out pixel difference on the background static reference frame and the current frame to generate geometric refraction potential field codes; performing refraction phase mapping on the geometric refraction potential field code to form a refraction phase disturbance tensor; inputting an improved SlowFast model, dynamically adjusting three-branch sampling, and outputting a preliminary candidate region; extracting refraction, phase and energy evolution sequences, constructing a coupling sequence and correcting an identification result; and calculating a risk level, marking a high-risk area, and outputting fire early warning at edge equipment. According to the invention, through constructing the refraction potential field features and the phase disturbance features and combining the improved multi-branch SlowFast deep learning model, rapid, accurate and stable edge side intelligent identification and early warning of flames and smog are realized.

The invention provides a video reference frame management method applying a real-time scene. The method comprises the following steps: updating a reference frame queue: (1) inputting an image Fn to be compressed; (2) generating a reconstructed image F'n from the to-be-compressed image Fn through a coding inverse process; (3) sending the reconstructed image F'n into an image reference frame queue and a traditional reference frame queue at the same time for respective update processing; comprising the following steps of: (1) inputting an image Fn to be compressed; (2) generating a scene ID of the image; (3) comparing the scene ID of the generated image with the scene ID of the scene reference frame queue; (4) judging whether the scene ID of the to-be-compressed image Fn is the same as the scene ID in the scene reference frame queue or not; (5) if yes, taking the scene reference frame images with the same scene ID as reference frames of the to-be-compressed image; and (6) if not, obtaining the reference frame from the traditional reference frame queue. According to the method, the video compression efficiency can be remarkably improved.

The embodiment of the invention relates to an image processing method and device and a medium. The method proposed herein comprises: determining scene description information based on at least one constructed reference frame, the scene description information indicating a complexity corresponding to the at least one reference frame, a motion complexity indicating motion vector information associated with the at least one reference frame, and a picture complexity indicating at least an object distribution in the at least one reference frame; based on the scene description information and the use state of the image processing resources, at least one control parameter is determined, the at least one control parameter comprises at least one of the following items: the resolution of the motion vector and the precision of the depth information, and the precision indicates the bit width of the depth information; determining at least one frame insertion parameter based on the at least one control parameter; and performing interpolation processing based on a target reference frame in the at least one reference frame and the at least one frame interpolation parameter to generate an intermediate frame. In this way, according to the embodiment of the invention, the efficiency of generating the intermediate frame can be effectively improved.

The invention discloses a video subtitle erasing method, device and equipment and a storage medium, and relates to the field of digital image processing, and the method comprises the steps: detecting subtitles of a subtitle video to be erased, merging timestamps of the same subtitles in the subtitle video to be erased, and determining a subtitle fragment set and a subtitle-free fragment set; splitting the subtitle segment into independent shot segments by using a preset lens splitting algorithm, and analyzing video frames of the independent shot segments to obtain a first frame and a tail frame; determining a target reference frame based on the first frame and the tail frame, generating an expanded independent shot segment according to the target reference frame and the independent shot segment, and segmenting a target character mask; and generating an erased independent lens segment according to the expanded independent lens segment and the target character mask through a preset erasure algorithm, performing a preset post-processing optimization operation on the erased independent lens segment to obtain a target independent lens segment, and integrating the target independent lens segment and the subtitle-free segment set to generate a target video. According to the method and the device, the video subtitles can be accurately erased.

The invention discloses a method for improving the performance of double-independent quantum key distribution of actual reference system measurement equipment, belongs to the technical field of quantum key distribution (QKD), and aims to improve the performance of the double-independent quantum key distribution when non-ideal factors such as a post-pulse effect of a detector and finite sample statistical fluctuation are considered. The invention relates to a method and a system for improving the performance of a reference system measurement device by introducing an advantage extraction technology into a double independent quantum key distribution (RFI-MDI-QKD) protocol of the reference system measurement device. Simulation results show that when the post-pulse effect and the statistical fluctuation effect are considered, the advantage extraction method can effectively improve the security key rate and the security transmission distance of the RFI-MDI-QKD protocol on the premise of not changing optical hardware. According to the invention, a valuable reference technology can be provided for practical research of the RFI-MDI-QKD system.

