877 results about "Multiple frame" patented technology

The invention relates to an optical film detection method, device and equipment based on image processing. The method comprises the following steps: collecting multiple frames of images of a multilayer optical film under different illumination conditions, and carrying out preprocessing and characteristic value calculation on the multiple frames of images to obtain a characteristic value response matrix; calculating an optimal weight coefficient of each frame of image according to the feature value response matrix, and performing multi-frame image feature fusion to obtain integrated feature representation; performing multi-scale defect feature extraction on the integrated feature representation to obtain a defect candidate region; spectral response characteristics are extracted based on the defect candidate area, matching degree calculation is carried out on the spectral response characteristics and a preset wavelength-film thickness mapping relation matrix, and a multilayer film defect distribution diagram is obtained; and performing autoregression prediction and processing control parameter optimization on the multilayer film defect distribution diagram to obtain a closed-loop control scheme. According to the invention, accurate identification and classification of different film layer defects are realized, and closed-loop control of defect detection results and manufacturing parameter optimization is further realized.

An image enhancement method using multi-frame stacking comprises the steps of storing multiple frames of a target scene captured by a frame-based camera during an acquisition interval; storing a series of events produced during the acquisition interval by an event-based camera viewing the target scene; synchronizing timestamps of the events and timestamps of the frames; selecting an anchor frame among the multiple frames; aligning a current frame with respect to the anchor frame, based on stored events having timestamps corresponding to an interval between the anchor frame and the current frame; and merging the aligned frame and the anchor frame.

The invention provides an improved multi-frame image target detection network, namely, TFA-Net (Temporal Fusion Attention Network), which is oriented to a welding seam defect detection task. According to the network, continuous multiple frames of images are used as input, and spatial features of each frame of image are extracted through a ResNet backbone network sharing parameters. On the basis, a multi-stage feature fusion module is fused and introduced, effective integration among features of different scales is realized, and the perception capability for small-size and weak-contrast defects is improved. In order to further capture dynamic information of the target in the time dimension, a time sequence modeling module is designed, modeling is carried out on a multi-frame feature sequence, and continuous features of the target changing along with time are extracted. And then, the network adopts a gating fusion mechanism to carry out adaptive weighted fusion on the static space features and the dynamic time sequence features, and the robustness and the discrimination capability of feature representation are enhanced. Finally, the fusion features are input into a decoder module, the category probability of the defects is predicted through a classification sub-network, anchor frame position offset is calculated through a frame regression sub-network, and accurate positioning and recognition of the multiple types of defects in the weld seam image are achieved. The network has the advantages of clear structure, strong generalization ability, high adaptability and the like, and is especially suitable for the problems of small target size, unclear texture, strong motion continuity and the like in a welding seam detection scene.

The invention relates to a multi-moving-target tracking method for a remote sensing video and a storage medium. The method comprises the following steps: firstly, establishing a plurality of detection subsequences based on multiple frames of images of an acquired remote sensing video, then respectively extracting a space-time fusion feature containing a static feature and a dynamic feature of each detection subsequence, and obtaining a detection frame of each frame of image based on the space-time fusion feature; then, according to the target library information of each frame, prediction states of multiple frames of images are obtained through Kalman filtering in sequence, and an optical flow matrix of two adjacent frames of images is calculated based on background feature points of the two adjacent frames of images; based on the optical flow matrix, the prediction state of each frame of image can be corrected and matched with the detection frame of each frame of image, and a first target library sequence composed of the target libraries of each frame of image is obtained. And finally, performing post-processing operation of interpolation reconstruction and static target elimination on the first target library sequence based on the optical flow matrix to obtain a second target library sequence. Therefore, the multi-moving-target tracking precision of the remote sensing video can be improved.

The invention discloses a multi-frame photoacoustic image reconstruction method based on optical flow alignment and depth feature fusion. The method comprises the following steps: S1, obtaining photoacoustic signal data; s2, reconstructing a photoacoustic cross-sectional image; s3, performing optical flow calculation and image alignment; and S4, training the deep feature fusion network. According to the method, through optical flow motion correction and a potential space learning mechanism, space-time information and complementary features in the aligned multiple frames of images are dynamically integrated, complementary information in the multiple frames of images is adaptively fused, noise is suppressed, and finally reconstruction of high signal-to-noise ratio and high spatial resolution images of biological tissues is achieved. Experimental results show that the method can significantly improve image quality, recover image distortion and detail loss caused by motion and noise, and provide a new effective scheme for promoting robust clinical application of a photoacoustic imaging technology.

