1842 results about "Image matching" patented technology

The invention relates to the technical field of agricultural knowledge base retrieval, and discloses an intelligent retrieval system for a knowledge base, which comprises an image preprocessing unit, an image adjusting unit, an image matching unit and a retrieval judgment unit, and is characterized in that a binary mask matrix is generated through image enhancement and pixel clustering, a modular entropy value is calculated in combination with parameters, and a basis is provided for feature extraction; multi-scale feature self-adaptive extraction is achieved through dynamic scale adjustment, feature distortion is avoided, feature points are evenly distributed through density dynamic adjustment and quality evaluation, high-quality feature points are screened, double screening of matching pairs is achieved by constructing a comprehensive evaluation mode, and the defects of an SIFT algorithm are overcome by dynamically adjusting a similarity threshold value and sorting. And the accuracy of image retrieval in a complex scene is improved.

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

The invention provides a three-dimensional modeling processing method based on unmanned aerial vehicle oblique photography. The method is applied to the technical field of three-dimensional modeling, and comprises the following steps: obtaining a serialized image set containing geographical coordinate information according to original image data collected by an oblique photography camera carried by a multi-rotor unmanned aerial vehicle; according to time-space synchronization parameters of the serialized image set, determining a multi-view image matching relation matrix with an overlapping degree quantitative index; determining mixed three-dimensional point cloud data fusing sparse point cloud and dense point cloud according to geometric constraint conditions of the multi-view image matching relation matrix; determining an initial three-dimensional grid model with multi-level details according to the topological connection relationship of the mixed three-dimensional point cloud data; and determining an optimized three-dimensional model based on adaptive texture mapping according to the surface curvature distribution characteristics of the initial three-dimensional mesh model. In this way, the efficiency of three-dimensional modeling can be improved.

The invention discloses an orthoimage generation method and device based on three-dimensional Gaussian splashing and a storage medium, and the method comprises the steps: responding to an orthoimage generation signal of a detected building region, obtaining a multi-view image obtained by photographing the detected building region from a plurality of view angles, determining an image acquisition parameter of an image acquisition device corresponding to the multi-view image; performing cross-image matching on the feature points in each image, and determining feature point pairs in each image based on a matching result; based on the feature point pairs and the image acquisition parameters, performing three-dimensional reconstruction on area points in the detected building area represented by the feature point pairs to obtain three-dimensional point clouds, and performing Gaussian distribution processing on the three-dimensional point clouds to obtain a plurality of three-dimensional Gaussian ellipsoids; and determining a two-dimensional projection plane, determining depth information of each three-dimensional Gaussian ellipsoid from the two-dimensional projection plane, and splashing each three-dimensional Gaussian ellipsoid into the two-dimensional projection plane based on the depth information to obtain an orthoimage of the measured building area.

The invention provides a multi-modal data urban land utilization scene classification method based on street scene image matching remote sensing image, designs a cross-view image matching method based on geometric transformation, and realizes spatial position matching of a multi-view street scene image and a remote sensing image; the method comprises the following steps: constructing an urban land utilization scene classification model based on a high-resolution remote sensing image and a streetscape image by utilizing a pre-trained large model with relatively strong generalization ability, and extracting depth features of different scales and different visual angles; designing a global-local feature fusion module, and aggregating features of different levels through self-attention; a gate control cycle fusion module is designed to realize interaction of streetscape images at different shooting time and shooting view angles; through a channel attention mechanism, remote sensing image features and streetscape image features are distributed according to different weights, contribution proportions of the features in different classification tasks are optimized, a dynamic gradient modulation strategy is designed, and different modal information is adaptively fused, so that accurate classification of urban land utilization remote sensing images is realized.

The invention provides an unmanned aerial vehicle oblique photogrammetry data intelligent processing method and system, and relates to the technical field of unmanned aerial vehicle photogrammetry, and the method comprises the steps: carrying out the preprocessing of an oblique photogrammetry original image, obtaining the exterior orientation elements of a camera, carrying out the scene semantic segmentation through a deep convolutional neural network, adaptively determining the optimal three-dimensional reconstruction parameters of each region, and obtaining the optimal three-dimensional reconstruction parameters of each region. On the basis of multi-view image matching, feature point parallax information is calculated to generate dense point clouds, different areas are processed by adopting an adaptive grid division strategy and a corresponding noise reduction algorithm, and finally, a three-dimensional scene model with real textures is constructed, so that high-precision three-dimensional reconstruction of different scene areas is realized.

