CN116993830A - Automatic calibration method for dynamic camera coordinate mapping - Google Patents
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Abstract
The invention provides an automatic calibration method for dynamic camera coordinate mapping, which comprises the following steps: s1, collecting data before and after the offset of a camera to obtain a picture pair set; s2, matching of characteristic points of the camera picture and the shifted picture is achieved based on a SIFT algorithm, and a characteristic point pair set corresponding to different pictures of the camera is obtained; s3, calculating to obtain a camera parameter set by utilizing the characteristic point pair sets of all the picture pairs; and S4, setting weights for each group of camera parameters to obtain camera parameters for correction, and realizing automatic calibration of dynamic camera coordinate mapping. The automatic calibration method for the dynamic camera coordinate mapping is adopted, manual intervention is not needed, automatic calibration of the dynamic camera coordinate mapping is realized through automatic optimization of camera parameters, and waste of human resources is reduced.
Description
Technical Field
The invention relates to the technical field of camera monitoring, in particular to an automatic calibration method for dynamic camera coordinate mapping.
Background
The distance between mechanical gears of the driving motor and the precision of structural members can cause errors in the precision of preset positions, and the camera can accumulate errors when rotated for a long time, so that a picture in the current posture of the camera is offset to a certain extent from a picture in the original posture, and the position of an object in the picture is changed. After the camera picture is offset, the geographic position coordinate of the target object is not matched with the corresponding position in the video monitoring picture. At present, the correction of the camera picture is mainly finished by manual operation, and the manual operation is time-consuming and labor-consuming, so that a large amount of human resources are wasted, and the cost is increased.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an automatic calibration method for the coordinate mapping of the dynamic camera, which does not need human intervention and realizes the automatic calibration of the coordinate mapping of the dynamic camera through the automatic optimization of camera parameters.
In order to achieve the above object, the present invention provides the following solutions: an automatic calibration method for dynamic camera coordinate mapping comprises the following steps:
s1, collecting data before and after the offset of a camera to obtain a picture pair set;
s2, matching of characteristic points of the camera picture and the shifted picture is achieved based on a SIFT algorithm, and a characteristic point pair set corresponding to different pictures of the camera is obtained;
s3, calculating to obtain a camera parameter set by utilizing the characteristic point pair sets of all the picture pairs;
and S4, setting weights for each group of camera parameters to obtain camera parameters for correction, and realizing automatic calibration of dynamic camera coordinate mapping.
Preferably, in the step S1, the following steps are included:
s11, before data of camera offset are collected, setting the zoom multiple of the camera to be Z, and obtaining pictures in different directions within 360 DEG range of the camera to obtain a picture set T 1 And then recording the gesture information of the camera corresponding to each picture to obtain a gesture information set:
M＝{(p 1 ,t 1 ,z 1 ),(p 2 ,t 2 ,z 2 ),(p 3 ,t 3 ,z 3 ),…,(p n ,t n ,z n )},n＞0
wherein p is n To take the corresponding azimuth angle, t n To take the corresponding pitch angle, z of the nth picture n The scaling times corresponding to the nth picture are taken;
s12, collecting data of the offset of the camera, setting the zoom multiple of the camera to be Z, rotating the camera to the corresponding position in the M according to the attitude information set M, and obtaining a picture of a picture at the position to obtain a picture set T 2 ；
S13, T is taken 1 And T 2 Pairing the pictures in the same posture to obtain a picture pair set:
T＝{(T 11 ,T 21 ),(T 12 ,T 22 ),(T 13 ,T 23 ),…,(T 1n ,T 2n )},n＞0
wherein T is 1n For a set of pictures T 1 N-th picture, T 2n For a set of pictures T 2 The nth picture of (a).
Preferably, in the step S2, the pairing of the feature points of the camera frame and the shifted frame includes the following steps:
s21, establishing a multi-scale space of an image and a Gaussian pyramid image;
s22, subtracting 2 Gaussian images of adjacent scales to obtain a Gaussian difference multi-scale space, and obtaining a local extremum point;
s23, accurately positioning the obtained extreme points by a surface fitting method, and removing edge points and points with lower contrast in initial characteristic points by adopting a hessian matrix of the Gaussian difference image to obtain the characteristic points of the image.