The invention relates to the technical field of intelligent retail terminals, solves the problem of low commodity replenishment accuracy in an unmanned vending cabinet in the prior art, and provides a retail terminal inventory replenishment method, device and system based on multi-modal data. The method comprises the following steps: decomposing real-time video data into multiple frames of real-time images according to the real-time video data of the unmanned vending cabinet in a commodity transaction scene; performing multi-modal feature extraction on the commodity region image to determine commodity feature information; performing human body feature extraction on the user area image in the historical commodity transaction scene, and updating user group feature information; inputting the user group feature information as incremental data into the commodity demand prediction initial model, and determining a commodity demand prediction target model; and inputting the commodity feature information into a commodity demand prediction target model, and screening out a target commodity from a preset commodity list according to a prediction result, and supplementing and putting the target commodity on a shelf. According to the invention, the replenishment accuracy of the unmanned vending cabinet can be improved.

The invention relates to an audio book video generation method and device, electronic equipment, a medium and a program product, and belongs to the technical field of computers. The audio book video generation method comprises the following steps: acquiring a multi-frame image corresponding to a story text, wherein the multi-frame image is used for representing a key plot of the story text; determining target audio data in the audio data corresponding to the story text; and generating an audio book video corresponding to the story text based on the target audio data and the multiple frames of images.

The invention discloses a wall surface water seepage automatic detection method based on a visible light camera. The method comprises the following steps: acquiring a wall surface image through an optical lens; performing frame cutting operation on the wall surface image to obtain an RGB image of each frame of image of the wall surface image; a classification model is trained to classify the RGB images, and wall images with the water seepage risk are screened out according to the classification result; acquiring continuous multi-frame images of the wall surface image with the water seepage risk to form a time sequence image; extracting a change region of the time sequence image through a dynamic target perception model; if the change area is greater than a preset threshold value, determining that the wall surface image with the water seepage risk is a water seepage wall surface image; and performing reclassification on the water seepage wall surface image through the water seepage semantic segmentation model, generating a segmentation mask of the high-confidence water seepage area, and outputting the water seepage wall surface image covered with the mask. The method has the advantages that the wall surface water seepage in indoor and outdoor environments can be quickly, accurately and automatically detected without auxiliary equipment.

The invention relates to a laser radar camera calibration method and device, and a medium. The method comprises the steps: collecting multi-frame laser radar point cloud data and synchronous corresponding image data in a construction scene; carrying out multi-frame point cloud fusion and dense reconstruction to generate a laser dense point cloud; visual sparse point cloud reconstruction is carried out, and cross-modal scale unification and space alignment are carried out on an initial visual point cloud obtained through reconstruction and the laser dense point cloud; generating a visual dense point cloud through three-dimensional Gaussian splashing; and performing registration on the laser dense point cloud and the visual dense point cloud by adopting a point-to-line iterative nearest point algorithm, and performing calculation to obtain an external parameter calibration matrix of the camera and the laser radar. Compared with the prior art, the method has the advantages of high precision, low cost, high stability and the like.

A no-reference video assessment framework employs a multi-resolution input representation and a patch sampling mechanism on a video having multiple frames 302 to aggregate information across different granularities in spatial and temporal dimensions. The framework effectively models complex spacetime distortions that occur in user generated content-type videos. According to one aspect, the framework embeds video clips 302 as multi-resolution patch tokens using complementary modules. This includes a multi-resolution video embedding module 322, and a space-time factorized Transformer encoding module 324, 326. The multi-resolution video embedding module 322 is configured to encode multi-scale quality information in the video, capturing both global video composition from lower resolution frame and local details from larger resolution frames. The space-time factorized Transformer encoding module 324, 326 aggregates the spatial and temporal quality from the multi-scale embedding input, and is configured to output a quality score 330 for the input video.

