135 results about "Vanishing point" patented technology

The invention relates to the technical field of computer vision and virtual reality, and discloses a three-dimensional video fusion method based on camera self-calibration and projection texture mapping, and the method comprises the following steps: S1, obtaining video data, and collecting at least one frame of two-dimensional image in a to-be-fused video stream; optionally, the two-dimensional image is preprocessed; and S2, calibrating internal reference of the camera, detecting linear features in the two-dimensional image by using an image processing algorithm, estimating the position of a vanishing point by using a least square method or other optimization algorithms based on the detected linear segment, and calculating an internal reference matrix of the virtual camera according to the optimized vanishing point position. The perspective relation between the video image and the surface of the three-dimensional model is determined through the vanishing point detection technology, accurate fusion of the video image and the three-dimensional model is achieved, the sense of reality of a virtual scene is improved, and the tedious camera calibration process and the complex three-dimensional reconstruction process based on a calibration plate are avoided.

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 an unmanned aerial vehicle pose determination method and device, electronic equipment and a storage medium, and belongs to the field of unmanned aerial vehicle pose determination, and the method comprises the steps: synchronously obtaining a visible light image, a depth image and inertial data of an unmanned aerial vehicle, and extracting a vanishing point set from the visible light image, extracting a plane normal vector set and a feature point set from the depth image; based on geometric constraint conditions that the vanishing point direction is aligned with a preset orthogonal direction and the plane normal vector is perpendicular to the orthogonal direction, constructing a rotation matrix solving model, and optimizing a rotation matrix by taking minimization of a three-dimensional map point projection error as a target; and meanwhile, combining feature point depth information and an inertia pre-integration error, constructing a translation vector solution model for joint optimization, and finally fusing a rotation matrix and a translation vector to output the pose of the unmanned aerial vehicle. Therefore, by implementing the method, the problem of pose drift caused by lack of geometric constraints in the prior art can be solved.

A method includes obtaining, using at least one processing device, image data associated with a scene. The method also includes identifying, using the at least one processing device, multiple line segments based on the image data. The method further includes identifying, using the at least one processing device, one or more boundaries around one or more objects detected in the image data. In addition, the method includes estimating, using the at least one processing device, a position of a vanishing point associated with the image data based on multiple collections of the line segments while excluding, from the multiple collections, one or more of the line segments that overlap with or that are included within the one or more boundaries.

A method includes obtaining, using at least one processing device, a vanishing point and a boundary based on image data associated with a scene, where the boundary is associated with a detected object within the scene. The method also includes repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple patches within the boundary and (ii) determining a similarity of the image data contained within the multiple patches. The method further includes identifying, using the at least one processing device, a modification to be applied to the boundary based on the identified patches and the determined similarities. In addition, the method includes generating, using the at least one processing device, a refined boundary based on the modification, where the refined boundary identifies a specified portion of the detected object.

A display video correction device includes: a feature extractor that extracts a feature from a camera image obtained from an in-vehicle camera; a Hough transformer that performs Hough transform on the feature extracted; a straight line detector that detects a plurality of straight lines in the camera image, based on a transform result of the Hough transform performed; a vanishing point calculator that calculates, based on the plurality of straight lines detected, first coordinates indicating coordinates of a vanishing point in the camera image; a difference calculator that calculates a difference between the first coordinates calculated and predetermined second coordinates; and a position corrector that corrects, based on the difference calculated, a position of a display area in the camera image to be displayed on a display device.

To provide an image recognition device and image recognition method which confirm whether a vanishing point exists at a correct position and detect an installation abnormality of an on-vehicle camera even when a white line cannot be detected during vehicle travel.SOLUTION: An image recognition device 10 comprises: an image acquisition unit 11 that acquires an image of a scene ahead of a vehicle imaged by an on-vehicle camera 5; and a vanishing point calculation unit (second vanishing point calculation unit 14) that sets a predetermined region in the image as a detection frame and calculates a vanishing point on the basis of movements of feature points within the detection frame. The vanishing point calculation unit sets the size of the detection frame on the basis of at least a traveling speed of the vehicle and a time interval for capturing the image, and calculates the vanishing point on the basis of a position of a feature point detected within the detection frame in a first image captured at a first time and a position of the feature point detected within the detection frame in a second image captured at a second time after the time interval has elapsed from the first time.SELECTED DRAWING: Figure 1

