19 results about "Global matching" patented technology

Examples relate to a concept for an entry-exit matching system, and in particular to an evaluation device, a method and a computer program for person re-identification for entry-exit matching in a transportation system. The evaluation device comprises processing circuitry configured to obtain a plurality of re-identification codes. Each re-identification code represents a person being recorded by at least one camera when entering or exiting at least a section of the transportation system. The processing circuitry is configured to match the plurality of re-identification codes using a global matching scheme to obtain a plurality of matched pairs of re-identification codes, such that each matched pair of re-identification codes comprises a re-identification code of a person entering and a re-identification code of a person exiting. The global matching scheme is based on reducing an overall distance between the re-identification codes of the matched pairs of re-identification codes over the plurality of matched pairs of re-identification codes. The processing circuitry is configured to determine points of entry and exit for the plurality of matched pairs of re-identification codes.

This invention discloses a long text matching method combining noise filtering and a divide-and-conquer strategy. The method includes: constructing a long text matching model, which comprises a keyword extraction layer, an association extraction layer, and a filtering layer. The keyword extraction layer extracts keywords from the text; the filtering layer filters noise from the text based on sentence similarity to obtain a denoised text sequence; and the association extraction layer further removes keywords from the denoised text sequence to obtain the remaining associated text. The long text matching model is trained with the optimization objective of minimizing a set overall loss function, which reflects the global matching distribution and combines the keyword and association matching distributions. For the target text, real-time text matching is performed using the trained long text matching model. This invention improves the generalization ability and accuracy of text matching.

This invention discloses a video tag dynamic overlay anti-shake method and system based on AR, comprising: acquiring real-time video stream and device pose parameters at corresponding timestamps; constructing a dynamic perspective projection model based on the device pose parameters and calculating the initial pixel coordinates of the target AR tag in the current video frame; planning a local feature search window based on the initial pixel coordinates; calculating the average pixel displacement vector of image feature points in the local feature search window of the current and adjacent historical video frames; performing reverse displacement compensation on the average pixel displacement vector to obtain the target rendering coordinates, and rendering the target AR tag to the current video frame based on the target rendering coordinates. Compared with the prior art, this invention can overcome the shortcomings of large drift errors caused by simply relying on mechanical pose, high computational power consumption caused by global matching, and high-frequency jitter of tags caused by slight device vibrations, thereby improving tag mapping accuracy, reducing system computational power consumption, and improving the visual stability of video overlay.

This invention proposes a method and system for detecting missing weld points on vehicle bodies based on 3D vision and global matching, belonging to the field of image recognition technology. The method includes: determining the transformation relationship between the camera coordinate system and the vehicle body coordinate system through calibration; at each observation position, a 3D camera acquires an image I containing a frame of 2D texture and the corresponding 3D data; inputting the acquired image I into a pre-trained deep learning object detection model, outputting the bounding box information (u, v, w, h) for each weld point; for each detected weld point, calculating the set S of the weld point's 3D coordinates in the vehicle body coordinate system. det For the theoretical solder joint library S cad Each theoretical solder joint P in cad,i In set S det The invention employs a "detect first, match later" strategy, which does not rely on the precise projection of the theoretical position, thus reducing the requirements for the positioning accuracy of the production line tooling fixtures and conveying system.

The application relates to a multi-sensor-based repositioning method, device, equipment and storage medium. The method comprises the following steps: acquiring environment images collected by respective sensors; performing global matching on the respective environment images and images in a prior environment image library to obtain respective robot poses before updating corresponding to the respective sensors; performing local matching on the respective environment images and respective surrounding environment images corresponding to the respective robot poses before updating in the prior environment image library to obtain score values of respective robot poses after updating; and determining a repositioning result of a current robot pose according to the score values of the respective robot poses after updating. By performing global matching and local matching on the environment images collected by different types of sensors and the images in the prior environment image library, the repositioning result of the current robot pose can be ensured to have accuracy and reliability.

