773 results about "Panorama" patented technology

The invention discloses a cross-source data three-dimensional reconstruction method and system based on improved Gaussian sputtering, and the method comprises the steps: collecting an unmanned plane inclined image and a ground panoramic image of a target region, and constructing a time-space correlation data set; based on multi-view geometric constraints, space-time coding matching point pairs are established through an adaptive feature pyramid, intelligent incremental cross-source data sparse reconstruction is carried out, and point cloud and camera parameters are output; adopting improved Gaussian sputtering, compressing a three-dimensional Gaussian kernel into a two-dimensional Gaussian primitive through double tangent vector constraint, and fitting surface geometry to realize multi-scale reconstruction; and optimizing primitive parameters by using a differentiatable renderer, completing multi-scale fine reconstruction through gradient back propagation, and generating a high-precision three-dimensional model. According to the method, multi-scale accurate geometric prior input and accurate camera poses are provided for three-dimensional reconstruction, the dependence on professional manual operation in a traditional three-dimensional reconstruction method is greatly reduced, and meanwhile, the geometric accuracy and visual fidelity of a reconstruction result are remarkably improved.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention relates to the technical field of image compensation, in particular to a reflection compensation method based on image processing, and provides the following scheme: acquiring an environment panoramic image by using a panoramic camera to establish a scene global coordinate system, and acquiring a multi-view original image in combination with a camera array arranged in the circumferential direction of an object; determining a rotation symmetry axis according to the multi-view contour features, reconstructing a three-dimensional geometric body, and calculating normal distribution and curvature change; generating a prediction image based on diffuse reflection and specular reflection hypothesis in the surface expansion domain through virtual visual angle disturbance, identifying a reflection region according to color and gradient consistency, and generating a reflection mask; and performing texture reconstruction on the reflective area by using geometric registration and multi-view compensation, and finally performing splicing and fusion to obtain a non-reflective high-fidelity panoramic image. The method does not need to change the field illumination condition, and can achieve the precise recognition and compensation of the complex curved surface reflection in the cultural relic in-situ collection environment.

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a computer vision-based hundred-million-pixel panoramic situation awareness method and system, and the method comprises the steps: synchronously collecting original local field-of-view images through multiple cameras; independently executing target detection and semantic segmentation in an original image domain; constructing local-to-global coordinate mapping based on the calibration parameters; projecting the target features to a global coordinate system and detecting a cross-view-field target; extracting multi-view-field texture, edge and color features, and performing weighted fusion according to a visible area; according to the method, through identification preposition and semantic level feature fusion, geometric splicing distortion is avoided, the cross-boundary identification precision is improved, the calculation load is reduced, target continuous tracking and situation real-time output in a high-density scene are realized, and the method has high engineering suitability and industrial popularization value.

The invention discloses a shielding complementing method based on a continuous streetscape panoramic image, and aims to solve the problem of information loss caused by shielding of dynamic objects (such as vehicles and pedestrians) in the existing streetscape image, and the method comprises the following steps: S1, recognizing and positioning a dynamic shielding object area in a continuous streetscape panoramic image sequence; s2, providing a continuous panoramic image-oriented feature extraction and matching algorithm (FDMPano), realizing robust feature retrieval and matching in a cross-view angle, and accurately positioning a reference image region which can be complemented; and S3, carrying out feature alignment and content mapping on the sheltered area based on a matching result, and generating an unsheltered panoramic image with consistent vision. According to the method, the internal relevance of continuous streetscape data is fully utilized, the occlusion area can be automatically completed with high quality, the integrity and availability of streetscape images are remarkably improved, and the method has wide application value in the fields of automatic driving, digital cities, virtual reality and the like.

A camera system has at least a first and a second camera each with an image sensor. Active areas of the image sensors of the first and second camera are determined and define an extended image space of a real-time panoramic video. A bounding box captures a fixed position in space or an object is set. The bounding box moves through extended image space. Image data determined only by the bounding box is harvested from camera image sensors and displayed within a window on a screen in real-time. Scan-line control of images sensors based on the bounding box is updated in real-time to form an e-gimbal. Steps of the e-gimbal are performed by a machine learning inference phase on a processor.