The invention relates to the technical field of image recognition, in particular to an unmanned aerial vehicle relative pose resolving method, which comprises the following steps of: constructing a mapping relation from a world coordinate system to an image pixel coordinate system based on an imaging model and internal and external parameters of a camera; the attitude angles, including a yaw angle, a pitch angle and a roll angle, of the unmanned aerial vehicle relative to the airport runway are estimated by using the pixel coordinates of the left and right sidelines and the vanishing points of the runway in the image in combination with the airport runway structure model; based on the attitude angle estimation result and the camera projection geometrical relationship, establishing an equation set, and solving the vertical height and lateral deviation position parameters of the unmanned aerial vehicle relative to the runway by combining a sideline pixel linear equation in the image and a three-dimensional linear equation of the runway boundary. According to the method, a unified mapping model from a world coordinate system to an image pixel coordinate system is established, an obtained attitude angle is combined with a linear relation between images of left and right side lines of a runway and the world, a linear equation set is constructed, and the vertical height and the lateral deviation distance of the unmanned aerial vehicle are solved.

The invention relates to a visual inertial positioning method and device for a railway train fused with scene information, and the method comprises the steps: building four geometric constraint models through combining a visual inertial odometer with the geometric features of a railway environment; the constraint models are respectively a sleeper advancing distance constraint model, a camera-rail surface absolute height constraint model, a rail vanishing point constraint model and a non-integrity constraint model. And the scene prior information is deeply fused with the visual inertial odometer. In a sliding window optimization framework, an IMU pre-integration residual error, a visual reprojection residual error and a geometric constraint residual error are jointly optimized through a dynamic marginalization strategy, multi-source information collaborative pose estimation is realized, high-precision visual inertial positioning of a train is realized, and particularly, excellent robustness is shown in an environment in which GNSS signals are shielded.

An information processing apparatus acquires an image taken by a camera and estimates a vanishing point by inputting the image to a trained model, the trained model being generated by subjecting a learning model for pose estimation to machine learning using a heatmap indicative of a true value for the vanishing point as training data.

The invention discloses a non-motor vehicle road network construction method based on streetscape images. The non-motor vehicle road network construction method comprises the following steps of S1, obtaining a plurality of streetscape images and metadata and track sequence data of the streetscape images; s2, extracting a motor vehicle lane area and a non-motor vehicle lane area from each streetscape image, identifying road infrastructures and states related to non-motor vehicle lanes, and classifying tracks; s3, fusing the obtained road area, road infrastructure and state and track sequence data to obtain non-motor vehicle geometric road section information, and constructing a topological relation between non-motor vehicle road sections; s4, determining the actual width of the non-motor vehicle lane according to the vanishing points in the directions of the motor vehicle lane and the non-motor vehicle lane and the vanishing points in the horizontal direction; and S5, attaching the attributes of the non-motor vehicle lanes to the road network geometric data to form a non-motor vehicle road network. According to the method, the non-motor vehicle road network containing geometric road section information, a road section topological relation and lane attribute information is constructed.

The invention relates to the technical field of tunnel image data processing, in particular to a tunnel smoke image recognition method and system based on multi-scale feature fusion. The method comprises the following steps: obtaining the spatial weight of each pixel point, wherein the spatial weight is in negative correlation with the distance from the pixel point to a vanishing point; obtaining an enhanced gradient of each pixel point, wherein the enhanced gradient is in positive correlation with the closeness degree between the second characteristic value and the first characteristic value of the pixel point, the first-order gradient module value and the spatial weight; taking the enhanced gradient greater than a gradient threshold as a seed point to perform connected domain analysis to obtain a candidate cluster in the tunnel image; according to the method, the extinction coefficient of the candidate cluster where the seed point is located is obtained, the pixel point corresponding to the candidate cluster is obtained from the historical image sequence of the current tunnel image, time domain accumulation is carried out on the enhancement gradient and the extinction coefficient of the pixel point, the smoke judgment score of the current tunnel image is obtained, tunnel smoke is recognized, and the accuracy of the recognition result is effectively improved.

An information processing apparatus estimates a plurality of vanishing points and an intrinsic parameter of a camera by inputting an image to a learning model trained by machine learning in advance for estimating a vanishing point and an intrinsic parameter of the camera; projects the estimated vanishing points onto a unit sphere in a world coordinate system on the basis of the intrinsic parameter; and calculates a rotation angle indicative of a posture of the camera on the basis of errors between the projected vanishing points and a plurality of reference vanishing points projected onto the unit sphere in advance.

