88 results about "Homography" patented technology

The invention discloses an IMU (Inertial Measurement Unit)-assisted moving target detection method under a dynamic condition, which comprises the following steps of: firstly, extracting feature points of a target image for matching to obtain feature point pairs; then, relative coordinate conversion is carried out on IMU sensor data, a corresponding basic matrix or homography matrix is solved, and then corresponding polar lines or mapping projection feature points are obtained; classifying the extracted feature points into two parts, namely static background feature points and dynamic target feature points; an affine transformation matrix is solved according to the corresponding relation of the static background feature points of the two adjacent frames of images and the motion parameter model, an inter-frame difference method is used for carrying out difference on the two frames of images and carrying out image binarization and morphological processing, and a corresponding motion target frame is obtained; and finally, traversing the moving target frame to carry out overlap ratio matching, and screening out a detection object of which the overlap ratio is greater than a set threshold value, namely an actual moving target in the current image. The method provided by the invention can effectively improve the precision and accuracy of moving target identification in a dynamic background.

The invention discloses a building outer wall falling risk detection method and system based on deep learning, and the method comprises the steps: obtaining an original surface image, extracting vertical face geometric features, constructing a homography matrix based on vanishing points, carrying out the geometric correction, and reconstructing an orthographic projection image; performing enhancement processing on the orthographic projection image, extracting an effective detection area and intercepting a detection image; inputting the detection image into a deep learning segmentation model into which edge weight constraint is introduced, and performing semantic segmentation, binaryzation and optimization to obtain a falling region; a mapping relation is established according to the actual physical size of the building outer wall, and pixel information of the falling area is converted into multi-dimensional parameters such as the actual physical area, the elevation, the width and the height; and combining the defect intensity input quantification risk assessment model to calculate a comprehensive risk index, determining a risk level and outputting a result. Automatic identification and quantitative evaluation can be realized, distortion and background interference are eliminated, accurate conversion from pixels to a multi-dimensional physical space is realized, and a scientific basis is provided for safety investigation.

The invention discloses a method for optimizing micro-crack segmentation based on deep learning and super-resolution reconstruction, and the method comprises the steps: cutting an image in real time, obtaining an image block which takes a component as a target main body, and synchronously recording a homography matrix for geometric mapping; selecting an amplification strategy to improve the resolution, and recording a scale mapping relation; inputting the enhanced image block into a double-flow network; adaptive fusion and reconstruction are carried out on the two branch features, and a high-resolution texture image is output; generating a geometrically corrected ortho-image, fusing the geometrically corrected ortho-image with original illumination information, and outputting a corrected image with a known pixel size; identifying cracks, spalling and honeycomb diseases in parallel; generating a unified defect confidence map; calculating real geometric parameters of the BIM in a BIM global coordinate system through coordinate back projection; and generating quantitative defect reports and maintenance suggestions. The method has the advantage that seamless connection between the detection result and the BIM global coordinates is realized.

The invention relates to the technical field of industrial vision and intelligent monitoring, and discloses a loom control system operation state monitoring and fault diagnosis method based on machine vision, which comprises the following steps: acquiring a video stream in a loom shed area and constructing a two-dimensional space-time slice tensor; performing global motion compensation processing on the space-time slice tensor by using a homography transformation matrix, mapping a compensated dynamic texture feature sequence to a three-dimensional phase space by using a time delay embedding algorithm, and reconstructing a closed phase space trajectory representing periodic operation logic of the loom; the discrete Frechet distance between the phase space trajectory of the current operation cycle and the preset reference trajectory is calculated, and a control instruction is generated. The health degree of the sequential logic of the system is directly quantified on the premise that specific components are not recognized by using the invariant characteristic of the phase space manifold topology; the technical problems that small phase lag is difficult to perceive and nonlinear faults cannot be early warned in a strong noise environment are solved.

The invention discloses a method and system for constructing a homography estimation model based on cross Mangbar dynamic feature fusion optimization, and the method comprises the following steps: building an overall framework of a network which is composed of neural network layers and comprises an input module feature pyramid, a rough matching module and a fine matching module; setting a structure of a crossed Mangbar model; setting the number, the connection mode and the type of crossed Mangbar models in the rough matching module; training the network to obtain a homography estimation network and a Mangbar network for multi-modal image fusion, better exploring intra-modal and inter-modal features, and dynamically enhancing detail texture information of a source image and specific information of each modal; for a feature extraction and reconstruction part of the network, a Mangbar model suitable for a fusion task is designed by combining a visual state space model with dynamic convolution and channel attention, so that the performance and the global modeling capability of the Mangbar model are kept, the channel redundancy is reduced, and the local feature extraction capability is enhanced.

