CN101038672A - Image tracking method and system thereof - Google Patents

Abstract

The invention discloses an image tracking method comprising: calculating histograms of all regions, which use a predetermined corner of a whole tracking region as a starting point, in the whole tracking region by an integral operation, to obtain a region integral histogram; calculating histograms of every search windows, where a target may be appeared, in the whole tracking region, matching the calculated histograms of the search windows with a standard histogram of the target, obtaining a matching result, and determining a tracking position of the target according to the matching result. In addition, the invention also discloses an image tracking system. The image tracking method and system provided in the invention can ensure tracking effect and increase tracking speed.

Description

A kind of image tracking method and system

Technical field

The present invention relates to the image tracking technique, relate in particular to a kind of image tracking method and system.

Background technology

In the present image tracking technique, usually adopt based on the target tracking algorism of histogram coupling and realize tracking to target, i.e. the range searching that target may occur in a new two field picture corresponding target of mating most is as the reposition of target.Wherein, consider that each two field picture is in shooting process, because the distance of target moves, and may make the size of target in every two field picture different, be that the yardstick of target in every two field picture might be different, therefore, the zone of zone diverse location in comprising tracing area that target may occur in a new two field picture, also comprise same position is carried out the formed zone of different scale convergent-divergent.

In target tracking algorism based on the histogram coupling, it is the Meanshift target tracking algorism that a kind of algorithm is arranged, adopt the strategy of iterative search in this algorithm, can trace into target in the short period of time, but because iterative algorithm is based on the principle that the matching degree gradient descends, therefore can only find the local optimum position of matching degree in the search procedure, and can not find the global optimum position, make under the situation that the object that similar color is arranged exists, occur the situation of target following failure easily, can't guarantee tracking effect.

In addition, in target tracking algorism based on the histogram coupling, also have a kind of algorithm to be target tracking algorism based on global search, adopt the strategy of exhaustive search in this algorithm, each position that target may be occurred and the target image in yardstick zone all mate with target image to be tracked, obtain the tracing positional of target in the next frame image according to matching result.

As seen, target tracking algorism based on global search can guarantee tracking effect, but in this algorithm, in the process that the target image and the target image to be tracked in each position that may occur target and yardstick zone mates, need to calculate each zone, it is the histogram of the target image of each search window, in the prior art, when calculating the histogram of search window, after having determined histogrammic color range group number, need calculate the affiliated histogram group of each pixel one by one according to the brightness color range of each pixel in this search window, add up other pixel number of each set of histograms, obtain the histogram of this search window.Because window area of every search, all to repeat the histogrammic process of aforementioned calculation one time, again because in exhaustive search, there is the calculating of a lot of pixels all to repeat, therefore calculated amount is bigger, when in the search window of 100 * 100 sizes, searching for 20 * 20 target, if consider to have the words of three kinds of dimensional variation, then calculate 3 * 80 * 80 * 20 * 20=7680000 time at least about need, as seen calculated amount is bigger, therefore in the prior art, relatively poor based on the real-time follow-up of the target tracking algorism of global search.

Summary of the invention

In view of this, one aspect of the present invention provides a kind of image tracking method; A kind of image tracking system is provided on the other hand, can guarantees tracking effect, and improve tracking velocity.

Image tracking method provided by the present invention comprises: by integral operation, calculating in the whole tracing area with whole tracing area predefined one jiao is the histogram of the All Ranges of starting point, obtains the domain integral histogram;

Utilize the domain integral histogram, calculate the histogram of each search window that target may occur in the whole tracing area, the histogram of each search window of calculating and the standard histogram of target are mated, obtain matching result, determine the tracing positional of target according to matching result.

Wherein, described integration histogram comprises: the color integration histogram, and/or, the gradient direction integration histogram.

In the such scheme, the described domain integral histogram that utilizes, the histogram that calculates each search window that target may occur is: each search window that may occur target, domain integral histogram with four angle correspondences of this search window carries out plus and minus calculation, obtains the histogram of this search window.

Wherein, if predefined one jiao is the upper left corner, then described domain integral histogram with four angle correspondences of search window carries out plus and minus calculation and is specially: after the domain integral histogram addition that the domain integral histogram of this search window lower right corner correspondence is corresponding with this search window upper left corner, deduct the domain integral histogram of this search window upper right corner correspondence and the domain integral histogram of lower left corner correspondence, obtain the histogram of this search window.

