CN101894369A - Real-time method for computing focal length of camera from image sequence - Google Patents
Real-time method for computing focal length of camera from image sequence Download PDFInfo
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Abstract
The invention provides a real-time method for computing the focal length of a camera from an image sequence, comprising the following steps of: selecting two adjacent images from the image sequence; matching characteristic points of the two images to obtain matching point pairs; solving any seven groups of matching points of the matching point pairs to obtain multiple possible solutions of the focal length; evaluating the accuracy of each possible solution, and using an evaluating value as the weight of each possible solution; computing the weighted sum of the possible solutions according to the weights for evaluation to obtain the probability density function of the focal length; sampling on a probability density function camber of the focal length for multiple times to obtain multiple sampling points; and fitting the sampling points by applying a Gaussian function, and using the peak value of the Gaussian function as a final focal length value. The real-time method for computing the focal length of the camera can be used for overcoming the defects of the self-calibration of the camera in practical application, thereby fast and accurately evaluating the focal length parameters of the camera.
Description
Technical field
The present invention relates to computer vision field, relate in particular to the implementation method of restoration scenario three-dimensional model from image sequence.
Background technology
At computer vision field, people are of long duration to the research of how to obtain camera motion and scene three-dimensional model from image sequence.Initial application is embodied in the machine vision aspect, the researchist judges the spatial relationship of scene by the geological information that extracts from image sequence, and further guidance machine people's motion, this class research needs to use special and expensive collecting device usually, and application scenarios is also had bigger limitation.Along with the development of hardware device with popularize, the researchist wishes to calculate the three-dimensional model of scene from the sequence image that common collecting device obtains, and the gordian technique that wherein relates to is exactly Camera calibration work.
The Camera calibration technology is the important step in the computer vision research, uses in fields such as three-dimensional reconstruction, navigation widely.From the history of development, the camera calibration technology can be divided into: traditional calibration technique, active calibration technique and self-calibration technology.The tradition calibration technique is used the demarcation thing of accurately making, ask for camera parameters according to setting up mapping between the three-dimensional coordinate of demarcating the thing unique point and the two dimensional image projection, the tradition calibration technique can access result more accurately, but operation inconvenience, can only be in some application of special occasions; Initiatively calibration technique is finished camera calibration by the special geometric scheme of projection in scene by the contact between geometric properties and the image.The advantage of this method has been to avoid to demarcate the use of thing, and can recover camera parameters accurately by accurate projection, and shortcoming is to have added projecting cell, has increased the equipment complexity, does not have versatility.Obtain the camera self-calibration technology of broad research in recent years, utilize the constraint of video camera confidential reference items to calculate corresponding focus of camera value, this technology does not need the information of scene and camera motion, can be in various image sequences flexible Application.
The camera self-calibration Study on Technology starts from early 1990s, at first by Faugeras, Luong and Maybank propose from the notion of demarcating, and introduce the Kruppa equation in the research of self-calibrating method in early days, utilize absolute conic and polar curve conversion to set up system of equations between the image, last directly solving equation group obtains the intrinsic parameter of video camera.But there is polysemy in separating that the method that is based on the Kruppa equation obtains, and directly finds the solution relatively difficulty of equation of higher degree group.Bill Triggs proposed based on the quadric self-calibrating method of absolute antithesis in 97 years.This method comprises the parameter of camera intrinsic parameter and plane at infinity simultaneously, utilizes camera intrinsic parameter constraint Simultaneous Equations to find the solution.This method is compared the Kruppa equation and is had stronger constraint, and can find the solution by linear and non-linear method, all has raising, shortcoming to be to require in the solution procedure positive semi-definite constraint often to be difficult to satisfy on operability and accuracy.
Summary of the invention
The present invention is intended to one of solve the problems of the technologies described above at least.
For this reason, one object of the present invention is to propose a kind of real-time method that calculates focal length of camera from image sequence, this method has solved camera self-calibration deficiency in actual applications, has realized fast and accurately the estimation to the focal length of camera parameter.