The invention discloses a recognition method, device and system for putting food materials in two hands and a storage medium, and belongs to the technical field of household appliances. The method comprises the following steps: acquiring multiple frames of first images; performing hand and food material detection and recognition on the multiple frames of first images to obtain hand and food material detection and recognition results corresponding to the first images in the multiple frames of first images; according to the hand and food material detection and recognition results corresponding to the first images in the multiple frames of first images, tracking the two-hand actions of the user, and determining whether the action of at least one hand in the two-hand actions of the user is food material putting or not; and under the condition that at least one of the actions of the two hands of the user is to put the food materials, determining a target shelf area in which the two hands put the food materials on the basis of a target image of a bounding box in which the hand and food material detection and recognition result in the multi-frame first image comprises the food material taking by the hand. By detecting the hands and the food materials and tracking the actions of the two hands of the user, accurate recognition of the food materials put into the two hands is achieved.

The invention relates to the technical field of medical image processing, in particular to an endoscopic surgery video real-time structure analysis method and system, and the method comprises the steps: carrying out the frame-by-frame semantic segmentation of real-time video data, and generating a segmentation mask of each frame of image; calculating a comprehensive quality score of the target frame based on the segmentation masks of the target frame and the previous frame of the target frame; analyzing a motion amount index between adjacent frames; generating the current length of a sliding time sequence window according to the comprehensive quality score and the exercise amount index, and fusing multiple frames of segmentation masks in the sliding time sequence window to generate a reference mask; extracting a key point from the reference mask, and obtaining a displacement vector of the key point from a previous frame of the target frame to the target frame; generating a pixel-level displacement field according to the displacement vector, deforming the segmentation mask of the previous frame of the target frame to the target frame, and generating a prediction mask of the target frame; and performing superposition display on the prediction mask and the image of the target frame. According to the scheme, the time sequence consistency and stability of the video semantic segmentation result can be enhanced.

The invention relates to a video generation method based on time sequence similarity, and is applied to the field of video generation. Specifically, the video generation method based on the time sequence similarity comprises the following steps: acquiring generated video information; generating a key video frame sequence with frame rate information based on the text information, wherein the key video sequence is separated by a plurality of frame marks; performing recursive interpolation processing on the key video frame sequence, fusing front and back key frame features through a preset attention mechanism, and generating an initial intermediate frame between adjacent key frames; extracting visual features of a plurality of key frames and intermediate frames included in the key video frame sequence, calculating cosine similarity of the visual features of adjacent frames, and adjusting intermediate frame generation parameters based on the similarity to optimize inter-frame coherence so as to obtain an optimized intermediate frame; and combining the key video frame sequence with the optimized middle frame to generate a complete video clip which is consistent with the text information and has a coherent inter-frame time sequence.

The invention provides a display method and device.According to the method, modal information of a target structure under a preset working condition is obtained, the modal information is used for representing deformation characteristics of the target structure under the preset working condition, and animation data of the target structure under the preset working condition are generated and displayed based on the modal information and target data; the animation data comprises multiple frames of images, each frame of image comprises a deformation state of a target structure at a corresponding moment, the target data comprises a predetermined change trend of a scaling factor and / or a specified scaling factor, and the predetermined change trend comprises a predetermined corresponding relationship between the scaling factor and the moment; the change rate of the scaling factor at each moment is less than or equal to a predetermined change rate, and the specified scaling factor is determined by responding to an input operation of the scaling factor of the specified component in the target structure. According to the display method, the modal information can be clearly and effectively displayed.

The invention discloses an ultrasonic image analysis method and an ultrasonic imaging system. The method comprises the following steps: acquiring multiple frames of ultrasonic images of the heart of a target object; automatically determining a myocardial contour of the multi-frame ultrasonic image; at least determining the image quality of the myocardial contour of the multi-frame ultrasonic image; determining a modification priority level of a myocardial contour region of each multi-frame ultrasonic image based on the image quality; and displaying at least one frame of ultrasonic image in the multiple frames of ultrasonic images and the corresponding identification information for identifying the modification priority. According to the ultrasonic image analysis method and the ultrasonic imaging system provided by the invention, the modification priority level of the ultrasonic image is determined, and the ultrasonic image and the corresponding identification information for identifying the modification priority level are displayed, so that the visual prompt for recommending the preferential modification frame is realized, and the operation and use efficiency of a doctor is optimized.