In the such scheme, describedly comprise by calculating the domain integral histogram:

Determine the set of histograms number;

Calculate the histogram group under each pixel in the whole tracing area;

Add up in the whole tracing area with the All Ranges C of predefined one jiao of O as starting point _{I, j}In belong to other domain integral number of pixels of each set of histograms;

According to set of histograms number and each C _{I, j}In other domain integral number of pixels of each set of histograms, obtain each domain integral histogram.

Wherein, all C in the whole tracing area of described statistics _{I, j}In belong to other domain integral number of pixels of each set of histograms and be specially:

According to the histogram group information under each pixel, on the direction on a limit under the whole tracing area O, the pixel number in each group is carried out the addition recursion calculate, obtain the direction integral number of pixels of each group on this direction;

To the described direction integral number of pixels of each group of obtaining, on the direction on another limit under the whole tracing area O, carry out the addition recursion and calculate, obtain each C _{I, j}In the domain integral number of pixels of each group.

In the such scheme, when integration histogram comprised the gradient direction integration histogram, the histogram group under each pixel of described calculating comprised:

Determine the angular interval of gradient direction according to the set of histograms number;

Calculate the affiliated angular interval of gradient direction of each pixel, obtain the affiliated histogram group of this pixel.

Wherein, the angular interval under the gradient direction of described each pixel of calculating comprises:

Calculate the tangent value of the frontier point of angular interval, obtain the tangent value interval;

Calculate the ratio of each pixel,, obtain the angular interval under the gradient direction of each pixel according to the residing tangent value of this ratio interval at the Grad of the Grad of the y of coordinate axis direction and x direction.

In the such scheme, the standard histogram of described target comprises: the overall histogram of target; Then described the histogram of each search window of being calculated and the standard histogram of target are mated, obtain matching result and comprise:

The overall histogram of each search window of being calculated and the overall histogram of target are mated, obtain the global registration degree of search window, with the global registration degree of resulting search window as matching result.

Preferably, the standard histogram of described target also comprises: with the blocked histogram behind the target piecemeal; Then obtain after the global registration degree of search window, the global registration degree of this search window as before the matching result, further comprised:

According to the global registration degree of search window, obtain a plurality of global registration degree and satisfy pre-conditioned search window;

Described a plurality of search windows are carried out piecemeal according to the partitioned mode corresponding with target respectively, obtain the subwindow of each search window;

Utilize the domain integral histogram, calculate the histogram of each subwindow, the histogram of each subwindow of obtaining and the blocked histogram of target corresponding blocks are mated, obtain the piecemeal matching degree of each subwindow;

According to the piecemeal matching degree of each subwindow in the global registration degree of each search window and this search window, obtain the matching result of each search window.

Preferably, calculate before the integration histogram of whole tracing area, further comprise:

Each search window that utilizes sorter may occur target in the whole tracing area carries out degree of confidence to be described, if there is not degree of confidence to satisfy the search window of tracer request, then carries out the operation of the integration histogram of the whole tracing area of described calculating.

Preferably, this method further comprises: if exist degree of confidence to satisfy the search window of tracer request, then the search window that degree of confidence is satisfied tracer request carries out the histogram coupling, and it fails to match as if histogram, then carries out the operation of the integration histogram of the whole tracing area of described calculating; The match is successful as if histogram, and then general's search window that the match is successful is as the tracing positional of target.

Preferably, this method further comprises: search window that will the match is successful is stored, and according to the histogram of the search window of the predetermined number of storage, calculates the weight of each group of histogram in each feature passage;

The then described histogram and the standard histogram of target with search window mates and is: according to the weight of each group in each feature passage, the histogram of search window and the standard histogram of target are mated.

Image tracking system provided by the present invention comprises: tracing area is provided with unit, domain integral computing unit and histogram coupling tracking cell, wherein,

Tracing area is provided with the unit, is used for being provided with at current frame image the tracing area of target, and set tracing area is notified to domain integral computing unit and histogram coupling tracking cell;

The domain integral computing unit, be used to by integral operation, calculating in the whole tracing area with whole tracing area predefined one jiao is the histogram of the All Ranges of starting point, obtains the domain integral histogram, and the domain integral histogram that obtains is offered histogram coupling tracking cell;

Histogram coupling tracking cell, the domain integral histogram that utilizes the domain integral computing unit to provide is provided, calculate the histogram of each search window that target may occur in the whole tracing area, the histogram of each search window of calculating and the standard histogram of target are mated, obtain the tracing positional of target according to matching result.

