57 results about "Pinhole camera model" patented technology

The invention relates to a visual computing and prewarning method of deviation angle and distance of an automobile from a lane line. The image processing and computer vision technologies are utilized, and the deviation angle and distance of the automobile from the lane line are computed in real time according to the road surface image acquired by a vehicle-mounted camera, thereby estimating the line crossing time for safety prewarning. The method comprises the following steps: detecting the lane lines of the road surface image to obtain a linear equation of partial lane lines; establishing a three-dimensional coordinate system by using the camera as the initial point, and recording the mounting height and depression angle of the camera; calibrating the focal length according to the lane detection result under the condition of a given deflection angle; computing the deflection angle and vertical distance of the automobile relative to the lane line according to a pinhole camera model; and estimating the deviation time from the lane according to the instantaneous running speed of the automobile, thereby obtaining the safety prewarning or intelligent control information of the running automobile.

The invention discloses an RGB-D image acquisition method. The RGB-D image acquisition method includes the steps that a depth camera is calibrated to obtain internal parameters Ad and external parameters [Rd|Td] of the depth camera, a color camera is calibrated to obtain internal parameters Ac and external parameters [Rc|Tc] of the color camera, and relative external parameters [Rr|Tr] of the depth camera under a camera coordinate system of the color camera are calculated; a depth image and a color image of one photographic field are obtained through the depth camera and the color camera respectively, the obtained Ad, the obtained Ac and the obtained [Rr|Tr] are used for projecting points on the depth image to the color image based on a pinhole camera model, registering of the depth image and the color image is carried out, and a registered RGB-D image is obtained. The RGB-D image acquisition method has the good application value for obtaining the RGB-D image in the outdoor environment.

The invention discloses a calibrating method for two-dimensional laser and a camera without overlapped viewing fields. A calibrating system comprises a two-dimensional laser scanner, the camera, a plane mirror and the like. The calibrating method includes calibrating intrinsic parameters of the camera by the aid of the traditional chessboard table via a pinhole camera model; reasonably configuring the laser scanner, the camera and the plane mirror, adhering the black and white chessboard table on the plane mirror, locating the chessboard table within the range of a viewing field of the camera and determining a transformation matrix between a coordinate system of the chessboard and a coordinate system of the camera; determining a transformation relation between the coordinate system of the chessboard and a coordinate system of a camera image reflected in the mirror according to a symmetric relation of mirror surface images so as to acquire a transformation relation between the actual camera and the camera image reflected in the mirror; performing observation at selected sufficient frequency by changing the position and gestures of a black triangular calibrating plate to acquire sufficient observation samples, estimating by a Gauss-Newton iterative process to acquire a transformation matrix between the coordinate system of the camera image and the two-dimensional laser scanner; and acquiring a rotation and translation transformation matrix between the coordinate system of the camera and a coordinate system of the two-dimensional laser scanner via relations between positions and gestures of the camera and the camera image and relations between positions and gestures of the camera image and the two-dimensional laser scanner.

The invention discloses a novel pure rotation self-calibration method based on a spherical projection model, aims at internal parameter calibration tasks of a pinhole camera, and particularly relates to a pure rotation camera self-calibration method based on the spherical projection model. A spherical projection model of the pinhole camera is constructed, and that the distance between a space stationary point and a corresponding spherical projection point is consistent during pure rotation of the camera is analyzed; a constrain equation set of internal parameters is constructed according to the property; and the equation set is solved by a nonlinear least square algorithm. Compared with prior methods, internal parameters can be obtained by using corresponding point characteristics on two images, complicated matrix numeric calculation is not needed, only four matching points on the two images can complete calibration of four internal parameters of the camera, and the method is suitable for on-line and off-line calibration. Simulation and experiment results show that the method is simple and practical, high in calibration accuracy and good in robustness for image noise and translation noise.

The invention discloses a target space positioning method based on a single camera, and belongs to the technical field of intelligent video monitoring. Aiming at the problem that an existing target space positioning technology based on a linear camera model does not consider imaging errors caused by lens distortion, based on the isotropous characteristic of lens distortion, when an ideal linear camera model is used for target space positioning, space positioning auxiliary information such as the position of the camera and geodetic absolute coordinates of a reference point and an identification point of the scene center are obtained by combining a GPS, the position, in an image, of a target is compensated and corrected, and therefore the actual geodetic absolute coordinates of the target can be obtained fast and accurately. Compared with the prior art, the robustness, the real-time performance and the applicability of the target space positioning method are improved greatly.

A method of determining the distance of an object from an automated vehicle based on images taken by a monocular image acquiring device. The object is recognized with an object-class by means of an image processing system. Respective position data are determined from the images using a pinhole camera model based on the object-class. Position data indicating in world coordinates the position of a reference point of the object with respect to the plane of the road is used with a scaling factor of the pinhole camera model estimated by means of a Bayes estimator using the position data as observations and under the assumption that the reference point of the object is located on the plane of the road with a predefined probability. The distance of the object from the automated vehicle is calculated from the estimated scaling factor using the pinhole camera model.