For achieving the above object, the present invention proposes a kind of real-time method that from image sequence, calculates focal length of camera, may further comprise the steps: from image sequence, choose adjacent two images; It is right to obtain match point that described two images are carried out Feature Points Matching; To any seven groups of match points of described match point centering to finding the solution to obtain a plurality of feasible solutions of focal length; The assessment described each feasible solution accuracy and the weight of assessed value as described each feasible solution; According to described weight described each feasible solution is calculated weighted sum obtains focal length with assessment probability density function; On the probability density function camber of described focal length, repeatedly sample to obtain a plurality of sampled points; Utilization Gaussian function match described sampled point, and with described peak of function as final focal length value.
In one embodiment of the invention, described two images are carried out Feature Points Matching to obtain match point to further comprising: as feature, choose algorithm picks certain characteristics point with the utilization unique point with the colouring information of image; Find some groups of match points right according to described unique point in described two images, wherein, it is right in matching process two the most close unique points of color characteristic to be formed one group of match point.
In one embodiment of the invention, two unique points of described each group match point centering are the same some projection result on two images respectively in the scene.
In one embodiment of the invention, described any seven groups of match points of described match point centering are further comprised a plurality of feasible solutions of finding the solution to obtain focal length: select seven groups of match points right at random from all match point centerings of described two images; Described seven groups of match points are correlated with the mathematics equation solution to obtain a polynary equation of higher degree group to substitution; Find the solution described polynary equation of higher degree group to obtain several feasible solutions of focal length.
In one embodiment of the invention, after obtaining several feasible solutions of focal length, also comprise: select other seven groups of match points right at random from the residue match point centering of described two images; Described other seven groups of match points are correlated with the mathematics equation solution to obtain a polynary equation of higher degree group to substitution; Find the solution described polynary equation of higher degree group to obtain several feasible solutions of focal length once more.
In one embodiment of the invention, up to not having abundant residue match point or find the solution to stop to carry out and exporting all feasible solutions after reaching pre-determined number.
In one embodiment of the invention, the accuracy of described each feasible solution of assessment and the weight of assessed value as each feasible solution further comprised: the right residue match point of described image in the described corresponding feasible solution of substitution, is verified as and judges the described correctness that may connect described residue match point is carried out related constraint; The coupling that satisfies constraint is counted in the ratio of all the match point centerings weight as described feasible solution.
In one embodiment of the invention, the described weight of described foundation further comprises with the probability density function that assessment obtains focal length described each feasible solution calculating weighted sum: the stochastic variable that described focal length is designated as a two dimension; Described each feasible solution corresponds to a kernel function on the probability space; On probability space described each kernel function is weighted summation, to obtain the probability density function of focal length, wherein, the weight of described each kernel function is the weight of corresponding described feasible solution.
In one embodiment of the invention, the value of described each sampled point is the probability of occurrence of corresponding feasible solution.
In one embodiment of the invention, the described sampled point of utilization Gaussian function match, and serve as that the described sampling point set of utilization is combined in and simulates Gauss's curved surface in the described probability space as final focal length value with described peak of function, wherein, the value of the two-dimensional random variable of described Gauss's curved surface peak value correspondence is the focal length value of corresponding two image camera.
Another aspect of the present invention has proposed a kind of device that calculates focal length of camera from image sequence, and comprising: image is chosen module, is used for choosing adjacent two images from image sequence; The images match module, it is right to obtain match point to be used for that described two images are carried out Feature Points Matching; The focal length computing module is used for any seven groups of match points of described match point centering finding the solution to obtain a plurality of feasible solutions of focal length; The weight evaluation module is used to assess the accuracy of described each feasible solution and the weight of assessed value as described each feasible solution; The probability density function constructing module is used for according to described weight described each feasible solution being calculated weighted sum and obtains the probability density function of focal length with assessment, and repeatedly samples on the probability density function camber of described focal length to obtain a plurality of sampled points; With the Gaussian function constructing module, be used to use the described sampled point of Gaussian function match, and with described peak of function as final focal length value.
The real-time method that calculates focal length of camera from image sequence by the present invention proposes not only solved camera self-calibration deficiency in actual applications, and realized fast and accurately the estimation to the focal length of camera parameter.