Preferably, this system further comprises: target classification device tracking cell, each search window that is used for utilizing sorter may occur whole tracing area target carries out the degree of confidence description, if do not have degree of confidence to satisfy the search window of tracer request, then send the notice of following the tracks of failure to described domain integral computing unit;

Described domain integral computing unit according to the notice of failing from the tracking of target classification device tracking cell, is carried out the operation of described domain integral histogram calculation.

Preferably, this system further comprises: the histogram matching unit;

Then target classification device tracking cell is further used for: if exist degree of confidence to satisfy the search window of tracer request, the search window that then described degree of confidence is satisfied tracer request offers the histogram matching unit;

The histogram matching unit, the degree of confidence that being used to calculate target classification device tracking cell provides satisfies the histogram of the search window of tracer request, the search window histogram of calculating and the standard histogram of target are mated,, determine the tracing positional of target according to matching result.

Preferably, this system further comprises: histogram weight updating block;

Then the histogram matching unit is further used for: the search window that the match is successful is offered histogram weight updating block;

Histogram weight updating block is used for the histogram of search window according to the predetermined number of storage, calculates the weight of each group of histogram in each feature passage, and the weight of each group in each the feature passage that calculates is offered histogram coupling tracking cell;

Histogram coupling tracking cell is further used for: the weight of each group in each the feature passage that provides according to histogram weight updating block, carry out described histogram and the operation of mating of the standard histogram of target to search window.

In the such scheme, described histogram coupling tracking cell comprises: search window determination module, histogram calculation module, histogram matching module and tracing positional determination module, wherein,

The search window determination module is used for from tracing area the definite current search window of tracing area that the unit provides being set, and determined current search window is notified to the histogram calculation module;

The histogram calculation module is used to utilize the histogram of the domain integral histogram calculation current search window that the domain integral computing unit provides, and the histogram of the current search window that calculates is offered the histogram matching module; Notice search window determination module provides next current search window;

The histogram matching module is used for the histogram of current search window that the histogram calculation module is provided and the standard histogram of precalculated target and mates, and matching result is offered the tracing positional determination module;

The tracing positional determination module, the matching result of all search windows that the target that is used for providing according to the histogram matching module may occur, the search window zone that coupling is best is as the tracing positional of target.

Wherein, described tracing positional determination module comprises: search window is chosen submodule, search window and is determined that submodule, piece histogram calculation matched sub-block, search window comprehensive matching calculating sub module and tracing positional determine submodule, wherein,

Search window is chosen submodule, and the matching result of all search windows of providing according to the histogram matching module is provided, and therefrom chooses and satisfies pre-conditioned a plurality of search windows, offers search window and determines submodule;

Search window is determined submodule, is used for choosing the definite current search window of search window that submodule provides from search window, and determined current search window is notified to piece histogram calculation matched sub-block;

Piece histogram calculation matched sub-block is used for search window is determined that the current search window that submodule provides carries out piecemeal according to the method for partition corresponding with target, obtains subwindow; To the subwindow of each, utilize integration histogram to calculate the subwindow histogram, the subwindow histogram that calculated and the blocked histogram of target corresponding blocks are mated, obtain the piecemeal matching degree of each subwindow, resulting piecemeal matching degree is offered search window comprehensive matching calculating sub module; The notice search window determines that submodule provides next current search window;

Search window comprehensive matching calculating sub module, the piecemeal matching degree that all subwindows of the current search window that provides according to piece histogram calculation matched sub-block are provided, calculate the comprehensive matching degree of current search window, and the comprehensive matching degree of the current search window that calculates is offered tracing positional determine submodule;

Tracing positional is determined submodule, and the comprehensive matching degree of all search windows of providing according to search window comprehensive matching calculating sub module is provided, and will coupling best search window zone is as the tracing positional of target.