The invention discloses a tomato raw material loading, unloading and sorting method and system fused with AI visual recognition, and relates to the technical field of AI vision. A tomato raw material loading, unloading and sorting method fused with AI visual identification comprises the following steps that multiple frames of images are collected, qualified images are reserved through quality detection, the qualified frames are subjected to pixel-level segmentation, tomato segmentation masks are generated through multi-frame fusion, and blind areas are complemented; based on the complemented segmentation mask, high residual target points are judged, space attributes are extracted, the optimal moving path and action queue of the crane pipe are obtained, and a dynamic complementing and scanning queue is inserted to be issued and executed; and in the execution process, residual changes are monitored, the flushing effect is judged, dynamic recognition, complementary scanning and queue updating are conducted, parameters are adjusted, and sorting is conducted till discharging is completed. Accurate recognition, efficient path planning and dynamic supplementary scanning in the tomato loading, unloading and sorting process are achieved, and then the problems that in the prior art, due to insufficient recognition precision, path planning is unreasonable, and residual areas are missed in scanning are effectively solved.

The invention discloses a sports equipment management personnel identification method, system and equipment, and the method comprises the steps: starting and initializing a camera through detecting a sports equipment use trigger signal, and stopping automatic focusing; and shooting multiple frames of images at different focal lengths according to a preset interval, determining a clear area by using a gradient magnitude algorithm, and performing image registration and fusion to form a composite image. And if the whole area is not covered, shooting and splicing are carried out again. And through composite image splicing, distortion cutting, noise reduction and normalization processing, a final image is output for determining the identity of a person. According to the invention, through multi-frame shooting and accurate image processing, a high-quality image is obtained, and information limitation and quality defects of a single image are effectively avoided. Various algorithms are fused to ensure that the image is clear, complete and standardized, the accuracy and reliability of personnel identity recognition are remarkably improved, an accurate image basis is provided for sports equipment management, the normalization and safety of equipment use are guaranteed, meanwhile, the image collecting and processing efficiency is improved, and the management cost is reduced.

The invention provides a river surface flow velocity estimation method and device fusing multi-mode optical flow estimation and PINN, and relates to the technical field of hydrological monitoring, and the method comprises the steps: obtaining meteorological observation data and continuous multi-frame river surface images; performing multi-scale feature processing on the plurality of frames of river surface images to obtain a plurality of related pyramid feature data; carrying out coding processing on the meteorological observation data and at least one frame of image in the continuous multiple frames of river surface images to obtain context mixed feature data; and obtaining an optical flow field according to the multiple pieces of related pyramid feature data and the context mixed feature data, and correcting the optical flow field to obtain a target optical flow field. According to the scheme, high-precision prediction of the river surface flow velocity is realized.

The invention discloses an all-terrain maneuvering target tracking system and an all-terrain maneuvering target tracking method, belongs to the technical field of all-terrain moving target tracking, and aims to solve the problems of poor target tracking trajectory adaptability and large vibration interference in a complex terrain. During operation, the target orientation is positioned, the advancing direction is updated in real time, after the collection range is dynamically delimited, multiple frames of images are collected, and a topographic map is constructed through cutting, splicing and distortion correction; preprocessing the topographic map, extracting landform features, and dividing passable areas; generating a tracking trajectory in the passable area, and optimizing the trajectory through the position change of the target; and adaptively adjusting parameters of the balance component according to the track area, and fusing a vibration signal to judge whether to trigger track updating so as to ensure that the track dynamically adapts to terrain and target movement. According to the invention, through enhancing image processing and multi-source data fusion, the continuity, accuracy and environmental adaptability of target tracking in a complex terrain are significantly improved, and the reliability and efficiency of operation are effectively enhanced.

The invention discloses a method and a device for eliminating high-altitude image jitter based on a static frame feature. The method comprises the following steps of: extracting frames from an input video and extracting a feature point set of each frame; taking the kth frame as a template, counting the occurrence times of feature points through multi-frame proximity matching, and generating a static feature template; feature points are extracted frame by frame and subjected to Lloylomb ratio test with a template, and after dynamic feature points are filtered, a homography matrix is established to achieve frame alignment; detecting a difference set region of the template frame and the current frame, performing clipping compensation, and rewriting the video after image defect is repaired; and iteratively processing until the video ends. The method is superior to a traditional method depending on manual point selection and anchor point fixation in the aspects of dynamic interference suppression, automation degree and picture integrity.