Aspect that the present invention adds and advantage part in the following description provide, and part will become obviously from the following description, or recognize by practice of the present invention.
Description of drawings
Above-mentioned and/or additional aspect of the present invention and advantage are from obviously and easily understanding becoming the description of embodiment below in conjunction with accompanying drawing, wherein:
Fig. 1 is the process flow diagram that calculates the focal length of camera real-time method from image sequence of the embodiment of the invention;
Fig. 2 is that the unique point of image sequence of the embodiment of the invention and match point are to synoptic diagram;
Fig. 3 is the synoptic diagram that the focal length probability density function is estimated in the nonparametric technique of the embodiment of the invention;
Fig. 4 is the synoptic diagram to the probability density function resampling of the embodiment of the invention;
Fig. 5 carries out the Gauss curve fitting synoptic diagram for the embodiment of the invention to resample points; With
Fig. 6 is the structure drawing of device that calculates focal length of camera from image sequence of the embodiment of the invention.
Embodiment
Describe embodiments of the invention below in detail, the example of described embodiment is shown in the drawings, and wherein identical from start to finish or similar label is represented identical or similar elements or the element with identical or similar functions.Below by the embodiment that is described with reference to the drawings is exemplary, only is used to explain the present invention, and can not be interpreted as limitation of the present invention.
The present invention be directed to the deficiency in the existing camera self-calibration technology, and a kind of real-time method that calculates focal length of camera from image sequence that proposes can estimate the focal length of camera parameter value fast and accurately by this method.
In order clearer understanding to be arranged to the real-time method that from image sequence, calculates focal length of camera that the embodiment of the invention proposes, below make concise and to the point description with regard to this method: this method is at first selected and the matching characteristic point adjacent two images, and picked at random wherein seven groups of match points to finding the solution a plurality of feasible solutions that obtain focal length, then all possible separating carried out the accuracy assessment, and assessed value is used for non-parametric estmation as weight, so that obtain the probability density function of focal length, on probability density function, resample at last, and utilize new sampled point to carry out Gauss curve fitting, further the peak value of function after the match finally separating as focal length of camera.Be the focal length of camera parameter value that the embodiment of the invention proposes.
Particularly, as shown in Figure 1, the process flow diagram that calculates the real-time method of focal length of camera from image sequence for the embodiment of the invention may further comprise the steps:
Step S101 chooses adjacent two images from image sequence.More specifically, the image in the described image sequence is numbered, for example: can be designated as I
1, I
2, I
3... can certainly be designated as any type of mark that other can separate image area.After finishing, numbering at first chooses I
1, I
2The method that two images use the present invention to propose is carried out finding the solution of focal length of camera, chooses I after finishing finding the solution
3, I
4Find the solution, by that analogy, the focal length of all images correspondence has all utilized the method that is proposed by invention to find the solution to finish in sequence.(following with I
1, I
2Be example).
Step S102, it is right to obtain match point that described two images are carried out Feature Points Matching.
2.1, with the colouring information of image as feature, utilize image characteristic point extraction algorithm SIFT algorithm from two images, to extract certain characteristics point (as the angle point of image, flex point etc.) respectively, and each unique point is write down its pixel coordinate in image, be designated as (x, y) and one 128 the dimension eigenvector, eigenvector is the local color feature under a plurality of metric spaces.
2.2, for I
1In each unique point, be designated as p1, at I
2In all unique points, be designated as { p
i.At { p
iIn find unique point p2, make that the eigenvector of p2 and the distance of eigenvector on theorem in Euclid space of p1 are nearest in all unique points.Therefore, (p1, it is right p2) to form a match point, in one embodiment of the invention this match point to being designated as m.
In conjunction with Fig. 2, for the unique point of the image sequence of the embodiment of the invention and match point to synoptic diagram, can be clear that from Fig. 2 match point is right, can also find out that from figure the same point when being shooting in the scene is the result of projection on two images respectively for two unique points of each group match point centering.
