CN108062762A - A kind of method for tracking target based on Density Estimator - Google Patents
A kind of method for tracking target based on Density Estimator Download PDFInfo
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- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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Abstract
The invention discloses a kind of method for tracking target based on Density Estimator, are related to computer vision field.The method of the present invention comprises the following steps：First of all for the variation for adapting to target morphology during vision tracks, COLOR COMPOSITION THROUGH DISTRIBUTION model is established to H component of the target in HSV space with Density Estimator simultaneously, so that is there is partial occlusion in target, tracking can be still accurately finished, then carries out target following with CamShift algorithms.The present invention is a kind of stronger algorithm of robustness, suitable for background variation is steady, target is single and distribution of color is not excessively complex environment, compared with based on gray-scale template matching method, and accuracy higher.
Description
Technical field
The present invention relates to computer vision fields, and in particular to a kind of method for tracking target based on Density Estimator.
Background technology
Vision tracking is the research hotspot in field of intelligent monitoring, refers to examine the moving target in video sequence
It surveys, extract, identify and tracks, to obtain the process of the parameters of target motion and movement locus.The tracking of mainstream has at present
MeanShift algorithms, particle filter algorithm etc..Using MeanShift algorithm keeps track targets, mean vector converges to sometimes
The local best points of Bhatacharyya coefficient curved surfaces so that tracking failure.Particle filter algorithm is a kind of based on dynamical state
The open system of spatial model (DSSM), tracking effect are stablized, but computationally intensive, and speed is slower.
In order to reduce operand, the real-time and accuracy of tracking are improved, adapts to what target scale variation carried out track band
It influences.The present invention proposes a kind of method for tracking target based on Density Estimator, and this method exists to target with Density Estimator
H components in HSV space establish COLOR COMPOSITION THROUGH DISTRIBUTION model so that partial occlusion is occurring in target, still can be complete exactly
Into tracking.Then CamShift algorithms are used into line trace.
The content of the invention
It is an object of the invention to propose a kind of method for tracking target based on Density Estimator, to realize foregoing invention mesh
, the method for the present invention specifically includes following steps：
1) COLOR COMPOSITION THROUGH DISTRIBUTION model is established to H components of the target in HSV space based on Density Estimator；
2) video sequence projected image is calculated based on the COLOR COMPOSITION THROUGH DISTRIBUTION model；
3) the continuous tracking of target is realized using CAMShift track algorithms.
As the preferred technical solution of the present invention, the step 1) includes：
Assuming that the point set of target region is { xi, i=1,2 ..., n, regional center point are x0, by the color of target
Information distribution is discrete for m section, function b (xi) represent xiThe H component values for locating pixel quantify, and assign it to corresponding
In section, m is quantification gradation, then, it is located at x for center0Target template, COLOR COMPOSITION THROUGH DISTRIBUTION model quThe following formula can be used
(1)-(4) describe：
In formula,For u grades of probability density, n is number of pixels total in target area, and h is the size of target area；b
(xi) it is kronecker delta functions, as b (xi) be u when, δ=1, as b (xi) be u when；Kernel function
It is a monotone decreasing convex function, for distributing weights to the pixel of target area, C is normaliztion constant.
As the preferred technical solution of the present invention, the step 2) includes：
2.1) RGB image is switched into HSV images, the H components in HSV images is separated to obtain image hue；Then make
Projection is carried out to image hue with object color component distributed model, preliminary projected image backproject is calculated；
2.2) mask operation is carried out, to HSV images, if position xiThe pixel value at place is src (xi), the mask value of the position
For dst (xi), threshold vector L and U are set, and mask calculating formula is：
K is k-th of channel components in above formula, takes Lk=(0,30,10), Uk=(180,256,256) finally calculate 81
The mask image mask of passage；
2.3) preliminary projected image backproject and mask image mask are done into logic "and" operation, obtained containing mesh
The final video sequence projected image M of the more accurate search range of targetq(x,y)。
As the preferred technical solution of the present invention, the step 3) includes：
- in initial two field picture set initial ranging window center and size；
- with according to formulaThe centroid position for calculating gained is the centre bit of new search window
It puts, and with formulaThe size of new search window is calculated, and it is mobile
Search window, wherein W and H are respectively the wide and high of search window；
- recalculate the position of barycenter and the size of search window, and mobile search window using new search window；
- repeat above operation until gained centroid position convergence or had reached setting cycle-index, stop
Only search process；The centroid position obtained at this time is position of the target in current frame image, with the search window at the position
Zeroth order square calculate next frame image initial search window size, and using this position as next frame image initial search window in
Heart position, so as to fulfill the continuous tracking of target.