The invention provides a three-dimensional reconstruction method based on guided filtering and Mama geometric feature fusion in the technical field of three-dimensional reconstruction, and the method comprises the steps: S1, collecting a multi-view RGB image, carrying out the calibration of a camera, obtaining a projection matrix and an internal reference matrix, and obtaining a reference image; s2, extracting image features from each RGB image; s3, sampling on the reference image to obtain a depth hypothesis plane, performing homography transformation on each image feature, and aggregating with the depth hypothesis plane to obtain a cost body; s4, sequentially performing filtering, regularization and exponential normalization on the cost body to obtain a probability body, and constructing a depth map and a confidence map based on the probability body; s5, performing frequency domain filtering and up-sampling on the depth map and the confidence map, and then performing feature fusion to obtain a visual angle depth; and S6, executing three-dimensional reconstruction operation based on the visual angle depth, the projection matrix and the internal reference matrix. The method has the advantage that the precision and robustness of three-dimensional reconstruction are greatly improved.

The invention discloses a detection method, device and equipment for wafer edge polishing. The detection method comprises the following steps: positioning a wafer to be detected; obtaining plane part morphology data and curved surface morphology data of the positioned wafer to be detected; performing curve fitting on the plane part morphology data and the curved surface part morphology data to obtain a first curve and a second curve; performing feature extraction and feature point matching on the curve; calculating a homography transformation matrix between the two curves based on the matched feature point pairs; determining an overlapping area of the first curve and the second curve; determining an optimal stitching point; and butting the first curve and the second curve according to the optimal stitching point, and taking the butted curve as an edge detection curve of the wafer to be detected. Through the integrated design of the functions of centering positioning, height compensation, curve fitting, morphology observation and the like of the wafer, the data synchronism of multi-parameter measurement is ensured, and the accuracy and reliability of a measurement result are enhanced.

The invention discloses a cross-view multi-target collaborative perception and global tracking method. The method comprises the following steps: receiving original video streams of different views in parallel; carrying out feature extraction and content perception feature recombination processing; establishing a homography transformation matrix representing the mapping relation between the visual angles; performing forward geometric mapping by using the homography transformation matrix, and performing reverse mapping, secondary confirmation and state updating; a joint cost model based on space-time-appearance fusion is constructed, the potential association degree of unassociated residual targets is quantified, and missing targets are recalled; performing duplicate removal and optimization on the targets in the association set; and training and optimizing the network. According to the embodiment of the invention, the method achieves the capturing of a pixel-level target, and improves the texture and edge expression capability of a tiny target in a deep feature map. Efficient fusion of multi-dimensional spatio-temporal information among visual angles is realized; higher robustness and tracking continuity are achieved, and the integrity and fineness of a final trajectory result are improved; and the recall is ensured, and the generation of redundant tracks is avoided at the same time.

The invention discloses an image registration method based on lightweight model homography estimation, and relates to the technical field of image registration. Aiming at the defect of poor robustness in a low-texture and low-overlapping-rate scene in the traditional method, the adopted scheme comprises the following steps: designing a compression unit module, equally dividing an input feature map into a left channel C1 and a right channel C2 according to channel dimensions, performing grouping convolution and channel rearrangement on the C1, and performing compression on the left channel C1 and the right channel C2; sequentially executing 1 * 1 convolution, channel rearrangement, 3 * 3 depth separable convolution and 1 * 1 standard convolution on the C2; carrying out Concat operation on output results of the two channels; designing a lightweight regression network based on a compression unit module sUnit, wherein the lightweight regression network is used for calculating a normalized displacement vector of a target scene; designing a mixed loss function for the lightweight regression network; and obtaining a lightweight model based on the steps, and training, evaluating and outputting the lightweight model. The method is used for the real-time homography estimation task on the edge device.