The invention provides a point and line feature-based pose estimation method and electronic equipment. The method comprises the following steps of extracting point features and line features from an environment image; calculating one or more vanishing points in the environment image based on the extracted line features; according to the geometrical relationship between each line feature and the vanishing point, grouping the line features into different dominant direction sets, and removing the line features which do not belong to any dominant direction set to obtain an optimized line feature set; and fusing the point features and the optimized line feature set, taking the fused point features and the optimized line feature set as input together, performing pose estimation through a visual SLAM algorithm, and outputting pose information of the camera. According to the method, the weak texture environment is effectively dealt with, the point features and the line features are fused, and the line features are optimized by using the vanishing points, so that the key problems that the traditional visual SLAM which only depends on the point features is rare in features and easy to fail in tracking in the low-texture structured environment are solved, and stable and high-precision positioning in the severe environment is realized.

The invention provides a vehicle-mounted camera external parameter calibration method, electronic equipment and a program product, and relates to the technical field of vehicles. The method comprises the following steps: when a camera external parameter dynamic calibration starting condition is satisfied, acquiring a road image sequence corresponding to each camera of a vehicle; each group of road image sequence comprises at least one image of a structured road and a corresponding acquisition moment; determining vanishing point positions and horizon line positions corresponding to the cameras by utilizing an end-to-end vanishing point / horizon line detection model based on each group of road image sequences; wherein the end-to-end vanishing point / horizon line detection model is used for outputting the vanishing point position in the road image sequence, or outputting the vanishing point position and the horizon line position in the road image sequence; and according to the vanishing point position and the horizon line position corresponding to each camera, external parameters of each camera are determined. According to the invention, an efficient and simple solution is provided for external parameter calibration of the vehicle-mounted camera.

This invention proposes a camera focal length and attitude calibration method based on camera position and dual vanishing points, belonging to the fields of computer vision, SLAM, and photogrammetry. This invention only requires knowledge of two sets of parallel lines in space, measuring the camera position and the unit direction vectors of the parallel lines, capturing feature images of the parallel lines, obtaining the imaging relationship in the camera, and extracting the corresponding vanishing points. Utilizing the important geometric phenomenon that the angle between two straight lines in the camera coordinate system is equal to the angle between the unit direction vectors of the two sets of parallel lines, a method for quickly solving the camera focal length is established. Finally, the correspondence of unit direction vectors is converted into the correspondence of spatial points between two coordinate systems with the same origin to solve for the camera attitude. This invention reduces the number of vanishing points required for camera calibration, shortens the calibration time, provides a unique solution, and can simultaneously solve for focal length and attitude, among other advantages.

The invention discloses a camera / laser radar joint calibration method based on vanishing point constraint, and belongs to the technical field of robot perception and computer vision, and the method comprises the following steps: S1, obtaining a circle center coordinate and an orthogonal vanishing point in a camera coordinate system, and a circle center coordinate and a direction vector of an orthogonal straight line in a laser radar coordinate system; s2, determining a rotation matrix from a camera coordinate system to a laser radar coordinate system; and S3, estimating a translation vector from the camera coordinate system to the laser radar coordinate system. According to the camera / laser radar joint calibration method based on vanishing point constraint, through a rotation-translation decoupling form, direction information of strong constraint is provided by using an orthogonal groove and an outer frame, and rotation is preferentially locked through vanishing point and straight line fitting, so that the influence of a circle center error on rotation is significantly weakened, and the calibration precision is improved. And the overall precision and stability are improved.

This invention discloses a method for extrinsic parameter calibration between a camera and a lidar based on the vanishing point method, comprising the following steps: S1, acquiring an optical image of a target reference object using a camera, and acquiring laser point cloud data of the target reference object using a lidar; S2, determining the principal point and focal length of the camera based on the optical image; S3, processing the laser point cloud data based on the determined principal point and focal length of the camera to obtain a lidar projection image; S4, calibrating the extrinsic parameters between the camera and the lidar using the vanishing points of the optical image and the lidar projection image, and obtaining the extrinsic parameter calibration results; S5, further optimizing the camera's principal point, focal length, and extrinsic parameter calibration results using an iterative optimization method to obtain optimized extrinsic parameter calibration results. This invention utilizes vanishing points to solve the extrinsic parameter calibration problem between a camera and a lidar in scenarios without a calibration board, improving the efficiency and accuracy of extrinsic parameter calibration between lidar and camera.