The invention discloses an iron tower operation safety early warning system and method, and relates to the technical field related to intelligent safety early warning, and the system comprises a data obtaining unit which is used for obtaining Beidou three-dimensional coordinate data and an operation site panoramic image in real time; the operation scene model establishing unit is used for establishing a dynamic three-dimensional operation scene model; the prediction model building unit is used for tracking the motion trail of each operation target based on the coordinate data and building a prediction model fusing kinematics rules and machine learning; and the risk level judgment unit is used for carrying out risk level judgment according to the spatial position and the attribute characteristics of the intersection area and generating operation early warning information. The technical problems of insufficient safety early warning accuracy and timeliness caused by insufficient high-altitude operation target dynamic track monitoring precision and risk early warning lagging in the prior art are solved, and the purposes of accurately identifying the movement track intersection risk in real time, dynamically evaluating the safety level and improving the safety early warning efficiency are achieved. And the early warning accuracy and timeliness of the operation safety of the iron tower are improved.

The invention discloses an automatic cattle checking method and system based on two-dimensional panoramic vision, and belongs to the technical field of computer vision and intelligent breeding, and the method comprises the following steps: reasonably arranging a plurality of fixed cameras in a cattle farm area, obtaining the internal reference and distortion coefficient of each camera through employing a camera calibration technology, and obtaining the internal reference and distortion coefficient of each camera; performing real-time distortion correction on the collected video frames; on the basis of side-by-side splicing or homography matrix perspective transformation, all paths of corrected images are fused into a complete two-dimensional panoramic image, and a view blind area of a single camera is eliminated; calling a lightweight target detection model on the panorama, and extracting bounding boxes and confidence coefficients of all cattle at one time; sorting according to the confidence from high to low, performing IOU non-maximum suppression de-duplication on the detection frame, and ensuring that the same cattle only counts once in an overlapping region; and the whole process is completed through the edge computing node. According to the invention, efficient, real-time and accurate on-site cattle checking of a large-scale cattle farm can be realized.

The invention belongs to the technical field of visual angle follow-up vehicle panoramic look-around, and particularly relates to a visual angle follow-up vehicle panoramic look-around system based on dynamic prediction, which is characterized in that monocular vision and laser radar time sequence data are fused, and a dense and stable dynamic target depth sequence is generated through dynamic target initial perception, cross-modal time sequence alignment and inter-frame depth correlation modeling; through environment interaction modeling and target behavior intention prediction double-branch parallel processing, a dynamic self-adaptive accurate track is output in combination with a weighted fusion strategy, and the prediction weight dynamic adjustment requirement of a complex motion scene is adapted; after a virtual view angle image is preliminarily synthesized based on the predicted trajectory, deviation detection and coordinate dynamic correction are realized through inter-frame target consistency verification, and artifact area positioning and pixel-level restoration are synchronously completed; multi-modal preprocessing and algorithm tasks are deployed according to hardware, and a model lightweight optimization and inter-frame prediction result multiplexing strategy is combined, so that low-delay operation of the system in a high-resolution panoramic image output scene is ensured.

The invention discloses a road culvert inner wall damage image detection and traffic capacity evaluation method and system, and relates to the technical field of road engineering detection and computer vision, and the method comprises the steps: collecting multi-source data through an annular camera, a laser section scanner and a mileage encoder carried by a detection robot, and carrying out the panoramic expansion and time-space registration; using a multi-scale attention panoramic segmentation network to identify damages such as lining cracks, joint slab staggering, damage and chipping, steel bar exposure and water leakage; calculating the clearance size and the deposition thickness of the culvert by adopting an ellipse fitting method; according to the method, the culvert detection speed reaches 50 m / h, and the damage detection accuracy reaches 89% or above.

The invention discloses a target coordinate positioning system and method based on a multi-view panorama camera, and relates to the technical field of target coordinate positioning, and the method comprises the steps: evaluating the spatial geometrical relationship between different camera units; performing target object detection on the image to obtain an undetermined target object in the image, and screening the undetermined target object in the image in combination with a historical image; evaluating the comprehensive cost between the target object of the image and candidate matching objects in other images, constructing a target cost matrix between the image and other images, and solving the target cost matrix; and according to the target object matching data among the images, performing image feature fusion on the images to obtain a fused image, obtaining and collecting the three-dimensional coordinates of the target object in the fused image to obtain target coordinate data in the fused image, so that more target objects can be detected, and the detection accuracy is improved. And accurate positioning of the target object can be realized.