From said method and system schema as can be seen, at first calculate the domain integral histogram of whole tracing area among the present invention, utilize the domain integral histogram then, pass through plus and minus calculation, calculate each zone that target may occur one by one, i.e. the histogram of search window.Be among the present invention the histogram group under each pixel only to be calculated once when calculating integration histogram, the histogrammic calculating of each search window that may occur target afterwards only needs according to the histogrammic result of domain integral, carry out plus and minus calculation three times, after the domain integral histogram addition corresponding of the domain integral histogram of usefulness search window lower right corner correspondence with the search window upper left corner, deduct the domain integral histogram of search window upper right corner correspondence and the domain integral histogram of lower left corner correspondence, obtain the histogram of search window, under the situation that a large amount of windows are mated, this algorithm greatly reduces operand, as being example still in the search window of 100 * 100 sizes, to search for 20 * 20 target, if the same words of considering to have three kinds of dimensional variation, then only need calculate 3 * 80 * 80 * 3+100 * 100 * 2=77600 approximately, as seen reduced calculation times greatly, improve arithmetic speed, guaranteed the real-time of following the tracks of; Because be that the histogram of the target that will calculate each search window that may occur mates with the histogram of target among the present invention, obtain the predicted position of target according to matching result, so guaranteed tracking effect again.

In addition, carrying out histogram when coupling,, also comprise gradient orientation histogram among the present invention, thereby when in tracing area, having the object similar, can not be subjected to the interference of this object, further guaranteed tracking effect to color of object except comprising color histogram.

In addition, when carrying out the histogram coupling, mate among the present invention, thereby avoided further having guaranteed tracking effect owing to the spatial positional information of losing each pixel in statistic processes causes the situation of following the tracks of failure by adopting blocked histogram.

At last, when carrying out the histogram coupling, further adopt the method that combines with the target classification device among the present invention, thereby realize rough the tracking by the target classification device, further improve real-time performance of tracking, realized meticulous tracking by the histogram coupling afterwards, guaranteed tracking effect.

Description of drawings

Fig. 1 is the process flow diagram of image tracking method in the embodiment of the invention one;

Fig. 2 (a) is 7 * 7 tracing area picture element matrix synoptic diagram for size;

Fig. 2 (b) is the matrix diagram of histogram group information under each pixel in the picture element matrix shown in Fig. 2 (a);

Fig. 3 is other row integration number of pixels matrix diagram of each set of histograms;

Fig. 4 is other domain integral number of pixels matrix diagram of each set of histograms;

Fig. 5 (a) is that a kind of regional window of picture element matrix shown in Fig. 2 (a) is divided synoptic diagram;

Fig. 5 (b) is the synoptic diagram that calculates the search box pixel number in domain integral number of pixels matrix shown in Figure 4;

Fig. 6 (a) is the structural representation of image tracking system in the embodiment of the invention one;

Fig. 6 (b) is a kind of inner structure synoptic diagram of histogram coupling tracking cell in the system shown in Fig. 6 (a);

Fig. 7 is that the standard histogram of target in the embodiment of the invention three constitutes synoptic diagram;

Fig. 8 is the process flow diagram of specific implementation in the image tracking method step 107 in the embodiment of the invention three;

Fig. 9 is the inner structure synoptic diagram of tracing positional determination module in the image tracking system in the embodiment of the invention three;

Figure 10 is the process flow diagram of image tracking method in the embodiment of the invention four;

Figure 11 is the structural representation of image tracking system in the embodiment of the invention four;

Figure 12 is another structural representation of image tracking system in the embodiment of the invention four.

Embodiment

Basic thought of the present invention is: the domain integral histogram that calculates whole tracing area; Utilize the domain integral histogram,, calculate the histogram of each search window that target may occur, the histogram that calculated and the histogram of target are mated, obtain the tracing positional of target according to matching result by plus and minus calculation.

For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with embodiment and accompanying drawing, the present invention is described in more detail.

Embodiment one:

Referring to Fig. 1, Fig. 1 is the process flow diagram of image tracking method in the embodiment of the invention one.As shown in Figure 1, this flow process comprises the steps:

Step 101 is provided with tracing area in current frame image.

In this step, the method to set up of tracing area can have a variety of.Wherein a kind of can for: obtain earlier the predicted position of target, on the predicted position basis of target, the whole tracing area of target be set according to the maximum movement speed of target then by position prediction.