The application relates to the technical field of lane line detection, and discloses a lane line detection method, device, computer equipment and storage medium, the method comprising the following steps: acquiring a lane image, detecting the lane image, and generating vanishing point coordinates; based on the vanishing point coordinates, a lane line detection model is used to generate a first lane line category and a second lane line category; lane line fusion is carried out based on the first lane line category and the second lane line category, and a lane line detection result is generated. The application improves the detection precision and accuracy of the lane line and reduces the safety hidden danger of automatic driving.

The application discloses a lane line detection method based on vanishing points and color filters, comprising the following steps: S1, acquiring continuous multiple frames of images from a driving video for pretreatment, acquiring the vanishing point position of the lane line under the driving environment, and then adaptively extracting a region of interest (ROI); S2, filtering the ROI according to the color features of the lane line to obtain a binary image, acquiring all the connected regions, their centroids and inclination angles in the binary image, and screening the connected regions meeting the lane line features based on the vanishing point features and angle constraints; S3, further acquiring the centroid points meeting the lane line features from the remaining connected regions, fitting the acquired centroid points using a least square method, and identifying the lane line. The lane line detection can be completed under complex conditions such as lane line damage, lane marking interference and lane line occlusion, has certain robustness, and improves the accuracy and real-time performance of the lane line detection method.

The application discloses a kind of line feature-based visual SLAM positioning precision promotion method, including extracting line feature on input image and short line segment is merged;Line feature is grouped according to angle to improve line feature matching speed;Angle and length threshold are set to screen line feature;The distance of candidate point and other line features is calculated, remove the outlier greater than threshold, calculate the center point as vanishing point to make the variance of in-point minimum;According to the coordinate variation of vanishing point, judge whether the motion of camera occurs rotation;Angle threshold is set, and the line feature with the included angle less than threshold is filtered out;Line feature is grouped according to angle to speed up the matching process of line feature after filtering;Normalize parameter is calculated by line feature endpoint;The camera pose is calculated by iteration to minimize the reprojection error.The application filters out the degenerate line feature by setting angle threshold, avoids the influence of degenerate line feature on pose calculation, and improves the accuracy of camera pose calculation by solving camera pose iteratively.

The application provides a fish-eye camera calibration method and a readable storage medium. The calibration method comprises the following steps: acquiring a calibration pattern; calibrating a first parameter in a manner of solving a vanishing point based on the calibration pattern; and calibrating a second parameter based on the calibration pattern and the first parameter. The first parameter comprises a focal length of the fish-eye camera and a translation parameter from an image plane coordinate system to an image coordinate system; and the second parameter comprises a distortion coefficient of the fish-eye camera. In this way, the parameters to be calibrated are solved in two steps, thereby avoiding the phenomenon that a large amount of data is required, a long calculation time is needed, a calibration result is unstable, and robustness is poor due to the calibration of a large number of parameters at one time. The calibration method can complete calibration only by using one calibration image under certain conditions, thereby solving the problem that the time, cost and robustness are not ideal in the prior art.

The application relates to the technical field of image processing, and discloses a claim settlement data anti-fake detection method, device, equipment and medium, belongs to the technical field of image processing, is applied to a financial technology scene, and comprises the following steps: performing shooting posture recognition on a shooting device to obtain a real shooting posture; performing edge line segment detection and vanishing point recognition on an original image obtained by the shooting device to perform shooting posture inference according to the recognized object vanishing point position, and obtain an inferred shooting posture; and performing anomaly detection on the original image according to the inferred shooting posture and the real shooting posture to obtain an image category obtained by whether the shooting device truly shoots a damaged object. In the application, the real shooting posture at the shooting moment of the shooting device is obtained, and the inferred shooting posture is inversely deduced according to the object vanishing point position detected from the original image, so that the authenticity of the claim settlement image is detected according to the inferred shooting posture and the real shooting posture, and the accuracy of the claim settlement anti-fraud detection is improved.