The invention relates to the technical field of image processing, in particular to a panoramic image splicing method for a curtain wall building facade. The method comprises the following steps: performing three-dimensional scanning on a curtain wall building facade to obtain entity point cloud data of the facade, and selecting a plurality of discrete points from the entity point cloud data as reference anchor points; shooting a plurality of local images of the curtain wall building facade, and in the shooting process of each local image, recording the spatial correspondence between the image viewing range and the reference anchor point; and according to the spatial correspondence, aligning the image area corresponding to the reference anchor point in each local image to the position of the reference anchor point in the entity point cloud data to form a preliminary spliced image. According to the invention, through an image processing technology, zero-offset binding of the panorama and the BIM is completed through one-time shooting, so that the millimeter-level positioning precision and the inspection efficiency of the curtain wall facade are improved.

The invention relates to the technical field of three-dimensional Gaussian scene watermarking, in particular to a panoramic watermark processing method and device for a three-dimensional Gaussian scene, equipment and a medium. Determining the parameter sensitivity of each first Gaussian unit; screening a second Gaussian unit of which the corresponding parameter sensitivity is greater than a preset parameter sensitivity threshold from all the first Gaussian units; and embedding the copyright message in the second Gaussian unit in the three-dimensional Gaussian scene, thereby completing the embedding operation of the panoramic watermark. According to the embodiment of the invention, the parameter sensitivity of the first Gaussian unit is estimated through the two-dimensional original image and the first rendered image of different visual angles, so that the geometric structure characteristics of the three-dimensional scene are considered when the watermark is embedded, and the watermark information distortion is avoided.

The invention discloses an indoor and outdoor fine three-dimensional modeling method based on multi-modal data fusion. The method comprises the steps that unmanned aerial vehicle close-range photography, airborne laser scanning and ground supplementary photography are carried out on a building in a low-altitude area, and a first outdoor photo data set, a first laser point cloud data set and a POS data set of the building are obtained; continuously collecting at an indoor entrance through a laser SLAM mobile scanning system and a panorama camera according to a pre-planned route to obtain a second indoor photo data set and a second laser point cloud data set of the building; aerial triangulation adjustment calculation of a multi-source data set is carried out under the assistance of a POS data set, and an indoor and outdoor integrated fine surface three-dimensional model is automatically constructed. According to the method, the indoor and outdoor integrated surface three-dimensional model of the large building can be duplicated, the element cosmic space base scene is realized, and data application is provided for the industries of element cosmic scenes, indoor navigation, surveying and mapping and the like.

The invention is suitable for the technical field of automobile auxiliary driving, and provides a quick panoramic image splicing method based on a star flash wireless communication technology, which comprises the following steps of: S1, calibrating internal parameters and distortion coefficients of a camera by adopting a Zhang Zhengyou calibration method, calibrating external parameters by combining perspective transformation, and mapping each view angle image to a unified overlook coordinate system; s2, acquiring an image through a camera supporting a star flash protocol, and transmitting the image to a vehicle body domain controller by using a star flash technology; and S3, after distortion removal and perspective transformation are performed on the received image, a local pyramid fusion strategy is adopted: binary mask marking and multi-band fusion are performed on an overlapping region, original data are reserved in a non-overlapping region, and a panoramic image is finally output. Through combination of high-precision calibration, local pyramid fusion and a star flash communication technology, high-quality and low-delay splicing of vehicle-mounted panoramic images is realized, and the stability and practicability of the system in a complex environment are improved.

The invention discloses a camera-based human body three-dimensional reconstruction method and system, and the method comprises the steps: collecting the multi-source data of an operation scene, generating a time-space correlation data set, carrying out the processing, and outputting a spliced panoramic image and six virtual visual angle plane images; processing the six virtual view angle plane images, the space-time association data set and internal and external parameters of the double-fisheye camera, and outputting an optimal view angle combination and an optimal view angle image corresponding to the optimal view angle combination; and finally, a continuous and complete human body three-dimensional point cloud model is generated and processed, a cooperative control instruction is output, and the point cloud is continuously updated. The method has the advantages of data collaborative elimination of blind areas, accurate reconstruction and good control effect.