Step S103, to any seven groups of match points of described match point centering to finding the solution to obtain a plurality of feasible solutions of focal length.
3.1, select seven groups of match points right at random from all match point centerings of two images, be designated as m
1, m
2... m
7
3.2, for each the group match point in 3.1 to m
iIn unique point, be designated as p
I1, p
I2, all corresponding satisfied following equation:
p
i2 T*F*p
i1＝0 ...(1)
P wherein
I1=(x
I1, y
I1, 1), p
I2=(x
I2, y
I2, 1), F is the unknown matrix of a 3*3.And with seven groups of match points in the substitution following formula, can obtain the system of linear equations of forming by seven equations.
3.3, the system of linear equations in 3.2 is further found the solution the general solution F that obtains F
1, F
2, satisfy F=x*F
1+ F
2, wherein x is unknown coefficient;
Next, the expression formula with above-mentioned F is updated in the following equation (matrix form is represented):
det(F)＝0 ...(2)
2FQF
TQF-trace(FQF
TQ)F＝0 ...(3)
Wherein det (F) represents the F determinant of a matrix, and the mark of trace (.) representing matrix, Q matrix are diagonal matrix diag ([111/ (f
1* f
2)]), f
1, f
2Focal length for the video camera correspondence;
Obtain the system of equations formed by ten polynary equations of higher degree by (2) (3) formula, find the solution this system of equations, obtain a plurality of separating, be designated as { S
i.Wherein each separates the focal distance f that has comprised two images
1, f
2, they all are correct separating on the mathematical meaning, because all satisfy above-mentioned polynary equation of higher degree group, but right because the match point of mistake in matching process, may occur, so can not guarantee that in real image each is separated all is correct separating on the physical significance.
3.4,3.1,3.2,3.3 circulations are carried out some times, so that obtain more feasible solutions of focal length.
Step S104, the assessment described each feasible solution accuracy and the weight of assessed value as described each feasible solution.
4.1, to each feasible solution S
i, can obtain a matrix, be designated as F, with other all match points to being updated in the formula (1) in 3.2 with this F matrix, the match point that satisfies equation to calling S
iSupport point.For a feasible solution, support point is many more, shows that then the correctness of this feasible solution is high more.
4.2,, be designated as w supporting to count at the ratio of all match point centerings weighted value as this feasible solution
i
Step S105 calculates weighted sum obtains focal length with assessment probability density function according to described weight to described each feasible solution.
5.1, at first focus of camera is designated as the stochastic variable (f of a two dimension
1, f
2).
5.2, separate among the step S104 each, a corresponding kernel function on the probability space has been used gaussian kernel function in one embodiment of the invention, as shown in Figure 3, estimate the synoptic diagram of focal length probability density function for the nonparametric technique of the embodiment of the invention.From Fig. 3 can be able to see the functional digraph of probability density function of focal length of the embodiment of the invention and the functional digraph of gaussian kernel function.(for demonstration directly perceived, the synoptic diagram of the one-dimensional case of having drawn, two-dimensional case is identical, just transfers curved surface to from curve).
5.3, on probability space, each kernel function in 5.2 is weighted summation, obtain the probability density function of focal length, be designated as p (f
1, f
2).The weight of each kernel function is the weight w of the feasible solution in corresponding 4.2
i
Step S106 repeatedly samples on the probability density function camber of described focal length to obtain a plurality of sampled points.Particularly,, sample to obtain a sampled point (sample point), can access all sampled points (sample point) of all feasible solution correspondences like this in the position of each feasible solution for the probability density function camber that weighted sum obtains.The value of each sampled point (sample point) is to probability of occurrence that should feasible solution.More specifically, as shown in Figure 4, be the synoptic diagram that probability density function is repeatedly sampled of the embodiment of the invention (for demonstration directly perceived, the synoptic diagram of the one-dimensional case of having drawn, two-dimensional case is identical, just transfers curved surface to from curve).In Fig. 4, can obviously see a plurality of sampled points (sample point).The probability density function camber that obtains for weighted sum is designated as p (f
1, f
2), at each feasible solution (f
I1, f
I2) the position sample, obtain corresponding sampled point (sample point), each sampled point (sample point), i.e. (f
I1, f
I2, p
i) p
iThe corresponding probability of occurrence of this feasible solution.
