CN102063727B - Covariance matching-based active contour tracking method - Google Patents
Covariance matching-based active contour tracking method Download PDFInfo
- Publication number
- CN102063727B CN102063727B CN201110003293A CN201110003293A CN102063727B CN 102063727 B CN102063727 B CN 102063727B CN 201110003293 A CN201110003293 A CN 201110003293A CN 201110003293 A CN201110003293 A CN 201110003293A CN 102063727 B CN102063727 B CN 102063727B
- Authority
- CN
- China
- Prior art keywords
- template
- covariance
- level set
- image
- active contour
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Landscapes
- Image Analysis (AREA)
Abstract
The invention relates to a covariance matching-based active contour tracking method and belongs to the technical field of visual tracking. In the covariance matching-based active contour tracking method, an image area energy term is modeled by using non-Euclidean geometry. The method comprises the following steps of: manually initializing a curve surrounding an objective and establishing a covariance matrix as a template of an objective contour for an area surrounded by the curve in a first frame; after the contour of the objective is obtained, recording a level set function value of the template to make preparation for a prior shape and calculating a symbolized distance function of the template; from the image of the next frame, deducing a gradient descent flow from a result of the previous frame according to the established energy functional and updating the level set function; and checking whether iteration stops or not. In the method, the tracking result is more accurate; meanwhile, the covariance matrix is used as an area descriptor and all kinds of information in an image sequence and the correlation between all kinds of information are considered comprehensively, and the method does not depend on foreground and background information distribution, so that the tracking method has universality.
Description
Technical field
The present invention relates to a kind of active contour tracing method, belong to the vision track technical field based on the covariance coupling.
Background technology
Motion target tracking is one of classical problem of computer vision field, has important use to be worth.In the real world applications, owing to image quality is not good, ambient lighting variation, shade, block with reasons such as target distortion and make tracking problem become a generally acknowledged challenging difficult problem.
Visual target tracking can be divided into following several big classifications: point target is followed the tracks of, and nuclear is followed the tracks of and profile is followed the tracks of.Point target is followed the tracks of and is adopted one or more points to come the modeling sensation target; Tracking based on nuclear has the advantage of computing simple and fast; In vision track, obtained in recent years to use widely; But target shape was expressed and is usually adopted such as ellipse in the middle of nuclear was followed the tracks of, and therefore simple geometric primitives such as rectangle are difficult to accurately extract Amoebida target complex outline.And the accurate extraction of sensation target shape is above-mentioned many key in application place of mentioning, has only profile to follow the tracks of curve tracking in other words and can provide this information.
The basic thought that skeleton pattern is followed the tracks of is to be plane curve with the Target Modeling in the sequence image---the active profile of promptly evolving, according to the correlativity of video information on room and time, confirm position and the attitude of target in video.Can skeleton pattern be divided into two types on parameter skeleton pattern and geometric profile model, the former curve representation has adopted the explicit parament description, and the latter then is embedded in the higher-dimension level of function with curve representation and concentrates (Level set).The former advantage is that computational complexity is lower, and shortcoming is not have topological adaptivity, and the latter's advantage is to possess topological adaptivity and calculation stability, and deficiency is that calculation cost is higher.
Terzopoulos has defined kinetic energy, potential energy, damping term respectively from the lagrangian dynamics principle; And the dynamic deformation model of deriving unified shape and moving and describe; Be expressed as that become and tool inertia for the moment dynamic outline, the equation of motion itself has been expressed a kind of follow-up mechanism based on dynamic balance, and shape constraining is general flatness constraint; Shortcoming is that system's dimension is too high, receives noise effect easily; Peterfreund has proposed the speed snake, and light stream estimation, active contour model, Kalman filtering are combined, and can effectively handle noise to a certain extent, block and complex background, and deficiency is that the distortion dimension is too high, calculation of complex; In order to solve the profile tracking problem of multi-peak observation model under the complex backgrounds such as foreign material; Isard has proposed the conditional probability propagation algorithm; Be the Condensation algorithm, the conditional probability propagation algorithm combines Stochastic Dynamics model and weight sampling along with advancing of time propagated about the complete probability distribution of shape and position, the target travel of the wave filter that is produced under can the tracking complex background of stalwartness; The deficiency of Condensation algorithm is: compare with kalman filter method; Calculation cost is still too high, if the measurement model modeling is unreasonable, then increases sample number in any case and also can not improve tracking performance.
The geometric profile model is proposed respectively by Malladi and Caselles the earliest, and thought comes from solution that Oshier and Sethian propose with the Level Set Method of curvature relevant speed along the border propagation problem that normal direction is advanced.Curve is the zero level set of higher-dimension function, and curve evolution is evolved by the higher-dimension function and tried to achieve.The equation of motion that produces can approach calculating by the finite difference method that satisfies entropy condition, and the speed term that from image, obtains is used for the propagation in image border termination border.