The invention discloses a method for realizing intelligent sag measurement of a power transmission line by using a binocular depth camera, which comprises the following steps of: firstly, fixing the binocular camera on a rotary holder at the central position of two towers to complete camera calibration and epipolar correction; then, controlling the holder to rotate according to a fixed stepping angle, collecting a binocular image sequence covering the whole section of lead, and preprocessing the binocular image sequence; performing feature matching and image splicing by using an SIFT algorithm based on the left eye image sequence to generate a panorama and establish a panorama-local mapping relationship; thirdly, detecting an insulator and a spacer target in the panorama based on a Mask-RCNN model, accurately positioning key point coordinates of the insulator and the spacer target, and mapping the key point coordinates to an original local image; in the corresponding left and right local images, a semi-global matching (SGM) algorithm is adopted to calculate parallax and generate depth information, and then three-dimensional coordinates of a target point are obtained through conversion; and finally, performing catenary fitting according to the ordered three-dimensional point set, introducing a temperature and windage yaw dynamic correction model, and accurately calculating a sag value. According to the invention, non-contact, automatic and high-precision sag measurement is realized, the method is suitable for routing inspection of complex terrains and large-span power transmission lines, and the operation safety and efficiency are remarkably improved. The invention also provides a system, equipment and a medium for realizing the method.

The invention provides a panoramic image generation method and device, equipment and a storage medium, and the method comprises the steps: carrying out the single-lens calibration of a wide-angle camera according to a checkerboard calibration method, and obtaining a distortion correction matrix; controlling the wide-angle camera and the array camera to perform time-space synchronous acquisition to obtain an initial panoramic image and a sub-image set; performing nonlinear distortion correction on the initial panoramic image according to the distortion correction matrix to generate a distortionless standardized panoramic image; acquiring sub-image feature information of each sub-image in the sub-image set, and acquiring a projection position, a perspective transformation matrix and a scale transformation matrix corresponding to each sub-image in the standardized panoramic image according to the sub-image feature information; and establishing a panoramic canvas coordinate system according to the standardized panoramic image, projecting each sub-image into the panoramic canvas coordinate system according to the corresponding perspective transformation matrix and the projection position, adjusting the size corresponding to the sub-image in the panoramic canvas coordinate system according to the scale transformation matrix, and generating a panoramic image according to the adjusted panoramic canvas coordinate system.

The invention discloses a panoramic view generation method, a medium, equipment and a vehicle. The panoramic view generation method comprises the following steps: acquiring image data of a camera around a vehicle and point cloud data of a laser radar; extracting ground points from the point cloud data, and projecting the ground points to a preset aerial view grid map to construct a three-dimensional grid map; projecting each grid in the three-dimensional grid map into a coordinate system of each camera to obtain a color value corresponding to each grid in the image data of the camera so as to obtain a panoramic view of each camera; and according to a preset camera priority, fusing the panoramic all-around views corresponding to the cameras to generate a final panoramic all-around view.

The invention discloses a panoramic image determination method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a current scene image, shot by at least one camera device, of an environment to which a vehicle belongs, and kinematics data and attitude data of the vehicle from a current shooting moment to a previous shooting moment; determining a scene image transformation matrix based on the kinematics data and the attitude data; acquiring a historical scene image shot by the camera device at a previous shooting moment, and performing stereo matching on the current scene image and the historical scene image based on the scene image transformation matrix and shooting parameters of the camera device to obtain a scene stereo image corresponding to the camera device; and determining and displaying a panoramic image based on the scene stereo image of each camera device. The authenticity, accuracy and reliability of panoramic image determination are improved, and the driving safety is improved.