Step S107, the utilization described sampled point of Gaussian function match (sample point), and with described peak of function as final focal length value.For the present invention there being clearer understanding,, resample points is carried out Gauss curve fitting synoptic diagram (for demonstration directly perceived, the synoptic diagram of the one-dimensional case of having drawn, two-dimensional case is identical, just transfers curved surface to from curve) for the embodiment of the invention in conjunction with Fig. 5.Be not difficult to find out, from this Fig. 5, can obviously see the Gaussian function image of the embodiment of the invention, from sampled point image and the final focal length value of estimating.Particularly:
7.1, new sample point the set { (f among the utilization step S106
I1, f
I2, p
i) in probability space, simulate Gauss's curved surface.
7.2 the value of the two-dimensional random variable of the curved surface of Gauss described in 7.1 peak value correspondence is respectively the focal length value f of two image correspondences
1, f
2
The invention allows for a kind of device that from image sequence, calculates focal length of camera, as shown in Figure 6, be the structure drawing of device that from image sequence, calculates focal length of camera of the embodiment of the invention.The device 100 that should be from image sequence calculates focal length of camera comprises that image chooses module 110, images match module 120, focal length computing module 130, weight evaluation module 140, probability density function constructing module 150 and Gaussian function constructing module 160.Wherein, image is chosen module 110, is used for choosing adjacent two images from image sequence; Images match module 120, it is right to obtain match point to be used for that described two images are carried out Feature Points Matching; Focal length computing module 130 is used for any seven groups of match points of described match point centering finding the solution to obtain a plurality of feasible solutions of focal length; Weight evaluation module 140 is used to assess the accuracy of described each feasible solution and the weight of assessed value as described each feasible solution; Probability density function constructing module 150 is used for according to described weight described each feasible solution being calculated weighted sum and obtains the probability density function of focal length with assessment, and repeatedly samples on the probability density function camber of described focal length to obtain a plurality of sampled points; With Gaussian function constructing module 160, be used to use the described sampled point of Gaussian function match, and with described peak of function as final focal length value.
But skilled in the art will recognize that, described probability density function constructing module 150 can be refined as the more specifically a plurality of functional modules of function, as: be used for according to as described in weight to as described in each feasible solution calculate the functional module of weighted sum obtains focal length with assessment probability density function and be used for as described in sample to obtain the functional module of sampled point on the probability density function camber of focal length.These also should belong within protection scope of the present invention.
The real-time method that calculates focal length of camera from image sequence by the present invention proposes not only solved camera self-calibration deficiency in actual applications, and realized fast and accurately the estimation to the focal length of camera parameter.
Although illustrated and described embodiments of the invention, for the ordinary skill in the art, be appreciated that without departing from the principles and spirit of the present invention and can carry out multiple variation, modification, replacement and modification that scope of the present invention is by claims and be equal to and limit to these embodiment.
Claims (13)
1. a real-time method that calculates focal length of camera from image sequence is characterized in that, may further comprise the steps:
From image sequence, choose adjacent two images;
It is right to obtain match point that described two images are carried out Feature Points Matching;
To any seven groups of match points of described match point centering to finding the solution to obtain a plurality of feasible solutions of focal length;
The assessment described each feasible solution accuracy and the weight of assessed value as described each feasible solution;
According to described weight described each feasible solution is calculated weighted sum obtains focal length with assessment probability density function;
On the probability density function camber of described focal length, repeatedly sample to obtain a plurality of sampled points;
Utilization Gaussian function match described sampled point, and with described peak of function as final focal length value.
2. the real-time method that calculates focal length of camera from image sequence as claimed in claim 1 is characterized in that, described two images is carried out Feature Points Matching to obtain match point to further comprising:
As feature, choose algorithm picks certain characteristics point with the colouring information of image with the utilization unique point;
Find some groups of match points right according to described unique point in described two images, wherein, it is right in matching process two the most close unique points of color characteristic to be formed one group of match point.