The embodiment of the invention provides a method and system for the simultaneous positioning and mapping, and a storage medium. The method comprises the steps: obtaining a big-view-field image through a big-view-field camera; projecting the big-view-field image to imaging planes of at least two virtual pin hole cameras in different directions based on a multiple virtual pin hole camera model according to the internal parameters of the big-view-field camera, so as to obtain a projection map based on each virtual pin hole camera; unfolding at least two projection maps in different directions, so as to obtain a big-view-field distortion removal image corresponding to the big-view-field image, wherein the multiple virtual pin hole camera model comprises at least two virtual pin hole cameras in different directions, and the centers of the virtual pin hole cameras are coincided with the center of the big-view-field camera; tracking the movement of the big-view-field camera based on the big-view-field distortion removal image, and building a map. Through the above scheme, the method can maintain all view fields of the big-view-field camera, and is not affected by the image distortion.

The invention discloses a pinhole camera detecting method based on binocular detection, which is characterized in that pinhole cameras hidden in a room can be effectively detect and its accurate position can be determined; based on the feature of the cat-eye effect of the pinhole cameras that the reflected light is returned along the original path and the scattering angle is quite small, and by means of an invisible laser beam, suspicious areas in a room with possible pinhole cameras can be scanned; two CCDs having certain transverse distance are adopted to simultaneously detect the reflectedlight of the suspicious area; the imaging time sequence of two CCDs maintain simultaneous, and after the sequence images of the two CCDS undergo difference and information processing, the difference images with high recognition degree can be obtained, and effective identification can be realized. The invention is advantageous in that as the two images obtained are both active detection images, thedifference between the reflected light at background can be little, and the difference between the reflected light of the pinhole cameras can be great, and thereby without too much subsequent information processing, the images aimed by the pinhole camera with high recognition degree can be obtained; the identifying accuracy can be greatly increased, and false-alarm rate can be reduced.

The invention discloses a visual SLAM loopback detection method fusing with geometric information, comprising: S1, acquiring a real key frame; S2: determining a change of view of a virtual camera, andcalculating the pose of the virtual camera in a SLAM system; S3, reconstructing three-dimensional point cloud in the SLAM system according to the real key frame; S4, calculating the pixel coordinatesof each three-dimensional point of the three-dimensional point cloud in a virtual key frame according to a pinhole camera model and projective geometry; S5, rendering the virtual key frame of the virtual camera; S6, extracting the feature descriptors of real key frame and the virtual key frame; S7: calculating the BoVs of the real key frame and the virtual key frame, adding the BoVs to a database, searching all real key frames and virtual key frames in the database during loopback detection to obtain loopback detection information. The visual SLAM loopback detection method fusing with geometric information can not only directly generate loopback detection information of different viewing angles by using monocular camera information, but also has higher efficiency than a traditional loopback detection method.

An on-orbit self-calibration method for a space target relative navigation vision measurement system comprises the following steps: establishing the space target relative navigation vision measurementsystem based a pinhole camera model; obtaining an internal and external parameter expression for the space target relative navigation vision measurement system by using an identified common feature point in a group of corresponding images in an image sequence; further obtaining an ideal constraint equation for the internal and external parameter expression by using the characteristics of a rotation matrix; and constructing the error function of the ideal constraint equation as an objective function, preliminarily obtaining parts of parameters of the space target relative navigation vision measurement system through optimization using a particle swarm algorithm having a fast calculation speed, and obtaining all remaining parameters through further optimization based on the internal and external parameter expression for the space target relative navigation vision measurement system in order to achieve the on-orbit self-calibration method for the space target relative navigation vision measurement system.

The invention relates to a millimeter-wave radar and camera rapid joint calibration method, and the method comprises the following steps: S1, enabling a camera and a radar group to be installed on a motion platform, and enabling the motion platform to be placed at an open flat place; s2, arranging a corner reflector right in front of the motion platform, adjusting the height of the corner reflector to be the same as the height of the radar set, and adjusting the distance between the corner reflector and the radar set until the corner reflector appears in the image range of the camera; s3, calibrating the internal parameters of the camera by using the pinhole camera model and adjusting the radar angle at the same time; s4, determining an initial projection external parameter of the millimeter wave coordinate system Xr-Yr to the camera coordinate system Xc-Yc; s5, adjusting a yaw angle and a translation distance value in the external parameter by calculating the deviation between the imaging position of the corner reflector in the image plane and the projection position of the radar detection point on the image plane; s6, outputting an internal parameter matrix and an external parameter of the camera, and completing the calibration. The corner reflector is high in reflection intensity and small in size, and better feedback can be obtained.

The invention discloses a mobile robot positioning method based on RGB-D camera and IMU information fusion. The method comprises the following steps: (1) establishing a pinhole camera model; (2) establishing an IMU measurement model; (3) performing structured light camera depth calculation and pose transformation calculation based on feature point matching; (4) carrying out IMU pre-integration attitude calculation and conversion between an IMU coordinate system and a camera coordinate system; and (5) performing an RGB-D data and IMU data fusion process and a camera pose optimization process, and finally obtaining an accurate positioning pose. According to the invention, the RGB-D camera and the IMU sensor are combined for positioning; the characteristic that the IMU has good state estimation in short-time rapid movement is well utilized, and the camera has the characteristic that the camera basically does not drift under the static condition, so that the positioning system has good static characteristics and dynamic characteristics, and the robot can adapt to low-speed movement occasions and high-speed movement occasions.