Systems, methods, and computer-readable media for aerial localization in the absence of satellite (e.g., global navigation satellite system (GNSS)) signals while flying over heterogeneous environments. The present embodiments provide a training strategy for robust cross-seasonal image matching in complex, real-world environments. The challenge put forward is to successfully localize a UAV flying over both densely and sparsely populated areas by means of image registration. To this end, the focus of the present embodiments can be on devising loss components to a visual recognition network that adequately embeds the diverse feature types on the ground. A normalized error metric can be provided as well in order to render a conceptually different method comparable to the present approach. The present technical contributions can provide robust visual recognition algorithms in challenging natural environments.

The invention discloses a cross-modal pedestrian re-identification method based on a multi-scale cross attention Transform, and the method comprises the following steps: step 1, image acquisition, step 2, feature set acquisition, step 3, feature fusion, and step 4, feature determination: in an initial stage of a network, two standard data sets are used to acquire RGB and infrared images, and then, the RGB and infrared images are subjected to feature fusion; the method comprises the following steps: extracting key features from RGB and an infrared image by using MSCAViT to obtain two feature sets, then converting the RGB image into a grayscale image, inputting the grayscale image into an MSCAViT model to obtain a third feature set, determining an optimal weight by using an OSCSO method through weighted fusion of the three feature sets, finally obtaining a fused feature, inputting a result feature into a decoding end of the MSCAViT, and finally obtaining a final image through the decoding end of the MSCAViT. According to the method, features finally used for image matching are generated, the model is evaluated through multiple indexes, compared with a traditional method, and the result shows that the proposed method shows a more excellent effect on solving the modal difference problem in a VI Re-ID task.

The invention provides a multi-modal geographic positioning method based on a knowledge graph. The method comprises the following steps: graph construction: constructing a geographic knowledge graph; image positioning: calculating the entity similarity between the to-be-queried image and each sub-image so as to select an image positioning candidate sub-image, calculating the image matching similarity between the to-be-queried image and each image positioning candidate sub-image so as to determine an image positioning target sub-image, and taking the latitude and longitude coordinates of each image positioning target sub-image as a positioning result; text positioning: converting a to-be-queried text into map representation, calculating the map similarity between a text query sub-graph and each sub-graph to select a text positioning candidate sub-graph, and calculating the semantic similarity between the to-be-queried text and each text positioning candidate sub-graph, and calculating a text matching similarity corresponding to each text positioning candidate sub-graph based on the graph similarity and the semantic similarity corresponding to each text positioning candidate sub-graph so as to determine a text positioning target sub-graph, and taking latitude and longitude coordinates of each text positioning target sub-graph as a positioning result.

The invention provides a high-precision positioning method and system based on multi-source information fusion. The high-precision positioning method comprises the following steps: acquiring the omm positioning information of the pose of an unmanned aerial vehicle in real time based on a visual inertial navigation unit; a global feature extraction algorithm is combined with a similarity algorithm, and a target satellite image matched with the airborne image of the unmanned aerial vehicle is selected from a satellite image library; using a local feature matching algorithm to combine with the geographic coordinates of the target satellite image to obtain GPS predicted positioning coordinates; fusing the odom positioning information and the GPS predicted positioning coordinates by using a global fusion technology to obtain preliminarily fused odom positioning information; according to the method, secondary fusion is carried out on the basis of GPS positioning data obtained by a real GPS sensor in combination with the preliminarily fused odom positioning information to obtain final odom positioning information, and through a double fusion strategy, the advantages of a multi-source positioning technology are fully integrated, and the positioning accuracy is improved.