For example: can adopt following position predicting method, obtain the predicted position of target:

If tracing into target is (x in the position of moment t _t, y _t), at moment t-Δ _tThe position be (x _T-1, y _T-1), can estimate simply that then the speed of current goal is v _x=(x _t-x _T-1)/Δ _t, v _y=(y _t-y _T-1)/Δ _t

Consider the irregular movement of target or the deviation in the tracing process, stable inadequately for fear of above-mentioned estimation, can adopt wave filter to come level and smooth velocity vector, and obtain following velocity vector by multiple image:

$v_x\left(t\right)=\frac{k\×(x_t-x_{t-1})/{\mathrm{\Δ}}_t+\underset{n=1}{\overset{k-1}{\mathrm{\Σ}}}(k-n)\×v_x(t-n)}{\underset{n=1}{\overset{k}{\mathrm{\Σ}}}n},$ $v_y\left(t\right)=\frac{k\×(y_t-y_{t-1})/{\mathrm{\Δ}}_t+\underset{n=1}{\overset{k-1}{\mathrm{\Σ}}}(k-n)\×v_y(t-n)}{\underset{n=1}{\overset{k}{\mathrm{\Σ}}}n},$

Wherein, k carries out level and smooth required total number of image frames.

Velocity vector according to target is estimated, can target of prediction in next position constantly be:

${\hat{x}}_{t+1}=x_t+v_x\left(t\right),$ ${\hat{y}}_{t+1}=y_t+v_y\left(t\right).$

Wherein, when not only predict the position of the target of a pixel, (x _t, y _t) and

All be the place-centric point of target, therefore, obtain target at next predicted position central point constantly

Afterwards, determine that according to this central point target is at next predicted position (x constantly _p, y _p, w _p, h _p), wherein, (x _p, y _p) be the coordinate of target at the upper left angle point of next moment position, (w _p, h _p) be width and the height of target in next moment position.

Obtain after the above-mentioned predicted position, can as follows the whole tracing area of target be set according to the maximum movement speed of target:

If largest prediction error Δ _Max=(Δ _Xmax, Δ _Ymax), then the target rectangular area that may occur is (x _s, y _s, w _s, h _s), wherein, x _s=x _p-Δ _Xmax, y _s=y _p-Δ _Ymax, w _s=w _p+ 2 Δs _Xmax, h _s=h _p+ 2 Δs _Ymax

With rectangular area (x _s, y _s, w _s, h _s) be set to whole tracing area.

Step 102, by integral operation, calculating in the whole tracing area with whole tracing area predefined one jiao is the histogram of the All Ranges of starting point, obtains the domain integral histogram.

In this step, during the integration histogram of zoning, be specifically as follows: the group of determining integration histogram is counted K, i.e. figure place, or title length; Calculate the group information f of the affiliated histogram group k of each pixel in the whole tracing area then _k(i, j), k ∈ (0,1 ..., K-1), promptly calculate each pixel and belong to histogrammic which position.For the purpose of unification, all herein places that relates to starting point all are at 0 by starting point and handle; The one jiao of O that preestablishes whole tracing area adds up in the whole tracing area with the All Ranges C of O as starting point as starting point _{I, j}In belong to the number of pixels of each group k, with each C _{I, j}In belong to each group k number of pixels be referred to as each C _{I, j}In belong to the domain integral number of pixels II of each group k _k(i, j); According to set of histograms number and each with the zone C of O as starting point _{I, j}In the domain integral number of pixels II of each group k _k(i j), obtains each zone C _{I, j}Histogram, and with each C _{I, j}Histogram be referred to as the domain integral histogram.Wherein, for tracing area set in the step 101, C _{I, j}Middle i, the value of j is 0≤i＜w _s, 0≤j＜h _s, and ${\mathrm{II}}_k(i,j)=\underset{m<i,n<j}{\mathrm{\&Sigma;}}{f}_k(i,j).$

During specific implementation, for each C of express statistic _{I, j}In number of pixels in each histogram group, can adopt the addition recursive operation, according to the histogram group information under each pixel, on the direction on a limit under the whole tracing area starting point O, pixel number in each group is carried out the addition recursion to be calculated, obtain the direction integral number of pixels of each group on this direction, direction integral number of pixels to each group of obtaining, on the direction on another limit under the whole tracing area O, carry out the addition recursion and calculate, obtain each C _{I, j}In the domain integral number of pixels of each group.