The invention provides an image registration method and an image registration device, which are applied to the technical field of image processing. Comprising the following steps: acquiring a target reference image and a first target to-be-registered image of a to-be-detected area containing FOD; constructing an optimal affine transformation model according to the target reference image and the N preset pitch angles, and further performing linear transformation on the first target to-be-registered image to obtain a second target to-be-registered image; according to a Gaussian difference pyramid of a second target to-be-registered image, screening an extreme point of which the principal curvature does not exceed a preset principal curvature threshold as a first feature point, and further screening a first feature point of which the re-projection error and the sampling probability meet a preset condition as a second feature point; and solving a homogeneous linear equation set of the pixel point pairs matched with the second feature points by adopting an SVD method to obtain a transformation homography matrix, and then registering the first target to-be-registered image to obtain a target image. The contradictory closed loop of low efficiency-insufficient precision is broken through, and the aviation operation safety is guaranteed.

For a cross-modal target prompt matching task, the invention provides a training-free multi-stage matching method based on local feature points and global spatial transformation so as to realize robust positioning of the same target in different modal images. According to the method, firstly, feature points and descriptors of a target prompt image and a to-be-matched image are extracted by utilizing a pre-trained SuperPoint, then, MINIMAGlue finely adjusted on cross-modal data is preferentially adopted to perform feature point matching, and when matching fails, LightGlue is switched to serve as remediation; in the matching process, an optimal angle is selected through a rotation enumeration strategy to enhance matching robustness; in the coarse positioning stage, calculating a minimum enclosing rectangle according to matching points in the ROI, and if the matching points are insufficient, estimating a homography matrix by adopting RANSAC and carrying out geometric correction on a rectangular frame; in the fine positioning stage, the central point of the coarse positioning result is used as a prompt to input MobileSAM for target segmentation, and a final rectangular frame is corrected through area consistency constraint. Through integration and fusion of the multi-stage process, accurate matching and stable positioning of cross-modal target prompt are realized under a training-free condition, and the generalization ability and practicability of the method are improved.

The embodiment of the invention provides a matching point pair generation method and device. The method comprises the following steps: obtaining a first feature map and a second feature map of a first size and a third feature map and a fourth feature map of a second size for a first image and a second image of different angles of the same scene; determining a feature block pair corresponding to a matching feature point pair from a third feature map and a fourth feature map which are large in size by using the matching feature point pair corresponding to the first feature map and the second feature map which are small in size; based on the similarity between at least part of feature points in a first feature block and at least part of feature points in a second feature block in the feature block pair, updating a current homography matrix corresponding to the feature block pair to obtain a target homography matrix corresponding to the feature block pair; and determining a matching point pair between the first image and the second image based on the target homography matrix, thereby realizing determination of the matching point pair between the images.

The application provides a feature matching method, system and device based on multi-geometry collaborative learning and a storage medium, and relates to the technical field of robot positioning and navigation. The steps include: acquiring an image pair, extracting two groups of sparse local features in the image pair and inputting the local features into a multi-geometry collaborative network model; enhancing the local features through an attention mechanism and iteratively updating the sparse network through a multi-layer perception and / or linear projection; generating and screening reliable matching points through affine transformation estimation, position coding and key point neighborhood expansion; constraining the matching process through pose information and adaptively adjusting the matching strategy for matching prediction; combining real matching information and homography geometry information for loss calculation to train the multi-geometry network model; and repeating the steps until a preset iteration number is reached. Through collaborative optimization of affine geometry, epipolar geometry and homography geometry, the application realizes mutual promotion of local feature discriminability and geometric consistency, and can achieve a better balance between speed and accuracy in feature matching.

The invention relates to the technical field of defect detection, in particular to a railway contact line surface defect detection method and system. The method comprises the steps of obtaining a sequence image; calculating a space-time matching weight based on the tracking duration and the normal distance of the feature point pair, and obtaining a global homography matrix through a weighted random sampling consensus algorithm for preliminary correction; calculating a local affine transformation matrix and a distortion factor for the image segment, and splicing to generate a two-dimensional expansion graph; and projecting the candidate defect area segmented on the expanded image back to the original image, calculating a gradient direction entropy ratio, and judging a real defect based on a self-adaptive threshold determined by a distortion factor. According to the scheme, non-rigid deformation of the contact line can be compensated, geometric distortion and real defects can be distinguished in a self-adaptive mode, and the detection accuracy is improved.