3. the real-time method that calculates focal length of camera from image sequence as claimed in claim 2 is characterized in that, two unique points of described each group match point centering are the same some projection result on two images respectively in the scene.
4. the real-time method that calculates focal length of camera from image sequence as claimed in claim 1 is characterized in that, described any seven groups of match points of described match point centering is further comprised a plurality of feasible solutions of finding the solution to obtain focal length:
Select seven groups of match points right at random from all match point centerings of described two images;
Described seven groups of match points are correlated with the mathematics equation solution to obtain a polynary equation of higher degree group to substitution;
Find the solution described polynary equation of higher degree group to obtain several feasible solutions of focal length.
5. the real-time method that calculates focal length of camera from image sequence as claimed in claim 4 is characterized in that, also comprises after obtaining several feasible solutions of focal length:
Select other seven groups of match points right at random from the residue match point centering of described two images;
Described other seven groups of match points are correlated with the mathematics equation solution to obtain a polynary equation of higher degree group to substitution;
Find the solution described polynary equation of higher degree group to obtain several feasible solutions of focal length once more.
6. the real-time method that calculates focal length of camera from image sequence as claimed in claim 5 is characterized in that, up to not having abundant residue match point or find the solution to stop to carry out and exporting all feasible solutions after reaching pre-determined number.
7. the real-time method that calculates focal length of camera from image sequence as claimed in claim 1 is characterized in that, the accuracy of described each feasible solution of assessment and the weight of assessed value as each feasible solution further comprised:
The right residue match point of described image in the described corresponding feasible solution of substitution, is verified as and judges the described correctness that may connect described residue match point is carried out related constraint;
The coupling that satisfies constraint is counted in the ratio of all the match point centerings weight as described feasible solution.
8. the real-time method that calculates focal length of camera from image sequence as claimed in claim 7 is characterized in that, the described weight of described foundation is calculated weighted sum to described each feasible solution and further comprised with the probability density function that assessment obtains focal length:
Described focal length is designated as the stochastic variable of a two dimension;
Described each feasible solution corresponds to a kernel function on the probability space;
On probability space described each kernel function is weighted summation, to obtain the probability density function of focal length, wherein, the weight of described each kernel function is the weight of corresponding described feasible solution.
9. the real-time method that calculates focal length of camera from image sequence as claimed in claim 8 is characterized in that the value of described each sampled point is the probability of occurrence of corresponding feasible solution.
10. the real-time method that from image sequence, calculates focal length of camera as claimed in claim 9, it is characterized in that, the described sampled point of utilization Gaussian function match, and serve as that the described sampling point set of utilization is combined in and simulates Gauss's curved surface in the described probability space as final focal length value with described peak of function, wherein, the value of the two-dimensional random variable of described Gauss's curved surface peak value correspondence is the focal length value of corresponding two image camera.
11. a device that calculates focal length of camera from image sequence is characterized in that, comprising:
Image is chosen module, is used for choosing adjacent two images from image sequence;
The images match module, it is right to obtain match point to be used for that described two images are carried out Feature Points Matching;
The focal length computing module is used for any seven groups of match points of described match point centering finding the solution to obtain a plurality of feasible solutions of focal length;
The weight evaluation module is used to assess the accuracy of described each feasible solution and the weight of assessed value as described each feasible solution;
The probability density function constructing module is used for according to described weight described each feasible solution being calculated weighted sum and obtains the probability density function of focal length with assessment, and repeatedly samples on the probability density function camber of described focal length to obtain a plurality of sampled points; With
The Gaussian function constructing module is used to use the described sampled point of Gaussian function match, and with described peak of function as final focal length value.
12. the device that calculates focal length of camera from image sequence as claimed in claim 11 is characterized in that, two unique points of described each group match point centering are the same some projection result on two images respectively in the scene.
13. the device that calculates focal length of camera from image sequence as claimed in claim 11 is characterized in that, up to not having abundant residue match point or find the solution to stop to carry out and exporting all feasible solutions after reaching pre-determined number.
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