S24, after the feature points of the image are obtained, matching 2 feature points by taking Euclidean distance as a similarity criterion of the multidimensional vector, and matching the feature points to obtain a feature point pair set:
P＝{[(x 11 ,y 11 ),(x 12 ,y 12 )],[(x 21 ,y 21 ),(x 22 ,y 22 )],…,[(x n1 ,y n1 ),(x n2 ,y n2 )]},n＞0
wherein [ (x) n1 ,y n1 ),(x n2 ,y n2 )]Is the pixel coordinate pair of the nth feature point pair.
Preferably, feature point pairing is carried out on each group of pictures in the picture pair set T, so as to obtain feature point pair sets of all the picture pairs:
Ω＝{P 1 ,P 2 ,P 3 ,…,P m },m＞0
wherein P is m And the feature point pair set is the m group of picture pairs.
Preferably, in the step S3, the camera parameters include an initial azimuth angle, an initial pitch angle, and an initial roll angle of the camera, and in order to implement correction of the camera, parameters after the camera is offset are calculated, including the following steps:
s31, before the camera is deviated, the conversion relation between the pixel coordinates and the longitude and latitude coordinates is as follows:
s32, after the camera is shifted, the conversion relation between the pixel coordinates and the longitude and latitude coordinates is as follows:
wherein H is (α,β,θ) The method is a conversion matrix related to an initial azimuth angle, an initial pitch angle and an initial roll angle, delta 1, delta 2 and delta 3 are the variation quantities of the initial azimuth angle, the initial pitch angle and the initial roll angle respectively, wherein (x, y) and (x ', y') are pixel coordinates of the same geographic position in two pictures before and after the offset of the camera, and (lon, lat) and (lon ', lat') are plane coordinates after the conversion of (x, y) and (x ', y').
Preferably, in the step S3, further includes:
s33, calculating an optimal solution of three variables (delta 1, delta 2 and delta 3) in the conversion relation between pixel coordinates after camera offset and longitude and latitude coordinates by adopting a genetic algorithm or other algorithms for searching the optimal solution, and calculating a camera parameter set according to a characteristic point pair set P, wherein the method comprises the following steps of:
s331, setting a threshold I, and taking Loss as a Loss function:
s332, if Loss is more than or equal to I, repeating the step S33 to obtain an optimal solution;
s333, if Loss is less than I, obtaining a corresponding camera parameter x= { a, b, c }, where a=α+Δ1, b=β+Δ2, and c=θ+Δ3;
s34, performing step S33 on the feature point pair set omega of all the picture pairs to obtain a camera parameter set K= { X 1 ,X 2 ,X 3 ,…,X m M > 0, loss function value set l= { Loss 1 ,Loss 2 ,…,Loss m },m＞0；
Wherein X is m Camera parameters for mth group of picture pairs, loss m Is the loss function value for the mth group of picture pairs.
Preferably, in the step S4, in order to reduce the influence of strong wind and strong fog and special weather, a weight W is set for each optimized set of camera parameters m ：
Wherein i is the total number of loss function value sets;
according to the weight of each group of camera parameters, the camera parameters X' = (A, B, C) for correcting are calculated as follows:
wherein A, B and C are respectively the initial azimuth angle and the initial depression angle of the camera after correctionElevation angle and initial roll angle, (a) m ,b m ,c m ) And the camera parameters corresponding to the m-th group of picture pairs.
Preferably, after the correction of the camera, the conversion relationship between the pixel coordinates and the longitude and latitude coordinates is as follows:
and (3) performing the operations from the step S1 to the step S4 at intervals, and thus realizing the automatic calibration of the dynamic camera coordinate mapping.