The present invention discloses a method and device for detecting misaligned changes in remote sensing images. The method comprises the following steps: extracting feature points from a first remote sensing image and a second remote sensing image respectively to obtain a first feature point set and a second feature point set; matching corresponding feature points in the first feature point set and the second feature point set to obtain a plurality of matching point pairs; calculating a homography matrix based on the plurality of matching point pairs, and performing inverse transformation on the first remote sensing image and the second remote sensing image based on the homography matrix; performing change detection on the inversely transformed first remote sensing image and the second remote sensing image to obtain a changed image; the method and device for detecting misaligned changes in remote sensing images are described. The method comprises the following steps: screening feature points in a pre-remote sensing image and a post-remote sensing image, matching the feature points to obtain matching point pairs, confirming the spatial relationship of the images through the matching point pairs, and finally transforming the images according to the spatial relationship to realize the detection of the remote sensing images. The method can provide one-stop processing for the detection of misaligned remote sensing images, thereby improving the detection accuracy.

This application discloses a real-time image stitching method and apparatus. The method includes: receiving images uploaded to the cloud by a camera device in real time; extracting feature points from the images and matching feature points between the current frame and the previous frame; calculating the ratio of the homography matrix score to the sum of the homography matrix score and the fundamental matrix score to determine whether to use the homography matrix or the fundamental matrix to recover the image's pose parameters and map points, and initializing the map; estimating the pose of the current frame and optimizing the pose of the current frame using candidate frames; converting the coordinates of the map points to two-dimensional coordinates and performing plane fitting; converting the coordinates of the image corner points in the camera coordinate system to the object coordinate system, calculating perspective transformation parameters and performing geometric transformation on the image; and performing Gaussian pyramid fusion on the generated tiles. This method solves the problems of low efficiency in scene reconstruction using motion reconstruction algorithms and the inability to process real-time photogrammetric data.

The invention relates to the technical field of three-dimensional scanning, in particular to a point cloud enhancement method and device based on binocular reconstruction, equipment and a storage medium. The method comprises the following steps: acquiring sparse point cloud; projecting the sparse point cloud to a preset left pixel plane and a preset right pixel plane to obtain a left pixel coordinate and a right pixel coordinate; determining a bounding box area according to the distribution relation of the left pixel coordinates, and determining a matching point pair set about the left pixel coordinates and the corresponding right pixel coordinates by traversing pixel points in the bounding box area; calculating a local homography matrix and an average depth value according to the matching point pair set, generating a matching point of a right pixel coordinate by using the homography matrix, performing correction through epipolar constraint, and performing three-dimensional reconstruction on the corrected matching point pair to obtain a reconstruction point; and verifying the reconstruction points according to the average depth value, and adding the reconstruction points meeting verification conditions to the global point cloud. According to the method, part of discrete and sparse point clouds can be effectively enhanced.

The present invention discloses a camera target handover method, which is applied to a monitoring area including at least two cameras, and includes: generating a homography matrix between the two cameras according to the projective relationship between the field of view images in the two cameras; using the homography matrix to map the target objects captured by the two cameras to each other's camera, and matching the target objects in the two cameras with the mapped objects respectively to generate respective one-way matching matrices; merging the one-way matching matrices of the two cameras to obtain a two-way matching matrix, so as to obtain the corresponding relationship between the target objects in the two cameras according to the element values ​​in the two-way matching matrix; using the above-mentioned corresponding relationship to find the known tracking target in the original camera in the target camera for handover. The technical solution provided by the present invention improves the accuracy of target handover, is easy to implement, and has good performance when applied to multi-target handover scenarios.

The invention relates to the technical field of industrial automation, in particular to a strain gauge front and back face recognition and mounting method based on visual recognition, which comprises the steps of dual polarization and speckle collection, radiation correction and geometric correction, Stokes vector calculation and target grid generation, grid-level Mueller inversion and phase statistics. Constructing an object-level evidence and forming attitude initial estimation, executing two-out-of-three consistent judgment according to a threshold and re-checking by using a tail end polarization image, and performing circle statistical fusion on the hand attitude and the attitude initial estimation to obtain a final attitude; and projecting the mounting reference to a station plane through the homography matrix, synthesizing a mounting pose and issuing an instruction. According to the method, stable judgment is kept in light reflection, weak texture and batch change scenes, the error pasting rate is reduced, and process tracing and online calibration are supported.