Level Set Method is to be propagated in the research by the interface that J.Sethian and S.Osher propose progressively to grow up the earliest, and it is to handle sealing moving interface effective computational tool of how much change in topology in the evolutionary process in time.The basic thought of Level Set Method is the level set that is expressed as two-dimentional toroidal function with plane closed curve implicit expression, and the point set that promptly has the same functions value is through the motion of finding the solution curve of curved surface evolution implicit expression.The advantage of this method is the processing changes of topology structure that it can nature, and stable numerical algorithm is provided.
The basic process of covariance track algorithm is following:
1) to each two field picture of video sequence, extracts corresponding characteristic pattern.To an any given two field picture I (x, y) (image can be one dimension gray level image, three-dimensional color image or the like) established its width and highly is respectively W and H; To figure in each pixel, for its set up a proper vector f (x, y); It can be by pixel coordinate, image color or half-tone information, the image gradient of image; Elements such as image edge direction constitute, and for example, can get
remember it dimension be the d dimension; Thus; Can from image I (x, extract in y) a W * H * d dimension characteristic pattern F (x, y)
F(x,y)=(I,x,y)
Obviously, f (x, y) ∈ F (x, y).
2), set up a d * d dimension covariance matrix C to target area R given in the image
RAs region description
3) a given template covariance matrix C
TCovariance matrix C with the present frame candidate target region
R, can find correct target area through the two covariance distance of minimization.
Summary of the invention
The objective of the invention is on the basis that existing active profile is followed the tracks of; The characteristic covariance matrix is sub as region description; Proposed a kind of active contour tracing method, utilized non-euclidean geometry modeling image-region energy term, made tracking results more accurate based on the covariance coupling.
The objective of the invention is to realize through following technical proposals.
A kind of active contour tracing method of the present invention based on the covariance coupling, the detailed process of this method is:
When template:
Utilize non-euclidean geometry method to establish the image energy model under the level set framework of template, derive the gradient katabatic drainage, upgrade level set function then according to the image energy model functional of setting up;
Image energy model functional under the level set framework is defined as:
λ
1, λ
2Be the weight of control prospect and background information, C
R(φ) with
The covariance of representing inside and outside zone of curve respectively, C
TThe covariance of representation template; Obviously, minimization energy functional ρ
I, 1The covariance that (φ) just means minimization candidate target region and template is apart from the covariance distance of candidate background zone with template that maximize simultaneously; Complete energy functional is:
ρ(φ)=λ
1ρ
i,11(φ)-λ
2ρ
i,12(φ)+αρ
s(φ)+βρ
r(φ)
ρ in the formula
I, 11(φ), ρ
I, 12(φ), ρ
s(φ), ρ
r(φ) difference representative image energy foreground items, image energy background item, shape energy term and punishment energy term, λ
1, λ
2, α, β representes weight.
Concrete steps are following:
1) at first frame, manually the curve of target is surrounded in initialization, for the curve area surrounded is set up the template of covariance matrix as objective contour;
2) after the profile that has obtained target, the level set function value of logging template is that prior shape is prepared and the symbolism distance function of calculation template;
3) begin from the next frame image,, derive the gradient katabatic drainage, upgrade level set function then according to the energy functional of setting up by the result of previous frame;
Whether 4) detect iteration stops; Here the iteration stop criterion that adopts: (a) distance between the covariance matrix of the covariance matrix of the current zero level collection of calculating inside and priori objective contour inside; If less than a threshold value, iteration stops, otherwise continues iteration; (b) restriction of iterations;
5) forward step 3) to.
Above-mentioned steps 2) can also do following processing before: because possibly there is error in manual selection, can utilize traditional geometry active contour model (like the C-V model), make profile more near real target to the objective contour dividing processing.
When template does not exist:
Our can directly maximize degree of not matching of prospect covariance matrix and background covariance matrix, thus realize target following based on image segmentation.Thus, our second kind of image energy model may be defined as
According to following formula, when the covariance distance in candidate target region and candidate background zone reaches maximum, just can the target area be extracted from the background area.
Concrete steps are following:
1) read picture, the curve of target is surrounded in manual or semi-automatic initialization, and curve is rule or irregular, and can not be too far away from target;
2) be respectively the inside and outside covariance matrix of setting up of curve, and the compute sign distance function;
3) by the iteration result of last time,, upgrade level set function according to setting up the gradient katabatic drainage that energy functional is derived;
4) detect iteration and whether stop, the iteration stop criterion that adopts here: (a) distance between the covariance matrix of the covariance matrix of the current zero level collection of calculating inside and outside, if greater than a threshold value, iteration stops, otherwise continues iteration; (b) restriction of iterations (the capping number of times guarantees travelling speed).