Compared with prior art, the invention has the advantages that：
The method of the present invention has higher robustness, and it is suitable for background variation is steady, target is single and distribution of color is not
Excessively complex environment, compared with based on gray-scale template matching method, the method for the present invention accuracy higher；And the method for the present invention
Stable tracking under the conditions of different motion can be achieved to target, effectively overcome the shadow that dimensional variation carrys out track band
It rings.
Description of the drawings
Fig. 1 is a kind of flow chart of method for tracking target based on Density Estimator in embodiment；
Fig. 2 is that object color component distributed model in embodiment establishes process schematic；
Fig. 3 is the calculating process schematic diagram of the video sequence projected image in embodiment.
Specific embodiment
The present invention provides a kind of method for tracking target based on Density Estimator, in order to adapt to mesh during vision tracking
The variation of form is marked, COLOR COMPOSITION THROUGH DISTRIBUTION modeling is carried out to the moving target in video sequence using Density Estimator, is used
CamShift algorithms are into line trace.Stable tracking can be achieved to target in the present invention under the conditions of different motion, overcomes ruler
The influence that degree variation carrys out track band, is a kind of stronger track algorithm of robustness.
Below in conjunction with present specification attached drawing, to a kind of method for tracking target based on Density Estimator of the present invention
Specific embodiment be described in further details, it is clear that described embodiment be only part of the embodiment of the present invention, without
It is whole embodiments, based on the embodiments of the present invention, those of ordinary skill in the art are not before creative work is made
All other embodiments obtained are put, shall fall in the protection scope of this application.
The method of this specific embodiment specifically includes following steps：
S1 establishes COLOR COMPOSITION THROUGH DISTRIBUTION model based on Density Estimator to H components of the target in HSV space；
In various colour models, the H components in HSV space can represent colouring information.Traditional color histograms graph model
The spatial information of target internal pixel is had ignored, in order to overcome this defect, the present invention is using Density Estimator method to mesh
Target H components establish one-dimensional COLOR COMPOSITION THROUGH DISTRIBUTION model, and the model foundation process is as follows：
Assuming that the point set of target region is { xi, i=1,2 ..., n, regional center point are x0.By the color of target
Information distribution is discrete for m section, function b (xi) represent xiThe H component values for locating pixel quantify, and assign it to corresponding
In section, m is quantification gradation.So, it is located at x for center0Target template, COLOR COMPOSITION THROUGH DISTRIBUTION model quFormula below can be used
(1)-(4) describe.
In above formula,For u grades of probability density, n is number of pixels total in target area；H is the size of target area,
The generally radius in region；δ is kronecker delta functions, as b (xi) be u when, δ=1, as b (xi) be u when；Core letter
NumberIt is a monotone decreasing convex function, for distributing weights to the pixel of target area, distance center is more remote
The weights that obtain of pixel it is smaller so that the pixel of different position is different to the percentage contribution of colour model in target area；C is
Normaliztion constant.Object color component distributed model to establish process as shown in Figure 2.
S2 is based on the COLOR COMPOSITION THROUGH DISTRIBUTION model and calculates video sequence projected image；
The calculating of S2.1 preliminary video sequential projection images；
The calculating of video sequence projected image is as follows：
1) RGB image is switched to the HSV images hsv of 83 passages；
2) the H components in HSV images are separated, forms the image hue of 81 passages；
3) projection calculating is carried out to image hue using object color component distributed model (to be converted to the H components of original image
Color probability distribution image), obtain the preliminary projected image backproject of 81 passages.
The mask operation of the preliminary projected images of S2.2；
By the video sequence for tentatively projecting calculating, it is also necessary to carry out mask operation to determine more accurate target zone.
To HSV images, if position xiThe pixel value (3 passage) at place is src (xi), the mask value (single channel) of the position is dst (xi)。
Threshold vector L and U are set, and mask calculating formula is：
K is k-th of channel components in formula, takes Lk=(0,30,10), Uk=(180,256,256) finally calculate 81 and lead to
The mask image mask in road.
Preliminary projected image backproject and image mask mask are done logic "and" operation by S2.3, are obtained containing mesh
The final video sequence projected image M of the more accurate search range of targetq(x, y), the calculating of the image of video sequence projection
Process is as shown in Figure 3.
S3 carries out target using CAMShift track algorithms and continuously tracks；
CAMShift (Continuously Adaptive Mean Shift, continuous adaptive average and variance) algorithm utilizes
The color characteristic of target finds moving target position and size in video image, in next frame video image, with fortune
Moving-target current location and size initialization search window, this process of repetition can realize the continuous tracking to target.
For projected image backproject and image mask mask are done logic "and" operation, obtain containing target
The image M of more accurate search rangeq(x,y).The centroid position of search window can be determined by zeroth order square and first moment：
Calculate zeroth order square M00：
X and y first moments M is calculated respectively10, M01：
Calculate the centroid position x of search windowc, yc：
The size in tracking Target ellipse region can be calculated by following formula：Calculate x and y regions second moment：
Calculating parameter：
The length of the axial angle θ and long axis l short axles w of Target ellipse are calculated according to the parameter a, b, c of gained：
The scope of tracking target is determined using image ellipse, the dimensional variation of target is suitable for, enhances the flexible of tracking
Property.