The invention discloses an automobile part quality detection method and system based on artificial intelligence visual inspection, and belongs to the field of artificial intelligence machine visual inspection, and the method comprises the steps: firstly, carrying out the registration of a collected RGB image and a depth image, and extracting a part region through a saliency detection network; two-dimensional key points are extracted based on an RGB region image and are matched with key points of a three-dimensional model, an initial three-dimensional attitude is obtained by adopting a PnP algorithm, iterative registration is performed with the three-dimensional model in combination with a point cloud generated by a depth image, and a fine three-dimensional attitude is obtained. And calculating a geometric transformation matrix from the part to a standard front view attitude according to the attitude, and performing attitude correction on the RGB and depth region image. And then matching the corrected image with a standard template image by using a feature detection and matching network so as to correct the position of the detection window. And finally, the three-dimensional size of the part is calculated in the corrected detection window in combination with the depth value, and tolerance judgment is carried out. And the precision, the robustness and the automation level of online detection of the automobile parts can be obviously improved.

The invention relates to the technical field of computer vision and image processing, in particular to a robust image matching system and method based on multi-scale attention feature fusion, and the system comprises a feature extraction module which extracts a multi-resolution feature map containing image features from a to-be-matched image; the position coding module performs spatial position coding on the image features to improve the spatial perception capability of the model; the multi-scale attention module performs multi-scale enhancement processing on the image features, so that the model is more effectively focused on the significant part of the image; the attention module establishes a dependency relationship between image features; the coarse-level matching layer module generates a coarse-level matching result and screens out a matching pair with relatively high confidence coefficient; and the coarse-to-fine module carries out fine processing on the coarse matching result, so that more accurate feature matching is realized. According to the method, the effectiveness and reliability of the method are verified by testing the image matching performance of the to-be-tested picture under the conditions that the to-be-tested picture contains different illumination changes, extreme visual angle changes, repeated textures and the like.

The invention relates to the technical field of marine oil spill pollution monitoring, in particular to an oil spill monitoring method and system combined with an image analysis processing technology. The method comprises the following steps: S1, acquiring an initial oil spill diffusion path through remote sensing monitoring equipment, extracting a floating ice distribution image in a path coverage area, selecting a floating ice image at the tail end of the path as a reference image, and establishing an adjacent floating ice image matching model; s2, based on the adjacent floating ice image matching model, determining a minimum feature floating ice image through an adaptive pixel threshold algorithm; and establishing an oil-ice complex edge feature matching matrix. According to the method, the recognition precision is improved through multi-feature fusion and a self-adaptive threshold value, oil film permeation and streaming laws are analyzed through multi-scale analysis, a spatio-temporal evolution model is constructed to realize diffusion path closed-loop tracking, phase change nodes are accurately captured in combination with a dynamic verification formula, the environmental adaptability of a complex ice region is remarkably enhanced, and the method is suitable for popularization and application. And real-time early warning and ecological protection decision support is provided for polar region oil spill accidents.

The invention relates to a passing method, system and device of an intelligent gate sentry, and the method comprises the steps: obtaining the portrait data of a visitor when the visitor enters a first preset detection region; performing image matching on the portrait data and a reservation database, and judging whether the visitor is a target reservation visitor; if the visitor is the target reservation visitor, a voice prompt is generated, and the voice prompt is used for prompting the visitor to perform an identity verification action; obtaining verification action data of the visitor; matching the verification action data with a verification action set during reservation; when the verification action data is successfully matched with the verification action, controlling the access control equipment to release; if the visitor is not the target reservation visitor or verification action matching fails, controlling the virtual security interaction terminal to perform access registration on the visitor to obtain access information of the visitor; and when the access information meets the preset requirement, controlling the access control equipment to release. According to the invention, the accuracy of visitor identity authentication can be improved.

Provided are a multimodal image matching method and system, terminal device, and storage medium. The method includes: performing self-supervised feature extraction on an optical image and a synthetic aperture radar (SAR) image to obtain a repetitive feature point between the optical image and the SAR image; segmenting the optical image and the SAR image into a first image block sequence based on the repetitive feature point, and performing feature extraction on the first image block sequence through a dual-branch network to obtain feature description vectors of the optical image and the SAR image respectively, where the dual-branch network includes a first branch network for extracting a global feature and a second branch network for extracting a local feature; and performing feature matching on the optical image and the SAR image based on the feature description vectors to obtain a matching point pair between the optical image and the SAR image.