The invention discloses a balancing optimization algorithm for a target measuring head. The invariant relation of a vector in the Euclidean space and the photographic space is found to be used as a constraint condition, space vectors from handheld target surface feature points B to the target measuring head A under different poses and imaging rules under a pinhole camera model are built, the constraint condition is used as a condition equation to build a function model, Helmet posterior variance estimation is adopted as a random model, finally, the difference value is brought into the condition equation, and difference value testing is completed. According to the algorithm, the space position information of each handheld target surface feature point is sufficiently utilized, the robustness is good, the stability is high, 3D space reduction precision of the handheld target measuring head is improved, and the effective method is provided for further improving the binocular stereoscopic vision measuring precision.

The invention provides a vision-based multi-depth two-dimensional plane size measurement method which is characterized in that firstly camera internal parameters are calibrated so that plane parameters are obtained; the distance from the camera to a measured object is measured by a laser distance measurement instrument or a displacement sensor according to the perspective projection relation of a pinhole camera model and transmitted to a vision system to be adjusted and then photographing is performed; and the size of the measured object is calculated through image processing. With application of the method, "dynamic calibration" is realized by the system by using vision instead of using the relatively expensive 2D laser sensor, and mapping from the plane to the actual space coordinates is realized by the system according to difference distances from the measured object to the camera so as to complete size measurement.

The invention provides a computer vision based transparent medium refractivity measurement method. The method includes: selecting a camera and a calibration plate; establishing a camera coordinate system, an image coordinate system and a relationship of the two coordinate systems; conducting two image acquisition on the calibration plate with the camera, with one image acquisition carried out under the air medium, and the other carried out under the circumstance of placing a to-be-measured medium between the calibration plate and the camera; utilizing a pinhole camera model to calculate the depth information of a corner point under a camera coordinate system, the corner point corresponded included angle between a back projection ray and an optical axis, and the position change of the corner point in two images; measuring the thickness of the to-be-measured medium, deducing the refractivity calculation formula of the to-be-measured medium according to a pinhole imaging model and the Snell law of refraction, and introducing the calculation parameters to obtain the refractivity of the to-be-measured medium. The method provided by the invention realizes measurement of object refractivity only through two image acquisition on the calibration plate with the camera, greatly simplifies the process of calculation, simplifies the measurement steps, reduces the measurement cost, and has high calculation accuracy.

The invention relates to a system calibration method, system and device for optimizing feature extraction and a medium, and the method comprises the steps: carrying out system calibration through extracting feature points of a calibration plate in an original image, and obtaining external parameters of a camera; repeatedly executing the preset iterative calculation until a preset termination condition is met, and completing system calibration. The preset iterative calculation comprises the following steps: calculating to obtain a conversion matrix according to a pinhole camera model and obtained camera external parameters, and converting an original image into a front view containing a calibration plate; extracting the feature points of the calibration plate in the front view, back-projecting the pixel coordinates of the feature points to the original image according to the conversion matrix, and performing system calibration to obtain the external parameters of the camera by obtaining the pixel coordinates of the back-projected feature points. According to the invention, the problem of low system calibration accuracy caused by image factors is solved, the accuracy of feature point extraction of the calibration plate is improved, and the influence of feature point extraction errors introduced by distortion and perspective back projection on a final system calibration result is reduced.

The invention belongs to the technical field of precision measurement and particularly relates to a synthetic aperture camera calibration method based on light field distribution. The method comprisessteps: in order to overcome the pupil aberration problem of a pinhole camera model, an imaging pupil is divided into a plurality of sub pupils, and according to actual light field distribution, an imaging sensor is divided into different imaging areas corresponding to sub pupils; and in combination of synthetic aperture camera model joint calibration optimization, a camera model more in line withthe actual imaging process is constructed. The pupil aberration caused by single pinhole imaging hypothesis can be effectively eliminated, the direction ambiguity of monocular vision is overcome, andgreat significance is achieved for improving the measurement precision of a photogrammetry technology.

The invention discloses a driving fatigue detection system and method based on a facial skeleton model, a medium and detection equipment, and relates to the technical field of image recognition. The method comprises the following steps: identifying an obtained image by using a human body posture detection model; performing 3D skeletization on the obtained recognition image through a pinhole camera model, extracting head posture features and face features by using a face detection model, and calculating fatigue features by using the extracted head and face posture features; detecting the obtained fatigue features by using a classifier to obtain a fatigue detection model, carrying out grading judgment on fatigue by using the fatigue detection model, and interacting with a user by using an APP. According to the method, fatigue behaviors are analyzed, fatigue recognition features are extracted, and a fatigue detection model fusing multiple fatigue features is obtained by using a random forest algorithm. The whole system is realized on the Raspberry Pi, development of a rear end and an APP end is completed, and the whole fatigue detection system has high integrity.