5) forward step 3) to.
Beneficial effect
Algorithm proposed by the invention utilizes the image energy quantifier in the logarithm euclidian metric criterion modeling active profile trace model; This makes tracking results more accurate; Sub with covariance matrix simultaneously as region description; Taken all factors into consideration various information in the image sequence and correlativity each other, do not relied on distribution preceding, background information, made tracking have more universality.
Description of drawings
Fig. 1 is the tracking results figure of the embodiment of the invention.
Embodiment
Below in conjunction with embodiment the application of present technique scheme is specified, and provide the result.
Embodiment
Active contour tracing method based on the covariance coupling;
Adopting certain street automobile image sequence is tested object, and resolution is 180x135, whenever follows the tracks of once at a distance from a frame; Total tracking frame number is 17 frames, and the extracting waste car is as the target of following the tracks of, and the characteristics of this sequence are; There is certain rotational transform in the shape of target; In the automobile turning process, utilize this method not only can converge to the border of target accurately, the angle that can also estimate simultaneously to rotate, change in size etc.
Concrete steps are following:
1) at first frame, manually the curve of white car is surrounded in initialization, and for the curve area surrounded is set up the template of covariance matrix as white car profile, shown in (a) among Fig. 1, the symbolism distance function of template is shown in (b) among Fig. 1;
2) because there is error in manual the selection, utilize the C-V model, make profile more near real target to the objective contour dividing processing;
3) after the profile that has obtained white car, the level set function value of logging template is that prior shape is prepared and the symbolism distance function of calculation template;
4) begin from the next frame image,, derive the gradient katabatic drainage, upgrade level set function then according to the energy functional of setting up by the result of previous frame;
Whether 5) detect iteration stops; Here the iteration stop criterion that adopts: (a) distance between the covariance matrix of the covariance matrix of the current zero level collection of calculating inside and priori objective contour inside; If less than a threshold value, iteration stops, otherwise continues iteration; (b) restriction of iterations;
6) forward step 4) to;
The tracking results of the 5th frame that obtains according to said method, the 7th frame, the 9th frame, the 11st frame, the 13rd frame, the 17th frame like (c) among Fig. 1 to shown in (h).
Above-described specific descriptions; Purpose, technical scheme and beneficial effect to invention have carried out further explain, and institute it should be understood that the above is merely specific embodiment of the present invention; And be not used in qualification protection scope of the present invention; All within spirit of the present invention and principle, any modification of being made, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (3)
1. the active contour tracing method based on covariance coupling is characterized in that: establish the level set framework image energy model functional down of template, derive the gradient katabatic drainage according to the image energy model functional of foundation, upgrade level set function then;
Have the image energy model functional under the level set framework of template to be defined as:
λ
1, λ
2Be the weight of control prospect and background information, C
R(φ) with
The covariance of representing inside and outside zone of curve respectively, C
TThe covariance of representation template; Minimization energy functional ρ
I, 1(φ) be the covariance distance of the covariance of minimization candidate target region and template apart from maximize simultaneously candidate background zone and template;
Concrete steps are following:
1) at first frame, manually the curve of target is surrounded in initialization, sets up the covariance matrix of template for the curve area surrounded, utilizes how much active contour models to the objective contour dividing processing then, makes profile near real target;
2) after the profile that has obtained target, the level set function value of logging template and the symbolism distance function of calculation template;
3) begin from the next frame image,, derive the gradient katabatic drainage, upgrade level set function then according to the image energy model functional of setting up by the result of previous frame;
4) detect iteration and whether stop, the iteration stop criterion that adopts here: (a) distance between the covariance matrix of the covariance matrix of the current zero level collection of calculating inside and template, if less than a threshold value, iteration stops, otherwise continues iteration; (b) restriction of iterations;
5) forward step 3) to.
2. a kind of active contour tracing method based on covariance coupling according to claim 1 is characterized in that: utilize non-euclidean geometry method to establish the image energy model functional under the level set framework of template.