The invention provides a scene-dependent video anomaly detection method, device and equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the abnormal activity analysis of a general abnormal activity rule and an abnormal activity rule of a target scene through employing a large language model, and obtaining an abnormal activity list of the target scene; uniformly sampling multiple frames of images in a time window taking each frame of image in the to-be-detected video as a time center, and constructing a video clip corresponding to each frame of image; generating a prompt input big language model from the subtitles of all frame images in each video clip and the abstract used for prompting description time information, and generating a time abstract corresponding to each video clip; and inputting the time abstract corresponding to each video clip, the abnormal activity list of the target scene, the abnormal score prompt and the output format requirement prompt into a large language model to obtain an abnormal score of each frame of image in the to-be-detected video. According to the invention, anomaly detection is carried out under the condition of scene dependence.

The invention designs a vehicle attitude determination and positioning method, and particularly relates to a special vehicle vehicle-mounted combined attitude determination and positioning method based on a ground view angle odometer, and the method comprises the following steps: obtaining the captured data of the ground view angle odometer, including a road surface image and IMU (Inertial Measurement Unit) data; the method comprises the following steps: extracting feature points from a pavement image based on an SIFT (Scale Invariant Feature Transform) vehicle ground view angle road visual odometer front-end algorithm to form feature descriptors; the method comprises the following steps: optimizing and processing a multi-frame feature descriptor based on a BA optimized vehicle ground view angle visual odometer back-end algorithm to obtain inter-frame pose estimation; inter-frame pose estimation and IMU data are fused based on a loose coupling mode, and the state and pose of the vehicle are predicted through an EKF algorithm. Compared with the prior art, the method has the advantages of realizing real-time and accurate pose positioning of the special vehicle and the like.

The invention discloses a multi-view athlete collision detection penalty auxiliary method for a Kabadi competition, which comprises the following steps: acquiring videos of different views of the Kabadi competition, respectively carrying out target detection, and generating a detection frame of an athlete; performing three-dimensional reconstruction on the position of the athlete according to the detection frame data of the multiple visual angles at the same moment, and analyzing to obtain space-time behavior data of the athlete; for each view angle, according to the athlete detection frame and the spatiotemporal behavior data, detecting whether potential collision occurs in the current frame, and if potential collision occurs in multiple continuous frames, marking the current frame as an event frame cluster; performing posture detection on the athletes in the event frame cluster, calculating the nearest distance between joint points of an attacking party and a defending party, selecting a frame with the minimum comprehensive distance as a penalty key frame, and marking the frame as a collision event; and analyzing and converting the space-time behavior data and the collision event of the athlete and a reconstructed image generated by a plug flow rear end into a live broadcast picture, state information and event prompt for a referee to understand and outputting the live broadcast picture, the state information and the event prompt.

The invention discloses an occupied grid labeling method and device, and the method comprises the steps: extracting an inter-frame pose transformation matrix and a detection frame tracking identifier corresponding to time sequence data when multiple frames of time sequence data are received; according to the inter-frame pose transformation matrix, carrying out occupied grid labeling on a static object in the time sequence data, and generating a static labeling result; tracking the position of the detection frame in each frame of time sequence data in real time according to the detection frame tracking identifier, and constructing a motion track of a target to which each detection frame belongs; in each frame of time sequence data, carrying out occupied grid labeling on the region where each motion track is located, and generating a motion labeling result; and fusing the static labeling result and the motion labeling result to generate an occupied grid labeling result corresponding to the time sequence data. Therefore, the occupancy grid label of the static object is propagated in combination with the inter-frame pose transformation matrix, and meanwhile, by fusing the occupancy grid label of the static object and the occupancy grid label of the motion trail, the requirements of the occupancy grid label for efficiency, precision and generalization are effectively met.