Since the distance of the target range camera of tracking is different or the factors such as rotations of tracking target, cause target with
The size in track region constantly changes.CAMShift track algorithms are to calculate next search with the zeroth order square of current search window
The size of window, it is assumed that the width of search window is a height of H of W, then：
Search process：Center and the size of initial ranging window in initial two field picture are set, then calculated with formula (9)
The centroid position of gained is the center of new search window, the size of new search window is calculated with formula (19) h and (20), and movement is searched
Rope window；Then the position of barycenter and the size of search window, and mobile search window are recalculated using new search window；Repetition is gone down
Know the centroid position convergence of gained or had reached the cycle-index of setting, stop search process.It obtains at this time
Centroid position is position of the target in current frame image, and next two field picture is calculated with the zeroth order square of the search window at the position
The size of initial ranging window, and using this position as the center of next frame image initial search window, repeat this process just
It can realize the continuous tracking to target.
The method proposed in the present invention can actually be embedded in FPGA realizations, camera or camera shooting of the exploitation with intelligent-tracking
Machine.Above example only plays the role of explaining technical solution of the present invention, and protection domain of the presently claimed invention is not limited to
Realization system and specific implementation step described in above-described embodiment.Therefore, only to specific formula and algorithm in above-described embodiment
Simple replacement, but the technical solution that its substantive content is still consistent with the method for the invention are carried out, should all belong to the present invention's
Protection domain.
Claims (4)
1. a kind of method for tracking target based on Density Estimator, which is characterized in that include the following steps：
1) COLOR COMPOSITION THROUGH DISTRIBUTION model is established to H components of the target in HSV space based on Density Estimator；
2) video sequence projected image is calculated based on the COLOR COMPOSITION THROUGH DISTRIBUTION model；
3) the continuous tracking of target is realized using CAMShift track algorithms.
A kind of 2. method for tracking target based on Density Estimator according to claim 1, which is characterized in that the step
1) include：
Assuming that the point set of target region is { xi, i=1,2 ..., n, regional center point are x0, by the colouring information of target
It is distributed discrete for m section, function b (xi) represent xiThe H component values for locating pixel quantify, and assign it to corresponding section
Interior, m is quantification gradation, then, it is located at x for center0Target template, COLOR COMPOSITION THROUGH DISTRIBUTION model quCan use the following formula (1)-
(4) describe：
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In formula,For u grades of probability density, n is number of pixels total in target area, and h is the size of target area；b(xi) be
Kronecker delta functions, as b (xi) be u when, δ=1, as b (xi) be u when；Kernel functionIt is one
Monotone decreasing convex function, for distributing weights to the pixel of target area, C is normaliztion constant.
A kind of 3. method for tracking target based on Density Estimator according to claim 2, which is characterized in that the step
2) include：
2.1) RGB image is switched into HSV images, the H components in HSV images is separated to obtain image hue；Then mesh is used
Mark COLOR COMPOSITION THROUGH DISTRIBUTION model carries out image hue projection and preliminary projected image backproject is calculated；
2.2) mask operation is carried out, to HSV images, if position xiThe pixel value at place is src (xi), the mask value of the position is dst
(xi), threshold vector L and U are set, and mask calculating formula is：
K is k-th of channel components in above formula, takes Lk=(0,30,10), Uk=(180,256,256) finally calculate 81 passages
Mask image mask；
2.3) preliminary projected image backproject and mask image mask are done into logic "and" operation, obtained containing target
The final video sequence projected image M of more accurate search rangeq(x,y)。
A kind of 4. method for tracking target based on Density Estimator according to claim 1, which is characterized in that the step
3) include：
Center and the size of initial ranging window are set in initial two field picture；
With according to formulaThe centroid position for calculating gained is the center of new search window, and with
FormulaThe size of new search window, and mobile search window are calculated,
Wherein W and H is respectively the wide and high of search window；
The position of barycenter and the size of search window, and mobile search window are recalculated using new search window；
It repeats above operation until the cycle-index of setting is restrained or reached to the centroid position of gained, stop search process,
The centroid position obtained at this time is position of the target in current frame image, is calculated with the zeroth order square of the search window at the position
The size of next frame image initial search window, and using this position as the center of next frame image initial search window, so as to
Realize the continuous tracking of target.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108846789A (en) * | 2018-05-31 | 2018-11-20 | 中国科学院合肥物质科学研究院 | A kind of high speed CAMShift method based on GPU |
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| CN113379789A (en) * | 2021-06-11 | 2021-09-10 | 天津大学 | Moving target tracking method in complex environment |
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Application publication date: 20180522 |