Wherein, O can be the upper left corner, or the upper right corner, or the lower left corner, or the lower right corner, and the direction on a limit under the O can be a line direction, also can be column direction, if line direction, then the direction on another limit under the O is a column direction; If column direction, then the direction on another limit under the O is a line direction.

Describing for the aspect, hereinafter all is the upper left corner with O, and the direction on a limit under the O is that the situation of line direction is an example.

Generally speaking, histogram is a color histogram all, so also adopts color histogram in the present embodiment, and the computation process to the above-mentioned zone integration histogram is described in detail below.

Color standard generally comprises RGB (RGB) graphics standard, or the YUV graphics standard etc.When calculating color histogram, can calculate the histogram of this feature passage with whole color as a feature passage, perhaps, in order to describe the color characteristic of target, also each Color Channel wherein can be calculated the histogram of each feature passage as a feature passage.As: if adopt the RGB graphics standard, then the feature passage can be red (R), green (G), blue (B) three Color Channels; If adopt the YUV graphics standard, then the feature passage can be Y, U, three Color Channels of V.

Because the brightness color range of pixel is distributed as 0～255, add up to 256, as if being example with one of them feature passage, (i, j) (i j) locates the color range value of pixel in this feature passage, and uses f in expression with I _k(i, j), k ∈ (0,1 ..., K-1) expression (i j) locates group information under the pixel, then (i, j) locate the histogram group of pixel under in this feature passage calculate can for: $f_k(i,j)=\left\{\begin{array}{cc}1,& \frac{I(i,j)}{256/K}=\mathrm{<figure-callout\; id="0"\; label="k"\; filenames="A20071009897000413.png,A20071009897000423.png"\; state="\{\{state\}\}">k</figure-callout>}\\ 0& \end{array}\right.,$ Wherein, be that 1 expression belongs to the k group, be 0 expression does not belong to the k group.Certainly, also can adopt interval computing method, promptly calculate the boundary value of each group respectively, according to the boundary value of group, judgement I (i, j) Suo Shu group interval, thus obtain I (i, j) Suo Shu group.Calculate all f _k(i, j) after, obtain K group matrix F _k, k ∈ (0,1 ..., K-1).

Referring to Fig. 2, Fig. 2 has provided affiliated other synoptic diagram of set of histograms of each pixel that a batch total obtains.Shown in Fig. 2 (a), Fig. 2 (a) is 7 * 7 tracing area picture element matrix synoptic diagram for size.Being convenient and describing, be that 7 * 7 situation is an example with whole tracing area size among Fig. 2, and the hypothesis set of histograms is counted K=8, the coordinate (x of starting point O _s, y _s) be (0,0), then in 7 * 7 picture element matrix tracing area, affiliated other situation of set of histograms of each pixel is 8 group matrix F as shown in Fig. 2 (b) respectively _k, k ∈ (0,1 ..., 7), Fig. 2 (b) is the matrix diagram of histogram group information under each pixel in the picture element matrix shown in Fig. 2 (a).Shown in Fig. 2 (b), F ₀Show pixel distribution of the 0th group in histogram ..., F ₇Show pixel distribution of the 7th group in histogram, F _kIn 1 expression belong to k group in the histogram, 0 expression does not belong to the k group in the histogram.As: F ₀In the value of (1,2) position be 1, i.e. f ₀The value of (1,2) is 1, and other F _k, k ∈ (1 ..., 7) in the value of corresponding position all be 0, i.e. f _k(i, j), k ∈ (1 ..., 7) be 0, the pixel of (1,2) position belongs to histogrammic the 0th group in the presentation graphs 2 (a), and the like, the group matrix F among Fig. 2 (b) _kShow the affiliated histogrammic group information of each pixel.

After the histogram group information that has drawn under each pixel, the statistics to the number of pixels in each histogram group is described in detail below.