The invention relates to the technical field of semiconductor manufacturing, in particular to an accurate detection method and system for a flat-edge sheet wafer placement rotary chuck, and the method comprises the steps: when a wafer is placed on the rotary chuck, scanning the lap joint condition of a wafer edge and a chuck contact area through a laser sensor, and obtaining the real-time distribution data of an edge lap joint sheet; flat edge position information is extracted from the edge lap joint distribution data, an image matching algorithm is adopted to compare a preset orientation reference, and a specific angle value of orientation deviation is determined; according to the orientation deviation angle value and the edge lapping piece distribution data, the rotating speed and the angle of the rotating chuck are adjusted through a cooperative control algorithm, and a final positioning result of the wafer on the chuck is obtained; after a final positioning result is obtained, the in-place state of the wafer is classified and judged through a deep learning model, and whether the residual deviation or wafer lapping phenomenon exists or not is determined; and extracting abnormal data from a classification judgment result, and adjusting collaborative parameters of carrying and placing according to the abnormal data to obtain an optimized process control scheme.

The invention relates to the technical field of image processing, in particular to a cap sealing visual detection method, device and system applied to a beverage bottle cap, and the method comprises the steps: collecting a standard cap sealing image after a beverage bottle is sealed, and a to-be-detected cap sealing image in each direction; respectively extracting feature points on the edge contours in the standard seal cover image and the to-be-detected seal cover image to form a standard edge sequence and a to-be-detected edge sequence; determining a gradient distribution index of any edge feature point; determining the feature criticality of any edge feature point; determining the edge symmetry degree of the to-be-detected sealing cover image in each direction; obtaining each feature key point; determining a quality discrimination coefficient of the beverage bottle cap; and performing iteration processing on the edge feature points in the to-be-detected edge sequence, re-screening feature key points, and based on the quality discrimination coefficient of each iteration, detecting the defect of the beverage bottle sealing cover. According to the invention, the accuracy of beverage bottle cap quality detection can be improved, and the image matching efficiency can be improved.

The invention relates to an automatic visual alignment method and system based on a photoetching machine and a storage medium, and relates to the technical field of optical alignment. The automatic visual alignment method comprises the following steps: sequentially focusing image taking areas according to a preset multi-light-source visual focusing mechanism, recording corresponding global focusing parameters, and collecting corresponding original single-layer images; screening all the original single-layer images according to a preset image definition threshold value to obtain a to-be-detected image, and extracting corresponding key channel features; calculating a comprehensive feature error of the key channel features according to a preset image hierarchy template, and judging an initial focusing state in combination with a preset parameter error interval; correcting the global focusing parameter according to the initial focusing state in combination with a preset cooperative calibration mechanism, generating a corresponding control signal, and cooperatively tuning the corresponding visual equipment; through multi-light-source multi-time visual image capturing, the problem of poor alignment precision caused by light source errors and image capturing errors is reduced, the image matching accuracy is improved, and the visual alignment precision is improved.

The invention discloses a design scheme generation method based on an information retrieval technology, and belongs to the field of information retrieval and image-text generation. Collecting the text description and an image matched with the content of the text description, preprocessing the text description and the image respectively, and performing feature extraction processing to obtain extracted visual feature monk and semantic features: extracting the visual features in the image data; for character description data matched with the image, processing the character image by using a semantic segmentation network so as to identify and segment a character region, extracting uniformly distributed semantic feature points through a quadtree distribution algorithm, and integrating the semantic feature points of all the regions to form a complete semantic feature point set corresponding to the image data including characters; a multi-scale feature map is constructed by using a feature pyramid network, the extracted visual features and semantic features are mapped to a unified feature space in the multi-scale feature map, and it is ensured that key information of the respective features is reserved in the mapping process.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image generation method, device and equipment based on theme information and a medium. The method comprises the steps that input information is analyzed to generate theme information and copywriting information, a cue word set is generated, and a figure image set and a background image set are generated; fitting the segmented figure image with the background image to form a head image candidate set, and selecting a head image matching template frame to generate a basic image; decomposing the copywriting to generate a sub-module initial picture set, and adding a gradual change effect to form a sub-module picture set; the adjusted sub-module pictures are obtained based on size adjustment, and a pre-synthesized image is generated through splicing; and identifying the blank area to draw a title text to obtain a final image. Through theme analysis, template matching, image splicing, modular copywriting processing and blank drawing, poster generation efficiency is improved, layout flexibility is enhanced, and visual unification is realized.