3. a kind of active contour tracing method based on the covariance coupling according to claim 2 is characterized in that: how much active contour models are the C-V model.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201110003293A CN102063727B (en) | 2011-01-09 | 2011-01-09 | Covariance matching-based active contour tracking method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201110003293A CN102063727B (en) | 2011-01-09 | 2011-01-09 | Covariance matching-based active contour tracking method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN102063727A CN102063727A (en) | 2011-05-18 |
CN102063727B true CN102063727B (en) | 2012-10-03 |
Family
ID=43998991
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201110003293A Expired - Fee Related CN102063727B (en) | 2011-01-09 | 2011-01-09 | Covariance matching-based active contour tracking method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN102063727B (en) |
Families Citing this family (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102322826A (en) * | 2011-05-24 | 2012-01-18 | 上海瑞伯德智能系统科技有限公司 | A kind of improved measuring method of object dimensional surface data |
CN102663755B (en) * | 2012-04-18 | 2014-07-02 | 北京理工大学 | Method for cutting nuclear magnetic resonance image with uniform gray levels |
CN102881002B (en) * | 2012-07-11 | 2014-12-17 | 天津大学 | Video background recovery method based on movement information and matrix completion |
CN104077784B (en) * | 2013-03-29 | 2018-02-27 | 联想(北京)有限公司 | Extract the method and electronic equipment of destination object |
JP5741660B2 (en) * | 2013-09-18 | 2015-07-01 | カシオ計算機株式会社 | Image processing apparatus, image processing method, and program |
CN103903280B (en) * | 2014-03-28 | 2017-01-11 | 哈尔滨工程大学 | Subblock weight Mean-Shift tracking method with improved level set target extraction |
CN106023155B (en) * | 2016-05-10 | 2018-08-07 | 电子科技大学 | Online target profile tracing method based on level set |
CN108509866B (en) * | 2018-03-12 | 2020-06-19 | 华南理工大学 | Face contour extraction method |
CN109815791B (en) * | 2018-12-13 | 2021-02-19 | 北京理工大学 | Blood vessel-based identity recognition method and device |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101281648A (en) * | 2008-04-29 | 2008-10-08 | 上海交通大学 | Method for tracking dimension self-adaption video target with low complex degree |
CN101408983A (en) * | 2008-10-29 | 2009-04-15 | 南京邮电大学 | Multi-object tracking method based on particle filtering and movable contour model |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20070237359A1 (en) * | 2006-04-05 | 2007-10-11 | Zehang Sun | Method and apparatus for adaptive mean shift tracking |
-
2011
- 2011-01-09 CN CN201110003293A patent/CN102063727B/en not_active Expired - Fee Related
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101281648A (en) * | 2008-04-29 | 2008-10-08 | 上海交通大学 | Method for tracking dimension self-adaption video target with low complex degree |
CN101408983A (en) * | 2008-10-29 | 2009-04-15 | 南京邮电大学 | Multi-object tracking method based on particle filtering and movable contour model |
Non-Patent Citations (2)
Title |
---|
Fatih Porikli et al..Covariance Tracking using Model Update Based on Lie Algebra.《Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition》.2006,全文. * |
Oncel Tuzel et al..Region Convariance:A Fast Descriptor for Detection and Classification.《LNCS》.2006,第3952卷全文. * |
Also Published As
Publication number | Publication date |
---|---|
CN102063727A (en) | 2011-05-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN102063727B (en) | Covariance matching-based active contour tracking method | |
CN103310194B (en) | Pedestrian based on crown pixel gradient direction in a video shoulder detection method | |
CN104200485A (en) | Video-monitoring-oriented human body tracking method | |
CN109974743B (en) | Visual odometer based on GMS feature matching and sliding window pose graph optimization | |
CN110688965B (en) | IPT simulation training gesture recognition method based on binocular vision | |
CN103886325B (en) | Cyclic matrix video tracking method with partition | |
CN106780560B (en) | Bionic robot fish visual tracking method based on feature fusion particle filtering | |
CN105869178A (en) | Method for unsupervised segmentation of complex targets from dynamic scene based on multi-scale combination feature convex optimization | |
CN103514441A (en) | Facial feature point locating tracking method based on mobile platform | |
US10249046B2 (en) | Method and apparatus for object tracking and segmentation via background tracking | |
CN101996401A (en) | Target analysis method and device based on intensity image and range image | |
CN109934224A (en) | Small target detecting method based on markov random file and visual contrast mechanism | |
CN101408983A (en) | Multi-object tracking method based on particle filtering and movable contour model | |
CN107895379A (en) | The innovatory algorithm of foreground extraction in a kind of video monitoring | |
CN102156995A (en) | Video movement foreground dividing method in moving camera | |
CN103903280A (en) | Subblock weight Mean-Shift tracking method with improved level set target extraction | |
CN106023245A (en) | Static background moving object detection method based on neutrosophy set similarity measurement | |
CN101976504A (en) | Multi-vehicle video tracking method based on color space information | |
CN103077531A (en) | Grayscale target automatic tracking method based on marginal information | |
CN103871062A (en) | Lunar surface rock detection method based on super-pixel description | |
CN104537686A (en) | Tracing method and device based on target space and time consistency and local sparse representation | |
CN106127112A (en) | Data Dimensionality Reduction based on DLLE model and feature understanding method | |
CN110991398A (en) | Gait recognition method and system based on improved gait energy map | |
CN102663777A (en) | Target tracking method and system based on multi-view video | |
CN105321188A (en) | Foreground probability based target tracking method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20121003 Termination date: 20130109 |