The application discloses a noise elimination and transformation model robust estimation method in multi-modal image registration, comprising: obtaining initial matching point pairs, establishing a homography transformation model to be estimated and residual error measurement; performing vector field consistency calculation on the initial matching point pairs, and constructing an edge expansion consensus set; performing initial fitting on the homography transformation model on the edge expansion consensus set to obtain an initial homography transformation model; estimating noise scale and initializing residual error threshold based on residual error statistics induced by the initial homography transformation model; regarding the residual error threshold and the homography transformation model parameters as same-order decision quantities, constructing a joint optimization framework of threshold-model cooperation; adaptively constructing a joint search domain according to regularity statistics of residual error distribution; constructing a threshold-coupled cost function under a mixed statistical modeling framework of inliers / outliers; performing collaborative iterative updating on the homography transformation model and the residual error threshold by adopting a group optimization strategy, and outputting the homography transformation model and a refined inlier set.

The present invention is an automatic matching evaluation method, storage medium and computer device for advertising material and on-site photos based on SIFT. The method includes: extracting feature points from advertising material pictures and on-site pictures, quickly matching feature points, and eliminating feature points whose distance is greater than a preset distance range; if the number of feature point groups that meet the distance range reaches four or more, then calculating the homography matrix of the advertising material picture feature points mapped to the on-site picture feature points; mapping the vertex coordinates of the advertising material picture to the on-site picture to obtain a polygon A composed of the mapped vertex set, and the on-site picture vertex set composed of a polygon B, and calculating the intersection of the two polygons A and B as a polygon S on the on-site picture; judging whether the polygon S is a convex quadrilateral, and if so, calculating the automatic matching evaluation score of the on-site picture shooting quality through a preset evaluation model. The present invention effectively reduces the cost of manual review and solves the problem of different review standards for different personnel.

The embodiment of the invention provides a homography obtaining device and method and electronic equipment. The device comprises a segmentation device for segmenting an image shot by a camera; the extraction device is used for judging vanishing points in the image and obtaining line segments corresponding to different vanishing points, reserving the line segments distributed on a predetermined object according to a segmentation result of the image, and extracting rectangles in the image according to the line segments distributed on the predetermined object; the projection device is used for projecting vertexes of the rectangle to corresponding points in a layout map; and a calculation means for calculating homography on the basis of the coordinates of the vertexes of the rectangle and the coordinates of the corresponding points in the layout. According to the method, the homography calculation accuracy can be improved, and the workload is reduced.

The invention discloses an industrial scene anomaly detection method based on multi-dimensional feature decoupling and a double-track state machine, and the method comprises the steps: taking an industrial field monitoring video stream as data input, firstly carrying out the spatial correction of the video stream through a feature anchor point-based dynamic registration algorithm and a homography matrix, and eliminating background jitter; secondly, the correction frames are input into a heterogeneous channel in parallel, and an initial foreground is generated through adaptive Gaussian mixture and HSV combined filtering; thirdly, reconstructing a target by using morphological filtering, inputting a double-track time sequence module, and shunting the double-track time sequence module to an existing track or a persistent track for judgment; and finally, integrating a time sequence accumulation result of each track, and outputting an alarm signal containing an abnormal type, coordinates and confidence. The method is suitable for complex scenes with numerous interferences, effectively solves the problems of jitter false alarm and mechanical retention missing detection, and improves the detection accuracy.

To provide an acquisition device, a method and an electronic device for homography.SOLUTION: A device includes: a division part which divides an image photographed by a camera; an extraction part which determines a vanishing point of the image to acquire a segment corresponding to different vanishing points, holds segments distributed at a predetermined object based upon division results of the image, and extracts a rectangle of the image based upon the segments distributed at the predetermined object; a projection part which projects the vertices on corresponding points in a layout pattern; and a calculation part which calculates a homography based upon the coordinates of the vertices of the rectangle and coordinates of the corresponding points in the layout pattern. According to the present invention, accuracy of the calculation of the homography can be improved to reduce the workload.SELECTED DRAWING: Figure 11

The present invention relates to the technical field of image recognition, specifically a one-way blurred image registration method based on line features, comprising the following steps: confirming the clear direction of the one-way blurred image, extracting line features with an included angle less than 45° with the clear direction and matching them; confirming the linear equation coefficients of the matched line features and the lengths of the line features; constructing a matrix equation and introducing a line segment length weight coefficient; solving the coefficient X based on the least squares method in step S3 to obtain a homography matrix; obtaining the homography matrix H by solving the coefficient X according to the least squares method, and using the homography matrix for image registration. Since the one-way blurred image has higher clarity in the non-blurred direction, by extracting line features along the other clear direction, the extraction has higher accuracy. Compared with the method of calculating the intersection points of the straight lines where the two line segments are located after extracting the line segments and then using the intersection points to calculate the homography matrix, it has higher numerical stability and robustness.