The application relates to a stereo matching and disparity map optimization method based on adaptive smooth constraint, which comprises the following steps: acquiring satellite stereo pairs and refined rational polynomial coefficients; processing the satellite stereo pairs and the rational polynomial coefficients by adopting a core line resampling method based on orthographic correction to generate core line image pairs; performing stereo matching on the core line images by adopting a semi-global matching algorithm based on adaptive parameters to generate an initial disparity map; performing optimization processing on the initial disparity map by using an improved weighted least square algorithm to obtain a final disparity map; and generating point clouds by spatial forward intersection based on the final disparity map, performing gridding processing on the point clouds, and acquiring a final digital surface model. Compared with the prior art, the application realizes the balance between noise suppression and detail reservation, thereby improving the accuracy and completeness of the DSM.

The disclosed computer-implemented method may include instant and optimized matching of transportation requesters with transportation providers by precomputing and caching evaluations of matching schemes between existing requests and available providers, where each of the matching schemes excludes one of the available providers. The possibility of matching any new request to a given provider may then be evaluated according to the cached results of the matching scheme that excluded that provider along with an evaluation of matching the new request to the provider, which may be a computationally easy problem. Thus, the new request may be matched to a provider instantly without waiting for the next iteration of solving the global matching problem. Matching requestors and providers in this way may also improve the accuracy of estimated time of arrival information provided to requestors. Various other methods, systems, and computer-readable media are also disclosed.

A method for UAV non-orthogonal communication and edge scheduling based on semantic symbol matching comprises the following steps: acquiring and partitioning the dataset, constructing an air-to-ground semantic system, matching semantic symbol sequences, correcting semantic errors and recognizing images, constructing a feature fusion strategy network, determining unloading, training the feature fusion strategy network, and testing the feature fusion strategy network. This invention employs a Transformer sequence prediction model, using semantic information to analyze the spatial correlation of the context, and repairs damaged semantic symbols caused by multi-vehicle non-orthogonal collisions or fading, thus improving the success rate. The UAV calculates the direction vector and spatial similarity of the observed signals, and uses the Hungarian algorithm to complete global matching, enabling concurrent vehicle unloading and improving the processing scale of the vehicle-to-everything (V2X) network. A multi-branch feature fusion strategy network is adopted, combining the policy network and the value network to construct a near-end policy optimization framework, achieving adaptive perception of real-time state changes of vehicles and UAVs, and improving system resource utilization.

The application provides a kind of power cabinet inspection method, device and inspection robot based on machine vision, it is related to power inspection technical field.The method comprises: obtaining the image to be identified of power cabinet, input to target detection model, detect the equipment information and equipment frame of each instrument and equipment;The image to be identified is input to optical character recognition model, detects text content and text frame;According to the positional relationship of each text frame, each text frame is merged and processed;According to the positional relationship of equipment frame and text frame, equipment frame and text frame are preliminarily matched, global optimal matching is carried out through global matching algorithm, and the unique corresponding relationship of equipment frame and text frame is obtained;The equipment frame and text frame with unique corresponding relationship are subjected to semantic verification to identify the instrument and equipment and label related to semantics.The application can solve the problem that the existing inspection robot automatic inspection process is highly dependent on off-line naming and calibration of inspection equipment.

The application provides a kind of accurate detection system and method of slag quantity based on binocular vision, system includes dry slag machine automatic speed measurement module, calculates the real-time running speed of dry slag machine conveying chain plate;Slag quantity detection module, the time sequence alignment of slag layer three-dimensional point cloud reconstructed by binocular stereo vision is carried out, and the volume of slag quantity is calculated;Interference automatic identification module is used to distinguish dry slag machine conveying chain plate structural member and coal slag, and provides higher quality slag layer three-dimensional point cloud data for slag quantity detection module.The application synchronously collects image by binocular stereo vision, reconstructs high-density three-dimensional point cloud of slag layer by combining improved semi-global matching stereo matching algorithm;Further, the volume of accumulated coal slag is accurately calculated by surface gridding based on Delaunay triangulation and scanning line integration method;And by associating the density parameters in coal database, the volume value is converted into mass data, which realizes accurate and quantitative detection of slag quantity from three-dimensional form to quality index.