The invention provides a relative pose measurement method based on a sequence frame image, and the method comprises the steps: collecting an infrared image of a target in real time through an infrared camera, and collecting the inertial pose information of an aircraft in real time through an inertial navigation system in the process of measuring the relative position pose of the aircraft; grouping the acquired images in the form of a sliding window; obtaining a first homography matrix and a second homography matrix; determining a final homography matrix; obtaining a final point line feature on the current frame image; acquiring vanishing points of the target left line and the target right line intersected on the image coordinate system and vanishing points of the target starting line on the image coordinate system; establishing a pose resolving equation; and performing pose calculation on the pose calculation equation to obtain a pitch angle, a course angle and a roll angle of the aircraft and lateral displacement, forward displacement and vertical displacement of the aircraft relative to the target.

This invention relates to the field of vehicle assisted driving technology, and discloses a method, system, and electronic device for three-dimensional matching of wheel trajectories. The method includes: acquiring a first image and a second image with a fixed spatial positional relationship, which are synchronously acquired by an image acquisition device; acquiring a current frame image from the first image or the second image; estimating an initial wheel trajectory based on the vanishing point in the current frame image and the vehicle's inherent parameters; iteratively updating the trajectory parameters by combining real-time steering wheel angle and vehicle speed; determining the region of interest and performing feature extraction and matching on the image within the region; optimizing through cost aggregation and extracting a single disparity value along the trajectory path. This method solves the problems of high computational cost, difficult deployment, and difficulty in adapting to dynamic changes in vehicles with fixed regions of interest in large-scale deep learning networks, thereby improving the accuracy and real-time performance of wheel trajectory tracking and providing a more reliable perception basis for trajectory prediction and active control of vehicle assisted driving systems.

The application discloses a 5G beam forming method based on target detection and three-dimensional measurement assistance, which comprises the following steps: step S10: an optical RGB camera is mounted above each 5G base station, and the 5G base station and the optical RGB camera are combined to form a visual radio reference anchor point; step S20: the absolute space position coordinates of the optical RGB camera are obtained by using a high-precision surveying instrument, the attitude rotation matrix of the optical RGB camera relative to a world coordinate system is obtained by using a structured vanishing point direction estimation method, and the relative pose of each optical RGB camera is obtained; the plane parameters of the real ground in the camera coordinate system are calculated in combination with the gravity direction vector and the optical RGB camera pose; the target personnel is accurately positioned and tracked through the cross-view multi-optical RGB camera target detection and three-dimensional measurement of computer vision, and the three-dimensional spherical coordinates of the user relative to the 5G base station are solved in real time and accurately.

A method of performing a camera to ground alignment for a camera system on a vehicle. The method includes determining if enabling conditions have occurred and estimating a location of a vanishing point in a source image. Ground lines are selected based on a source image. A lane line detection is performed based on clustering of the ground lines to determine lane lines in the source image. At least one of pitch, yaw, or roll of the vehicle are estimated from the source image. A cost function based on estimates of pitch, yaw, and roll is minimized to obtain an optimal pitch value, an optimal yaw value, an optimal roll value, and lane lines from the source image. A sliding window-based refinement is performed on source images. Alignment results are broadcast to a downstream application or it is determined if the camera system on the vehicle is misaligned.

Techniques are described for identifying correspondences between images to generate a fundamental matrix for the camera positions related to the images. The resultant fundamental matrix enables epipolar geometry to correlate common features among the images. Correspondences are identified by confirming feature matches across images by applying a homography to data representing features across images. Further techniques are described herein for generating a representation of a boundary of a feature of a structure based on a digital image. In one or more embodiments, generating a representation of a boundary of a particular feature in a digital image comprises determining a portion of the image that corresponds to the structure, and determining a portion of the image that corresponds to the particular feature. One more vanishing points are associated with the portion of the image corresponding to the particular feature. The one or more vanishing points are used to generate a set of bounding lines for the particular feature, based on which the boundary indicator for the feature is generated.

The application discloses a kind of inland river ship freeboard measurement method based on monocular vision.First, the internal parameter calibration of monocular camera is carried out using checkerboard by Zhang Zhengyou calibration method, and the internal parameter of camera is obtained;The camera shoots the side view of the ship, and the Deeplabv3+ model is used to segment the part between the ship deck line and the waterline according to the side view of the ship;Further, the ship deck line and the waterline are detected to determine the position of the ship freeboard and calculate the pixel height of the ship freeboard;The conversion from pixel height to actual height is realized by the camera internal parameter obtained by calibration, and the real value of the ship freeboard is calculated.The application models the parameters of the camera itself, does not rely on vanishing point and vanishing line for ship freeboard measurement, and provides assistance for the safety navigation of inland river ships on water as the basis for ship overload detection.