The invention relates to the technical field of image geographic positioning, in particular to a progressive cross-view-angle image geographic positioning method based on spatial feature aggregation and position awareness, which comprises the following steps: acquiring paired satellite images and ground panoramic images as sample images, and constructing a training sample set; a cross-view image geographic positioning model composed of a double-branch backbone network and a fine-grained prediction network is constructed, two branches of the double-branch backbone network are a satellite processing branch and a ground panorama processing branch, and the fine-grained prediction network is composed of a bird's-eye view image transformation module and a position sensing prediction module; inputting training samples in the training sample set into the cross-view image geographic positioning model, and performing iterative training on the cross-view image geographic positioning model until convergence; and inputting a to-be-positioned paired satellite image and ground panoramic image into the trained cross-view-angle image geographic positioning model to obtain a positioning result output by the trained cross-view-angle image geographic positioning model.

This invention provides a method for 3D reconstruction of panoramic images based on 3DGS, which relates to the fields of computer vision and image processing. The method comprises: Step 1: Acquiring a sequence of panoramic images of the scene to be reconstructed and uniformly encoding them via equirectangular projection to obtain a coded dataset; Step 2: Performing panoramic structure-from-motion processing on the coded dataset to generate six-degree-of-freedom camera pose parameters for each image and a corresponding sparse 3D point cloud. This method achieves high-precision and high-efficiency 3D reconstruction of panoramic scenes, capable of outputting high-quality panoramic images or dense 3D models, improving both the quality and efficiency of 3D reconstruction.

The invention relates to the technical field of panoramic images, and discloses a self-correcting panoramic image splicing method for a vehicle around view system, which comprises the following steps: acquiring dynamic data of a vehicle to obtain an environment characteristic dynamic state, establishing a space-time incidence matrix according to the environment characteristic dynamic state, adjusting a fuzzy kernel estimation parameter, and performing accurate estimation and multi-frame super-resolution reconstruction of motion blur; fusing dynamic data, defining a vehicle attitude feature matrix, dynamically correcting vehicle attitude and camera external parameters, unifying cross-camera colors, performing ground constraint, constructing a multi-objective optimization framework, and balancing splicing quality and real-time performance; through monocular depth estimation and geometric constraint, the perspective difference of a close-range object is optimized, and through an edge retention loss function, the depth precision at the boundary of the object is enhanced; monocular depth estimation is optimized through binocular parallax constraint; constructing a multi-scale feature fusion network, and recovering image details; a factor graph model is constructed to integrate historical data, and accumulative errors of long-time-sequence splicing are eliminated.

The invention discloses a target ground object AI identification method based on a multi-source aerial photo, and relates to the technical field of aerial photo processing, and the method comprises the following steps: S1, collecting a panoramic aerial photo of a to-be-identified region; s2, extracting a first data set by using the gray-level co-occurrence matrix of the panoramic aerial photo; s3, extracting a second data set according to response values of all angular points in the panoramic aerial photo; s4, inputting the first data set and the second data set into a preliminary screening condition model, and taking pixel points meeting the preliminary screening condition model as suspected target ground objects of the to-be-identified region; and S5, processing the suspected target ground feature of the to-be-identified area to obtain an accurate target ground feature. In order to overcome the limitation of a single feature, multiple information sources, such as texture features extracted by a gray-level co-occurrence matrix and structural features extracted by corner detection, are fused, the features of image control points in aerial photography can be described more comprehensively, and the accuracy and robustness of recognition are improved.

An environmental capture system (ECS) captures image data and depth information in a 360-degree scene. The captured image data and depth information can be used to generate a 360-degree scene. The ECS comprises a frame, a drive train mounted to the frame, and an image capture device coupled to the drive train to capture, while pointed in a first direction, a plurality of images at different exposures in a first field of view (FOV) of the 360-degree scene. The ECS further comprises a depth information capture device coupled to the drive train. The depth information capture device and the image capture device are rotated by the drive train about a first, substantially vertical, axis from the first direction to a second direction. The depth information capture device, while being rotated from the first direction to the second direction, captures depth information for a first portion of the 360-degree scene. The image capture device captures, while pointed in the second direction, a plurality of images at different exposures in a second FOV that overlaps the first FOV of the 360-degree scene. The depth information capture device and the image capture device are rotated by the drive train about the first axis from the second direction to a third direction. The depth information capture device, while being rotated from the second direction to the third direction, captures depth information for a second portion of the 360-degree scene. The image capture device, while pointed in the third direction, captures a plurality of images at different exposures in a third FOV that overlaps the second FOV of the 360-degree scene.