The application discloses a kind of based on intelligent matching's net booking carpooling scheduling management method and system, it is related to dispatching management technical field, it first utilizes historical log data deep mining driver behavior characteristics and system operation law, respectively constructs and trains the dual model of accurately depicting driver preference and evaluating system income. Subsequently, with this dual model as the basis, global matching evaluation is carried out on real-time supply and demand, and the maximum boundary correlation strategy is used to optimize distribution, to generate a recommended list that takes into account driver willingness and diversity. Further, the recommended list is pushed to the driver end to receive autonomous selection feedback, and based on the feedback, conflicts are resolved, and the final matching solution is dynamically derived. In this way, the problem of low dispatch success rate and driver turnover caused by ignoring the heterogeneous preferences of drivers in traditional scheduling can be effectively solved, thereby achieving a coordinated improvement in the satisfaction of both supply and demand and the overall scheduling efficiency of the system in the context of net booking carpooling.

The application provides a swarm-to-swarm allocation method, device, equipment and medium, and relates to the technical field of automatic control, wherein the method comprises the following steps: clustering multiple unmanned aerial vehicle nodes and multiple target nodes based on attribute information of the multiple unmanned aerial vehicle nodes and the multiple target nodes to obtain multiple unmanned aerial vehicle clusters and multiple target clusters; performing bipartite matching on all target clusters and all unmanned aerial vehicle clusters to obtain a matching relationship between each target cluster and each unmanned aerial vehicle cluster; performing intra-cluster node matching on any target cluster and unmanned aerial vehicle cluster that establishes the matching relationship based on a mixed integer programming model to obtain an intra-cluster matching result; performing matching on remaining nodes based on the mixed integer programming model to obtain a remaining matching result; and determining a global matching result based on the intra-cluster matching result and the remaining matching result. The swarm-to-swarm allocation method provided by the application improves matching efficiency while ensuring matching accuracy.

The application discloses a multi-modal image matching method based on phase consistency structure information distillation. The method is used on a pair of multi-modal images to be matched. Firstly, stable structure prior information is obtained through phase consistency calculation, and is combined with multi-scale features extracted by a convolutional neural network to form structure-guided feature expression. Then, structure distillation training is performed on the network by using structure consistency loss and cross-modal consistency loss, so that the coarse scale and fine scale features obtained have stronger structure consistency under the cross-modal condition. On this basis, a feature transformation module stacked alternately by self-attention and cross-attention is used to complete coarse scale global matching to generate a candidate matching pair. Then, the candidate matching pair is refined locally by fine scale feature matching, and a final matching set is obtained. The application deeply integrates phase consistency structure prior and a deep learning matching framework, guarantees the generalization of cross-modal matching, and improves matching precision and calculation efficiency.

This invention discloses a rapid image stitching acquisition method for large-size aerospace structures, comprising: acquiring original images of the workpiece and performing vertical stitching to obtain a sequence of vertically stitched images; dividing the overlapping areas of adjacent vertically stitched images into blocks, using a deep learning network to extract and match features from image block pairs, determining local matching point pairs and restoring coordinates to form a global matching point pair set; calculating the affine transformation matrix of adjacent images based on the global matching point pair set, and then obtaining the cumulative affine transformation matrix using the first image as a reference; projecting each image onto a global canvas and fusing them at once according to the cumulative affine transformation matrix to obtain a complete workpiece surface image; finally, performing sliding window detection to identify small-size targets on the vertically stitched images, and performing whole-image detection to identify large-size targets on the complete image, and statistically storing the target information. This invention overcomes the problems of feature loss in high aspect ratio image stitching, accumulation of multi-image stitching errors, and missed detection of multi-scale targets.