This application relates to the field of image processing technology for fly traps, providing an adaptive stitching method and system for fly trap images. The method includes: extracting feature vectors from multiple images using the SIFT algorithm and a ResNet network respectively, and weighting and fusing the two features using adaptive weight coefficients 'a' generated by the ResNet network to obtain a fused feature vector; based on the fused feature vector, using an improved random sampling consensus algorithm to solve the homography matrix between the two images, performing non-uniform random sampling based on similarity during the sampling stage, and using the top K% of the most similar inliers to form a refined inlier set for calculation during the calculation stage; and completing image stitching based on the homography matrix. This application solves the technical problems of difficult feature matching, stitching misalignment, and obvious fusion traces caused by the small size, dense distribution, and high similarity of insect targets in fly trap images, thus improving the robustness, accuracy, and efficiency of image stitching.

The invention discloses a foundation pit supporting structure surface defect detection system and method based on a hierarchical graph model, and the method comprises the steps: firstly carrying out the construction and data collection of a detection system, then extracting a supporting structure surface region from an obtained original data image through a semantic segmentation model, and removing a non-target background; detecting a defect mask in the surface area of the supporting structure after the non-target background is removed by using an improved YOLOv11-seg instance segmentation model, and generating an independent mask; secondly, geometric index calculation of the area, the length and the width is carried out on each obtained defect mask, a pixel unit is converted into an actual physical unit in combination with a homography transformation matrix H, and it is ensured that a detection result can be directly used for structural safety evaluation; and then evaluation and VLM expert judgment are carried out, and finally a diagnosis report is generated and a result is visualized. According to the automatic crack analysis system, macroscopic evaluation and microscopic analysis can be integrated, computing resources can be intelligently managed, and environmental noise can be effectively resisted.

The invention discloses a splicing method and device of a large-size high-resolution image. The method comprises the following steps: cutting an image pair overlapping region; inputting the cut image pair into a convolutional network for feature extraction; performing network prediction to generate a mean value and a variance; sorting the variances, and selecting the first k matched point pair data with the lowest variance as an output result of the network; reading the coordinate information to update the point pair data information, and generating a real point pair corresponding relation of the large-size image; generating a homography matrix by using point pair information, and acting on the image to complete final splicing; and repeating the steps from left to right and from top to bottom, when the upper and lower parts and the left and right parts are overlapped at the same time, respectively cutting the input network twice to output two results, and splicing the two results to complete the splicing of the whole image. According to the method, a dense correspondence algorithm between high-resolution images is established, the feature information corresponding precision level during image registration is improved, and the problem of low image splicing precision of large-size images is solved.

Example implementations include a method, apparatus and computer-readable medium for estimating a real world height in an image, comprising obtaining a first image and a second image from a camera installed at an unknown height in an environment, wherein the first image includes a first object of known height at a first location and the second image includes the first object at a second location; determining, based on the known object height, a first vector that intersects with a point on the first object in the first image and points to a real world ground plane; determining, based on the known object height, a second vector that intersects with the point on the first object in the second image and points to the real world ground plane; and determining a height of the center of a camera lens of the camera using an intersection of the first and second vector.

The invention provides a multi-view target tracking method which can be applied to the technical field of artificial intelligence. The multi-view target tracking method comprises the following steps: extracting feature points from a synchronous image, and obtaining a time sequence homography matrix of the synchronous image according to the feature points; matching the reference points of the multiple targets of the at least two view angles to obtain matching point pairs; obtaining a cross-view homography matrix according to the matching point pairs; further obtaining a comprehensive transformation matrix of the plurality of targets between at least two visual angles; establishing tracks of the multiple targets according to the reference points of the multiple targets at different time; projecting the tracks of the at least two visual angles into one visual angle by using the comprehensive homography matrix; and matching the tracks and the projections of the plurality of targets according to the similarity of the tracks and the projections of the plurality of targets so as to perform target tracking. The invention also provides a multi-view target tracking device, equipment, a storage medium and a program product. According to the embodiment of the invention, the robustness of target association in a complex scene can be improved.