Therefore, the automatic calibration method for the dynamic camera coordinate mapping is adopted, human intervention is not needed, and the automatic calibration of the dynamic camera coordinate mapping is realized through the automatic optimization of camera parameters.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an automatic calibration method for dynamic camera coordinate mapping according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide an automatic calibration method for dynamic camera coordinate mapping, which does not need human intervention and realizes the automatic calibration of the dynamic camera coordinate mapping through the automatic optimization of camera parameters.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a diagram of an automatic calibration method for dynamic camera coordinate mapping according to the present invention, as shown in fig. 1, the present invention provides an automatic calibration method for dynamic camera coordinate mapping, comprising the following steps:
s1, collecting data before and after the offset of a camera to obtain a picture pair set;
in step S1, the method specifically includes the following steps:
s11, before data of camera offset are collected, setting the zoom multiple of the camera to be Z, and obtaining pictures in different directions within 360 DEG range of the camera to obtain a picture set T 1 And then recording the gesture information of the camera corresponding to each picture to obtain a gesture information set:
M＝{(p 1 ,t 1 ,z 1 ),(p 2 ,t 2 ,z 2 ),(p 3 ,t 3 ,z 3 ),…,(p n ,t n ,z n )},n＞0
wherein p is n To take the corresponding azimuth angle, t n To take the corresponding pitch angle, z of the nth picture n The scaling times corresponding to the nth picture are taken;
s12, collecting data of the offset of the camera, setting the zoom multiple of the camera to be Z, rotating the camera to the corresponding position in the M according to the attitude information set M, and obtaining a picture of a picture at the position to obtain a picture set T 2 ；
S13, T is taken 1 And T 2 Pairing the pictures in the same posture to obtain a picture pair set:
T＝{(T 11 ,T 21 ),(T 12 ,T 22 ),(T 13 ,T 23 ),…,(T 1n ,T 2n )},n＞0
wherein T is 1n For picture collectionT 1 N-th picture, T 2n For a set of pictures T 2 The nth picture of (a).
S2, matching of characteristic points of the camera picture and the shifted picture is achieved based on a SIFT algorithm, and a characteristic point pair set corresponding to different pictures of the camera is obtained;
in step S2, the pairing of the feature points of the camera frame and the shifted frame includes the steps of:
s21, establishing a multi-scale space of an image and a Gaussian pyramid image;
s22, subtracting 2 Gaussian images of adjacent scales to obtain a Gaussian difference multi-scale space, and obtaining a local extremum point;
s23, accurately positioning the obtained extreme points by a surface fitting method, and removing edge points and points with lower contrast in initial characteristic points by adopting a hessian matrix of a Gaussian differential image to obtain the characteristic points of the image;
s24, after the feature points of the image are obtained, matching 2 feature points by taking Euclidean distance as a similarity criterion of the multidimensional vector, and matching the feature points to obtain a feature point pair set:
P＝{[(x 11 ,y 11 ),(x 12 ,y 12 )],[(x 21 ,y 21 ),(x 22 ,y 22 )],…,[(x n1 ,y n1 ),(x n2 ,y n2 )]},n＞0
wherein [ (x) n1 ,y n1 ),(x n2 ,y n2 )]A pixel coordinate pair which is the nth feature point pair;
pairing the characteristic points of each group of pictures in the picture pair set T to obtain a characteristic point pair set of all the picture pairs:
Ω＝{P 1 ,P 2 ,P 3 ,…,P m },m＞0
wherein P is m And the feature point pair set is the m group of picture pairs.
S3, calculating to obtain a camera parameter set by utilizing the characteristic point pair sets of all the picture pairs;
in step S3, the camera parameters include an initial azimuth angle, an initial pitch angle, and an initial roll angle of the camera, and in order to implement correction of the camera, parameters after the camera is offset are calculated, including the following steps:
s31, before the camera is deviated, the conversion relation between the pixel coordinates and the longitude and latitude coordinates is as follows:
s32, after the camera is shifted, the conversion relation between the pixel coordinates and the longitude and latitude coordinates is as follows:
wherein H is (α,β,θ) The method is a conversion matrix related to an initial azimuth angle, an initial pitch angle and an initial roll angle, delta 1, delta 2 and delta 3 are the variation quantities of the initial azimuth angle, the initial pitch angle and the initial roll angle respectively, wherein (x, y) and (x ', y') are pixel coordinates of the same geographic position in two pictures before and after the offset of the camera, and (lon, lat) and (lon ', lat') are plane coordinates after the conversion of (x, y) and (x ', y').