The application discloses a sea surface stereo matching and depth measurement method based on wave main direction correction, and aims to solve the feature degradation and mismatch caused by the consistency of the transverse sea wave texture and the matching search direction. The method first denoises and enhances the image and extracts the texture, calculates the global flow direction by using the block Hough transform, and adaptively starts the rotation optimization; secondly, an ambiguity scoring model based on normalized cross correlation (NCC) is constructed to filter the optimal rotation angle, the image is synchronously rotated and cropped, and the transverse repeated texture is reconstructed into a periodic structure with high gradient change; then, feature points are extracted in the rotated image domain, the RANSAC algorithm is combined to filter and estimate the fundamental matrix to complete the epipolar rectification, the prior disparity search range is locked according to the distribution of the homonymic points, and the semi-global matching (SGM) algorithm is used to generate the initial disparity; finally, the real depth is recovered through reverse correction, the point cloud is generated, and the three-dimensional surface of the sea wave is reconstructed. The application fundamentally eliminates the matching confusion caused by the transverse texture, significantly improves the three-dimensional reconstruction accuracy, and is suitable for application scenes such as marine wave monitoring and disaster warning.

This application relates to the field of distribution network fault location technology, specifically to a method, device, and system for locating fault sections in distribution networks, solving the technical problem of the inability to accurately locate fault locations in distribution networks. The method includes: acquiring a static topology map and a dataset of alarm terminals; for each candidate section in the static topology map, deriving the expected pointing vector from each theoretical node in the static topology map to the candidate section based on the topological connection relationships of the static topology map; constructing a cost matrix for matching each terminal with each theoretical node one-to-one based on the field current vector, expected pointing vector, and topological hop distance of each terminal in the alarm terminal dataset; solving for the optimal allocation scheme that minimizes the global matching cost of the cost matrix and the corresponding minimum total matching cost; and determining the fault section location result based on the minimum total matching cost.

This invention relates to the field of computer remote control and cluster scheduling technology, specifically a host scheduling node method based on a computer remote control system. The method includes: collecting operating parameters of each node in a remote host cluster and predicting fault risk using Markov chains; calculating node load rate and health; determining node status; constructing an ARIMA-LSTM hybrid model to predict future load rate; receiving a set of tasks to be scheduled, calculating task priority coefficients, and generating a scheduling queue; constructing a task-node bipartite graph, using graph neural network embedding vectors, and combining resource constraints with cosine similarity and the Hungarian algorithm to solve for the global optimal match; detecting faulty nodes, migrating incomplete tasks, isolating faulty nodes, and performing recovery detection. This invention achieves accurate perception and prediction of node status and load trends, and improves the reliability, resource utilization, and execution efficiency of high-priority tasks in cluster scheduling through intelligent global matching and proactive fault tolerance.

Embodiments of the present specification provide a method and device for training a semantic feature vector generation model and semantic search. In the method for training the semantic feature vector generation model, text extraction is performed on a sample reference text of an obtained training sample to obtain corresponding first and second partial texts; the sample query text of the obtained training sample and the sample reference text and the corresponding first and second partial texts are provided to a current semantic feature vector generation model to obtain corresponding semantic feature vectors; the global matching information and the local matching information of the sample query text and the sample reference text are determined based on the similarity between the semantic feature vectors corresponding to the sample query text and the sample reference text, the first partial text and the second partial text respectively; and the loss value of the loss function determined according to the global matching information and the local matching information is used to adjust the parameters of the current semantic feature vector generation model.

This invention discloses a lightweight depth estimation method and system based on global search with high disparity. The method first acquires and preprocesses left and right views to form a stereo image pair. Next, the stereo image pair is input into a feature extraction network to extract multi-scale feature tensors, including 1 / 8 and 1 / 4 scale feature tensors, and a context feature tensor. Then, based on the 1 / 8 scale feature tensor, a global matching cost volume is constructed, and a coarse disparity map is calculated. The initial disparity map is then repaired and upsampled to the 1 / 4 scale. A three-scale matching cost volume is constructed with reference to the 1 / 4 scale feature tensor and input into a bilateral aggregation network for regression optimization. Finally, a convex upsampling module restores the regression-optimized disparity map to the original image resolution, and the depth image is calculated by combining the stereo parameters. This invention provides smooth repair for physically occluded and weakly textured regions, outputting high-precision estimation results with rich details and sharp depth edges.