The invention discloses a two-stage cascaded video focus detection method and system, and relates to the technical field of image target recognition, and the method comprises the steps: obtaining an endoscope image; selecting a reference frame; carrying out iterative calculation on the feature embedding of the reference frame to obtain deep space-time representation; generating a time attention weight based on the deep spatio-temporal representation, and performing weighted fusion on the deep spatio-temporal representation to obtain an enhanced feature; performing dimension reduction on the enhanced features to obtain video prompt information; and extracting feature embedding of the target reasoning frame and reasoning frame features in the video prompt information, performing iterative calculation on the reasoning frame features to obtain deep reasoning features, and performing focus detection on the deep reasoning features to obtain a detection result. According to the method, a two-stage cascade Transform architecture is adopted, the quality degradation phenomena of dynamic blur, exposure imbalance, reflection artifacts and the like of inference frames are effectively relieved, and efficient joint modeling of spatial-temporal characteristics is achieved.

The invention relates to the technical field of video compression, in particular to an AI-based video compression platform, which comprises a key target detection module, an image region division module, a coding level setting module, a parameter adjustment and analysis module and a coding result integration module. According to the method, key region extraction is completed by collecting pixel color combination and contour change information in a video frame image, background and non-background region boundaries are delimited, a region attribute labeling result is constructed, a region distribution classification relation mapping coding level is established, and reference frame and prediction interval configuration is extracted. Real-time adjustment of compression levels is realized by performing normalized combination analysis on textures and motion change trends of different regions, and region fragments under different compression levels are recombined and subjected to unified code stream processing, so that coding resources can be dynamically allocated on the basis of accurately identifying contents in a compression process; the problems of inaccurate target positioning, fixed compression configuration, delayed content response and the like in an existing compression system are effectively solved.

The embodiment of the invention relates to an image processing method and device and a medium. The method proposed herein includes: generating, by a graphics processing unit, a first reference frame, a second reference frame, and motion vector information corresponding to the first reference frame; transmitting the motion vector information to a digital signal processor by a graphic processing unit; the digital signal processor divides the motion vector information into a plurality of groups; processing the plurality of groups in parallel by using a plurality of processing cores of the digital signal processor, each processing core being configured to: write a group of motion vectors indicated by a corresponding group to a group of projection positions of the group of motion vectors in the interpolated frame by using a scatter instruction; respectively reading source pixel information from the first reference frame and the second reference frame based on the written group of motion vectors by utilizing a collection instruction; the source pixel information is fused by a digital signal processor to generate an interpolated frame. In this way, according to the embodiment of the invention, the generation efficiency of the interpolation frame can be effectively improved.

Disclosed are an inter-frame prediction method, apparatus and system, relating to the field of video / image encoding and decoding. The method comprises: acquiring a mapping relationship between a current frame and a reference frame of the current frame; determining initial motion information on the basis of a current image block in the current frame and the mapping relationship, the initial motion information being used for indicating motion information between the current image block and a mapped image block, in the reference frame, having a mapping relationship with the current image block; further determining target motion information of the current image block on the basis of the initial motion information; and then determining predicted pixels of the current image block on the basis of the target motion information. The method provides a more efficient and less complex process to determine the target motion information, resulting in higher accuracy of the obtained motion information, and therefore, the method has higher prediction accuracy and higher encoding / decoding efficiency, and achieves smaller data volumes for both encoding and transmission.

The invention discloses a video code rate dynamic allocation compression method based on content complexity prediction, which comprises the following steps of: S1, acquiring a video frame sequence, and preprocessing; s2, inputting the frame-level feature tensor into a gated residual convolutional network, and outputting a complexity prediction sequence; s3, constructing a frame priority queue, and calculating the complexity jump amplitude between adjacent frames; s4, a compression area is divided, and a corresponding code rate resource scale factor is allocated; s5, configuring a reference frame structure, a prediction interval and an initial quantization step size for each compression region, and calculating a region target bit number; s6, distributing regional bits to each frame in a compression coding process, and dynamically adjusting a frame-level quantization parameter and an entropy coding strategy; and S7, after compression is completed, reversely updating convolution prediction network parameters through bit distribution errors. According to the invention, fine code rate dynamic allocation and adaptive compression control based on content complexity are realized, and the video compression quality and bit utilization efficiency are effectively improved.