In addition, the step S3 further includes:
s33, calculating an optimal solution of three variables (delta 1, delta 2 and delta 3) in the conversion relation between pixel coordinates after camera offset and longitude and latitude coordinates by adopting a genetic algorithm or other algorithms for searching the optimal solution, and calculating a camera parameter set according to a characteristic point pair set P, wherein the method comprises the following steps of:
s331, setting a threshold I, and taking Loss as a Loss function:
s332, if Loss is more than or equal to I, repeating the step S33 to obtain an optimal solution;
s333, if Loss is less than I, obtaining a corresponding camera parameter x= { a, b, c }, where a=α+Δ1, b=β+Δ2, and c=θ+Δ3;
s34, performing step S33 on the feature point pair set omega of all the picture pairs to obtain a camera parameter set K= { X 1 ,X 2 ,X 3 ,…,X m M > 0, loss function value set l= { Loss 1 ,Loss 2 ,…,Loss m },m＞0；
Wherein X is m Camera parameters for mth group of picture pairs, loss m Is the loss function value for the mth group of picture pairs.
S4, setting weights for each group of camera parameters to obtain camera parameters for correction, and realizing automatic calibration of dynamic camera coordinate mapping;
in step S4, a weight W is set for each optimized set of camera parameters in order to reduce the influence of heavy wind and heavy fog m ：
Wherein i is the total number of loss function value sets;
according to the weight of each group of camera parameters, the camera parameters X' = (A, B, C) for correcting are calculated as follows:
wherein A, B, C are respectively the initial azimuth angle, the initial pitch angle and the initial roll angle after the correction of the camera, (a) m ,b m ,c m ) The camera parameters corresponding to the m group of picture pairs;
after the correction of the camera, the conversion relation between the pixel coordinates and the longitude and latitude coordinates is as follows:
and (3) performing the operations from the step S1 to the step S4 at intervals, and thus realizing the automatic calibration of the dynamic camera coordinate mapping.
Therefore, the automatic calibration method for the dynamic camera coordinate mapping is adopted, human intervention is not needed, and the automatic calibration of the dynamic camera coordinate mapping is realized through the automatic optimization of camera parameters.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. An automatic calibration method for dynamic camera coordinate mapping is characterized by comprising the following steps:
s1, collecting data before and after the offset of a camera to obtain a picture pair set;
s2, matching of characteristic points of the camera picture and the shifted picture is achieved based on a SIFT algorithm, and a characteristic point pair set corresponding to different pictures of the camera is obtained;
s3, calculating to obtain a camera parameter set by utilizing the characteristic point pair sets of all the picture pairs;
and S4, setting weights for each group of camera parameters to obtain camera parameters for correction, and realizing automatic calibration of dynamic camera coordinate mapping.
2. The method according to claim 1, wherein in the step S1, the method comprises the steps of:
s11, before data of camera offset are collected, setting the zoom multiple of the camera to be Z, and obtaining pictures in different directions within 360 DEG range of the camera to obtain a picture set T 1 And then recording the gesture information of the camera corresponding to each picture to obtain a gesture information set:
M＝{(p 1 ,t 1 ,z 1 ),(p 2 ,t 2 ,z 2 ),(p 3 ,t 3 ,z 3 ),…,(p n ,t n ,z n )},n＞0
wherein p is n To take the corresponding azimuth angle, t n To take the corresponding pitch angle, z of the nth picture n The scaling times corresponding to the nth picture are taken;
s12, collecting data of the offset of the camera, setting the zoom multiple of the camera to be Z, rotating the camera to the corresponding position in the M according to the attitude information set M, and obtaining a picture of a picture at the position to obtain a picture set T 2 ；
S13, T is taken 1 And T 2 Pairing the pictures in the same posture to obtain a picture pair set:
T＝{(T 11 ,T 21 ),(T 12 ,T 22 ),(T 13 ,T 23 ),…,(T 1n ,T 2n )},n＞0
wherein T is 1n For a set of pictures T 1 N-th picture, T 2n For a set of pictures T 2 The nth picture of (a).