A panoramic stitching method, a panoramic stitching apparatus, an electronic device, and a storage medium are provided. The panoramic stitching method comprises acquiring captured images from cameras in a same scene; obtaining a remapping lookup table and a fusion lookup table generated based on a stitching model; and mapping and fusing the captured images based on the remapping lookup table and the fusion lookup table to obtain a panoramic stitched output image. The stitching model comprises a hemisphere and a cylinder, and an origin of a virtual camera coordinate system associated with the cameras is located at a center of a bottom surface of the hemisphere. The bottom surface is where the hemisphere is in contact with the cylinder. This method effectively reduces camera parallax and alignment errors, significantly enhancing the quality of panoramic stitching, which is particularly well-suited for open horizontal scenes, delivering exceptional visual stitching results.

The application discloses a target coordinate positioning system and method based on a multi-view panoramic camera, relates to the technical field of target coordinate positioning, and comprises the following steps: evaluating the spatial geometric relationship between different camera units; performing target object detection on images to obtain to-be-determined target objects in the images, and screening the to-be-determined target objects in the images in combination with historical images; evaluating the comprehensive cost between the target objects in the images and candidate matching objects in other images, constructing a target cost matrix between the images and the other images and solving the target cost matrix; performing image feature fusion on the images according to the target object matching data between the images, obtaining a fused image, obtaining the three-dimensional coordinates of the target objects in the fused image and collecting the three-dimensional coordinates to obtain target coordinate data in the fused image, so that more target objects can be detected, and accurate positioning of the target objects can be realized.

A camera system has at least a first and a second camera which are attached to a common platform, each camera having a lens and an image sensor. Active areas of the image sensors of the first and second camera are determined. Image data only gathered from active areas of image sensors establish a panoramic image, which may be called an extended image space. Position sensors allow a processor to determine a location and a pose of the camera system. Coordinates of the extended image space are associated with a position of an object in physical space relative to a pointing direction of the camera system. A screen displays image data of a window of preset size around the image of the object in extended image space. A neural network is used to train the camera system to predict a position of an object based on camera rotations.

The invention provides a multi-view panoramic image indoor layout prediction method fusing semantics and planarity. The method comprises the following steps: step 1, carrying out three-dimensional semantic segmentation combined with three-dimensional point cloud reconstruction and a multi-view geometry and two-dimensional segmentation large model; step 2, obtaining a pure structured point cloud; step 3, multi-plane detection and classification; 4, extracting an initial contour; 5, generating a wall line based on plane fitting; step 6, performing multi-label graph segmentation guided by the two-dimensional contour and the wall surface; step 7, extracting a non-Manhattan ceiling line; and step 8, stretching the two-dimensional wall line along the vertical direction on the basis of the contour of the dimensional plane graph in combination with the vertical height of the room, generating an intersection line of the wall surface and the ceiling and an intersection line of the wall surface and the ground, and finally outputting a complete indoor three-dimensional vectorization layout model. According to the method, the layout integrity of the complex scene is improved, the boundary recovery precision of the occlusion area is improved compared with that of a traditional projection method, and the detection accuracy is improved by matching with multi-model RANSAC plane detection.

The invention discloses a panoramic image splicing method, system and device, and relates to the technical field of image processing, and the method comprises the steps: collecting images around a vehicle at different visual angles; using the MASK masks to generate overlapping region masks of the different view angle images, and performing local feature detection on the overlapping region masks to generate a transformation matrix; aligning the images of different visual angles according to the transformation matrix; a Canny edge detection operator is fused in splicing seam energy detection, the energy weight of the image edge area of the aligned image is enhanced, and a splicing seam is guided to actively avoid an object with remarkable structural features; b spline interpolation is introduced to optimize an initial splicing seam path; constructing a Laplacian pyramid along Gaussian blur of a splicing seam, and sequentially carrying out gradual fusion to generate a seamless panoramic image; according to the method, the basic function of eliminating the visual blind area is realized, a clear and visual panoramic view can be provided for a driver, and the driving safety is effectively improved.