The invention discloses an air-ground image matching method, device and equipment and a storage medium, and the method comprises the steps: carrying out the sparse reconstruction of an aerial image, obtaining a sparse point cloud model of an aerial image pose and a scene, generating the camera visual angle information of a new camera according to the sparse point cloud model, and enabling the camera visual angle information to serve as a middle visual angle of the scene, the visual angle difference between the space images and the ground images can be reduced, three-dimensional reconstruction is carried out through camera visual angle information, the rendered image of the middle visual angle after rendering is obtained, feature points corresponding to the rendered image are further removed after feature point matching is carried out on the ground image, the aerial image and the rendered image, and the image quality is improved. At the moment, a matching relationship between the ground image and the aerial image can be obtained, and an air-ground joint scene graph is generated according to the matching relationship to complete matching of the air-ground images. According to the embodiment of the invention, the homonymous connection points between the air-ground images can be automatically generated, and automatic air-ground joint model construction is realized.

The invention discloses a multi-modal remote sensing image matching method, particularly relates to the technical field of remote sensing image processing, and is used for solving the problem of multi-modal image matching. The method mainly comprises the following steps of: 1, improving a phase consistency model, and constructing a phase-moment weighted joint direction feature in combination with a maximum moment and a minimum moment to replace the feature expression of the traditional image gradient; 2, implementing a point product fusion strategy on the phase-amplitude characteristics extracted by the phase consistency model and the maximum moment, and constructing phase-moment weighted joint amplitude characteristics; and 3, on the basis of the steps 1 and 2, identifying the direction of the feature points and screening local peak values to determine the main direction. Three values adjacent to a peak value are selected, and the peak value position is interpolated through parabola fitting so as to improve the matching precision; and step 4, constructing a logarithm polar coordinate descriptor based on regularization non-uniform partition to generate a feature description vector. Through the mode, high-precision and high-efficiency matching of the multi-mode remote sensing image can be realized.

Image fingerprints (embeddings) are generated by an image fingerprinting model and indexed with an approximate nearest neighbors (ANN) model trained to identify the most similar fingerprint based on a subject embedding. For image matching, a score is provided that indicates a similarity between the input embedding and the most similar identified embedding, which allows for matching even when an image has been distorted, rotated, cropped, or otherwise modified. For image classification, the embeddings in the index are clustered and the clusters are labeled. Users can provide just a few images to add to the index as a labeled cluster. The ANN model returns a score and label of the most similar identified embedding for labeling the subject image if the score exceeds a threshold. As improvements are made to the image fingerprinting model, a converter model is trained to convert the original embeddings to be compatible with the new embeddings.

An image classification model, e.g., a neural network model, may be trained on a set of training medical imaging exams each including a training report and a training medical image. A model generation module or device may, for each of the training medical imaging exams: use finding item criteria to reorganize text of the training report into a list of finding items, each associated with text extracted from the training report text, use natural language processing to analyze the resultant text associated with each finding item to determine an associated classification of each finding item, store, in a training dataset, the training medical imaging exam, the associated finding items, the matching text, and the classifications resulting from the analysis of the matching text. The trained neural network learns to output, for other medical imaging exams, one or more finding items and an associated classification data.

The invention discloses a global enhancement method for a wide-area astronomical image. The method comprises the following steps: carrying out high-frame-frequency image acquisition on a starry sky area; converting the reference star from a star catalogue position to an observation position, and resolving a linear negative film model; geometric distortion correction is carried out based on the resolving result; based on the image after geometric distortion correction, matching and identifying a reference star in a full view field, and obtaining celestial coordinate information of all fixed star images in the view field; on the basis of celestial coordinate information, correcting a poorer astronomical effect of the astronomical image, establishing an ideal coordinate system taking the center of a view field as a tangency point, and projecting a time sequence observation image to the ideal coordinate system; translating the processed graph, accumulating all translated images, and averaging all accumulated pixel positions to realize image superposition enhancement, thereby effectively improving the dynamic range of a detector and the detection capability of a wide-area astronomical observation system; and meanwhile, the signal-to-noise ratio of the astronomical image can be effectively improved, and the astronomical image centering and light measuring precision can be further improved.