3. The automatic calibration method of dynamic camera coordinate mapping according to claim 1, wherein in the step S2, pairing of the feature points of the camera frame and the offset frame includes the steps of:
s21, establishing a multi-scale space of an image and a Gaussian pyramid image;
s22, subtracting 2 Gaussian images of adjacent scales to obtain a Gaussian difference multi-scale space, and obtaining a local extremum point;
s23, accurately positioning the obtained extreme points by a surface fitting method, and removing edge points and points with lower contrast in initial characteristic points by adopting a hessian matrix of the Gaussian difference image to obtain the characteristic points of the image.
S24, after the feature points of the image are obtained, matching 2 feature points by taking Euclidean distance as a similarity criterion of the multidimensional vector, and matching the feature points to obtain a feature point pair set:
P＝{[(x 11 ,y 11 ),(x 12 ,y 12 )],[(x 21 ,y 21 ),(x 22 ,y 22 )],…,[(x n1 ,y n1 ),(x n2 ,y n2 )]},n＞0
wherein [ (x) n1 ,y n1 ),(x n2 ,y n2 )]Is the pixel coordinate pair of the nth feature point pair.
4. The automatic calibration method for dynamic camera coordinate mapping according to claim 3, wherein feature point pairing is performed on each group of pictures in the picture pair set T to obtain a feature point pair set of all picture pairs:
Ω＝{P 1 ,P 2 ,P 3 ,…,P m },m＞0
wherein P is m And the feature point pair set is the m group of picture pairs.
5. The automatic calibration method of dynamic camera coordinate mapping according to claim 1, wherein in the step S3, the camera parameters include an initial azimuth angle, an initial pitch angle, and an initial roll angle of the camera, and in order to implement correction of the camera, parameters after the camera is offset are calculated, including the following steps:
s31, before the camera is deviated, the conversion relation between the pixel coordinates and the longitude and latitude coordinates is as follows:
s32, after the camera is shifted, the conversion relation between the pixel coordinates and the longitude and latitude coordinates is as follows:
wherein H is (α,β,θ) The method is a conversion matrix related to an initial azimuth angle, an initial pitch angle and an initial roll angle, delta 1, delta 2 and delta 3 are the variation quantities of the initial azimuth angle, the initial pitch angle and the initial roll angle respectively, wherein (x, y) and (x ', y') are pixel coordinates of the same geographic position in two pictures before and after the offset of the camera, and (lon, lat) and (lon ', lat') are plane coordinates after the conversion of (x, y) and (x ', y').
6. The method according to claim 5, wherein in step S3, further comprising:
s33, calculating an optimal solution of three variables (delta 1, delta 2 and delta 3) in the conversion relation between pixel coordinates after camera offset and longitude and latitude coordinates by adopting a genetic algorithm or other algorithms for searching the optimal solution, and calculating a camera parameter set according to a characteristic point pair set P, wherein the method comprises the following steps of:
s331, setting a threshold I, and taking Loss as a Loss function:
s332, if Loss is more than or equal to I, repeating the step S33 to obtain an optimal solution;
s333, if Loss is less than I, obtaining a corresponding camera parameter x= { a, b, c }, where a=α+Δ1, b=β+Δ2, and c=θ+Δ3;
s34, performing step S33 on the feature point pair set omega of all the picture pairs to obtain a camera parameter set K= { X 1 ,X 2 ,X 3 ,…,X m M > 0, loss function value set l= { Loss 1 ,Loss 2 ,…,Loss m },m＞0；
Wherein X is m Camera parameters for mth group of picture pairs, loss m Is the loss function value for the mth group of picture pairs.
7. The method according to claim 1, wherein in step S4, weights W are set for each optimized set of camera parameters in order to reduce the influence of heavy wind and heavy fog on special weather m ：
Wherein i is the total number of loss function value sets;
according to the weight of each group of camera parameters, the camera parameters X' = (A, B, C) for correcting are calculated as follows:
wherein A, B, C are respectively the initial azimuth angle, the initial pitch angle and the initial roll angle after the correction of the camera, (a) m ,b m ,c m ) And the camera parameters corresponding to the m-th group of picture pairs.
8. The automatic calibration method for dynamic camera coordinate mapping according to claim 7, wherein after the correction of the camera, the conversion relationship between the pixel coordinates and the longitude and latitude coordinates is:
and (3) performing the operations from the step S1 to the step S4 at intervals, and thus realizing the automatic calibration of the dynamic camera coordinate mapping.
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