A computerized technique is disclosed of identifying object features in an environment of a vehicle. The technique includes receiving, by an encoder, data representing a plurality of frames, the frames providing point-in-time versions of a segmented pointed cloud derived from output of one or more radar sensors of the vehicle and including points that represent radar detections corresponding to an object in the environment at respective instants in time. The technique further includes arranging the plurality of frames in a time-ordered queue and processing the frames in the queue, including (i) selecting, from among the points, a plurality of sample points that spans multiple frames of the queue, (ii) forming a plurality of groups of points based on respective sample points of the plurality of sample points, and (iii) extracting features of the object based on the plurality of sample points and the plurality of groups.

The invention discloses a display picture detection method and device, equipment, a medium and a product, relates to the technical field of image recognition, and utilizes an image classification model to analyze multiple frames of images so as to determine an initial classification result. And respectively extracting an edge feature map, a diagonal gradient feature map, an optical flow feature map and a difference feature map from the multiple frames of images. And based on the edge relative displacement between the edge feature maps of the adjacent frames and the gradient relative displacement between the diagonal gradient feature maps of the adjacent frames, determining a first identification result whether the display picture of the image flickers or not. And according to the initial classification result, the optical flow structure similarity corresponding to the multiple frames of optical flow feature maps and the difference structure similarity corresponding to the multiple frames of difference feature maps, determining a second identification result whether the display picture of the image is lagged or not. Different types of feature maps in multiple frames of images are analyzed, so that the abnormal problem of the display picture can be timely and accurately detected, and the maintainability of the system is improved.

The invention discloses a dribbling evaluation method, a related device, equipment and a storage medium. The method comprises the following steps: acquiring a target video shot when an examination object uses a target sphere to carry out dribbling examination in an examination site; detecting at least based on a current image in the target video to obtain a first position of the assessment object in the assessment site and a second position of the target sphere; obtaining a first motion track of the assessment object based on the first positions of the multiple frames of images in the target video, and obtaining a second motion track of the target sphere based on the second positions of the multiple frames of images in the target video; and obtaining a dribbling evaluation result of the assessment object based on the first movement track and / or the second movement track. According to the scheme, the accuracy of carrying out automatic evaluation on dribbling assessment can be improved.

The present disclosure provides a method for analyzing exam cheating. Among them, the method includes: obtaining multiple frames of images in a video stream and determining the paper offset direction of a first object in each frame of the multiple frames of images; judging whether there is a risk of paper exposure based on the paper coordinates and the hand coordinates of the first object in each frame of image; if there is a risk of paper exposure, determining a cheating suspect object associated with the first object based on the paper offset direction; determining an angle eigenvalue, an eye writing eigenvalue, and a paper result analysis eigenvalue corresponding to the cheating suspect object; determining a comprehensive score based on the angle eigenvalue, the eye writing eigenvalue, and the paper result analysis eigenvalue, and determining the degree of cheating suspicion of the cheating suspect object according to the comprehensive score. It realizes the use of intelligent video analysis technology, combined with the correlation analysis of the answer sheet results, improves the efficiency and accuracy of cheating analysis, reduces the burden of manual review, and improves the accuracy of cheating behavior recognition.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for compressed-domain compositing of video streams. A server obtains compressed video streams, each compressed video stream including multiple frames, each frame including a video content region that is a portion of the frame, encoded using a segment identifier for pixels included in the video content region, and encoded using a set of static symbol frequencies. The server receives, from a user device, a request for video stream content, and composites a first and a second compressed video stream to obtain a compressed-domain composite video stream including a first video content region of the first compressed video stream and a second video content region of the second compressed video stream, and provides, to the user device, a packet including a set of frames of the compressed-domain composite video stream decodable by a single decoder.

The invention discloses a method and a system for generating a real-time dialogue image of a digital life entity, and relates to the technical field of artificial intelligence, the method comprises the following steps: acquiring audio and video information, the audio and video information comprising multiple frames of image information and audio information comprising a target person, and the image information and the audio information being in time synchronization; identifying image emotion information in the image information; performing semantic analysis on the audio information to generate semantic information; generating target semantic information according to the image emotion information and the semantic information; generating target prompt word information according to the target semantic information; and inputting the target cue word information into a pre-trained dialogue model to generate dialogue information of the digital life entity and the target person. The obtained target prompt word contains rich and accurate effective information, the current state of the target person can be accurately reflected, and the digital life body has high dialogue ability and is more real.