The invention provides a cross-modal different-source image matching method based on unsupervised domain adaptation, and aims to solve the problem that an existing deep learning method depends on target domain annotation data in cross-modal remote sensing image matching. The conversion of a source domain image and a target domain image is realized through a style migration technology, and domain offset between the source domain image and the target domain image is reduced by using adversarial learning. According to the method, a source domain (with labels) and a target domain (without labels) are aligned from two angles of images and features, knowledge migration is realized, and a matching task in the target domain is solved. The technology fuses unsupervised domain adaptation, focuses on cross-modal different-source image matching, effectively reduces the demand of target domain annotation data, and is of great significance to practical application. Transition from labeled data to unlabeled data is promoted, high-precision target scene image matching is achieved, a new thought is provided for solving the data labeling problem, and the method is expected to be widely applied to various remote sensing image processing scenes.

The invention provides a method and device for matching an image with a text, and relates to the technical field of artificial intelligence, in particular to the technical field of computer vision, image processing, natural language processing, deep learning and large language models. According to one specific embodiment, the method comprises the steps that an image feature vector is extracted from an image, and the image feature vector comprises visual information and semantic information of the image; performing similarity matching on the image feature vector and a text semantic vector in a text semantic vector library to obtain a similarity matching result; and based on a similarity matching result, selecting a text matched with the image.

The invention provides a semi-supervised image classification method and system based on pseudo labels and embedded clustering matching, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a marked image and an unmarked image, and performing enhancement processing on the unmarked image; for the marked image, calculating supervision loss based on a semi-supervised image classification model; inputting the enhanced unmarked image into a semi-supervised image classification model, screening false labels based on a dynamic threshold value and a learning state of a category, and calculating unsupervised loss; respectively mapping the weakly enhanced image and the strongly enhanced image to an embedding space to generate K-Means clustering results which are respectively used for generating a target image and a strongly enhanced embedded clustering image, and calculating image matching loss; total loss is established based on supervised loss, unsupervised loss and graph matching loss, and the performance of the semi-supervised image classification model is improved by minimizing the total loss. Consistency regularization is applied to a single sample and a sample affinity level, so that confirmation deviation is effectively relieved.

The invention relates to the technical field of foundation pit detection, and discloses a foundation pit displacement monitoring method and system based on machine vision and monitoring equipment. The method comprises the steps that a high-definition camera collects image data of a foundation pit area in real time, the collected image data are preprocessed, reference points in the image data are analyzed through an optical flow method, displacement of the reference points in a foundation pit is estimated, and three-dimensional position changes of the reference points are calculated through the stereoscopic vision technology and image matching. The system comprises an image acquisition module, an image preprocessing module, a displacement calculation module, a filtering optimization module, an anomaly detection and early warning module and a data storage module. The equipment comprises a high-definition monitoring camera, a data processing terminal, a storage unit, an alarm device and a communication module. According to the invention, through image acquisition of a multi-camera system and a stereoscopic vision technology, comprehensive monitoring of the displacement of the foundation pit is ensured, high-quality image data can be provided in various environments, and precise acquisition of three-dimensional depth information is realized in combination with stereoscopic vision.

The invention discloses a non-vision field imaging detection method and system fused with an event camera, and the method comprises the following steps: enabling the event camera and a standard optical camera on a detection prototype to be aligned with a ground region of a corner, forming a video file based on a tiny pixel color difference amplification technology, and carrying out the calculation and analysis of the video file; processing the video file by using an image matching algorithm, and eliminating noise by using Kalman filtering; establishing a linear system, and then obtaining a reconstructed image by using a forward transmission light intensity function; performing Euler video amplification processing on the reconstructed image, wherein the Euler video amplification comprises motion estimation, spatial decomposition, time filtering and signal enhancement; detecting the detection area, and mapping the motion track of the target hidden behind the corner to a two-dimensional plane; the system comprises a simulation scene construction module, a three-dimensional point cloud set construction module and an information integration module. According to the invention, a micro motion algorithm and optical flow calculation are popularized to non-vision field imaging, so that the optical imaging technology can act on a vision field range and also can act on a non-vision field range.