If use RI _kRepresent the capable integration number of pixels matrix of k group, use RI _k(i j) arrives point (i, the capable integration number of pixels of j) locating, then RI from picture element matrix x start of line point (i, 0) in the capable integration number of pixels matrix of expression k group _k(i, computing formula j) can for: $\underset{0\&le;j\&le;h_s-1}{\mathrm{\&Sigma;}}\underset{0\&le;i\&le;w_s-1}{\mathrm{\&Sigma;}}R{I}_k(i-1,j)+f_k(i,j),$ Wherein, RI _k(0, j)=f _k(0, j).

During specific implementation, computation process can for:

for?j＝0→h _s-1

{RI _k(0，j)＝f _k(0，j)

for?i＝1→w _s-1

RI _k(i，j)＝RI _k(i-1，j)+f _k(i，j)

}

Aforementioned calculation procedural representation: from the group matrix F _kThe 0th the row to h _s-1 row, i.e. j=0 → h _s-1, the addition recursion is carried out in each provisional capital calculate, wherein the addition recursion is calculated as: with first value of this row initial value as this row integration number of pixels, i.e. RI _k(0, j)=f _k(0, j), second value addition of initial value and this row obtained second capable integration number of pixels value, second capable integration number of pixels value and the 3rd value addition are obtained the 3rd capable integration number of pixels value, recursion successively, up to obtaining this row last row integration number of pixels value, i.e. i=1 → w _s-1, RI _k(i, j)=RI _k(i-1, j)+f _k(i, j).

For the picture element matrix of 7 * 7 shown in Fig. 2 (a), h _s=7, w _s=7, then can obtain 87 * 7 capable integration number of pixels matrix RI as shown in Figure 3 _k

To calculate the 0th group capable integration number of pixels matrix RI ₀Be example, calculate initialize, i.e. RI since first row ₀(0,0)=f ₀(0,0)=0, recursion is calculated afterwards, RI ₀(1,0)=RI ₀(0,0)+f ₀(1,0)=0+1=1, RI ₀(2,0)=RI ₀(1,0)+f ₀(2,0)=1+0=1 ..., RI ₀(6,0)=RI ₀(5,0)+f ₀(6,0)=3+0=3 begins to calculate second row then, and process is identical with first row, and up to having calculated the 6th row, the 0th group capable integration number of pixels is calculated and finished.

Capable integration number of pixels for other group is calculated according to the computation process identical with the 0th group capable integration number of pixels, obtains the capable integration number of pixels of each group at last, promptly obtains the direction integral number of pixels of each group on line direction.

Capable integration number of pixels RI to each group of obtaining _k(i j), is to carry out the addition recursion on the column direction to calculate on the direction on another limit under the whole tracing area starting point O, if use II _kRepresent the domain integral number of pixels matrix of k group, use II _k(i j) arrives point (i, zone C j) from picture element matrix starting point (0,0) in the domain integral number of pixels matrix of expression k group _{I, j}Number of pixels, with II _k(i j) is called point (i, j) Dui Ying domain integral number of pixels, then II _k(i, computing formula j) can for: $\underset{0\&le;i{\&le;w}_s-1}{\mathrm{\&Sigma;}}\underset{0\&le;j\&le;h_s-1}{\mathrm{\&Sigma;}}{\mathrm{II}}_k(i,j-1)+{\mathrm{RI}}_k(i,j),$ Wherein, II _k(i, 0)=RI _k(i, 0).

During specific implementation, computation process can for:

for?i＝0→w _s-1

{II _k(i，0)＝RI _k(i，0)

for?j＝1→h _s-1

II _k(i，j)＝II _k(i，j-1)+RI _k(i，j)

}

Aforementioned calculation procedural representation: from row integration number of pixels RI _kThe 0th is listed as w _s-1 row, i.e. i=0 → w _s-1, each row is all carried out the addition recursion calculate, wherein the addition recursion is calculated as: first value that will be listed as is as the initial value of the domain integral number of pixels of this column element correspondence, i.e. II _k(i, 0)=RI _k(i, 0), second value addition of initial value and these row obtained second domain integral number of pixels value, second domain integral number of pixels value and the 3rd value addition are obtained the 3rd domain integral number of pixels value, recursion successively, up to last domain integral number of pixels value, i.e. j=1 → h of obtaining this column element correspondence _s-1, II _k(i, j)=II _k(i, j-1)+RI _k(i, j).

Wherein, the domain integral number of pixels value of element correspondence is: with starting point O and this element domain integral number of pixels value as cornerwise rectangular area.Wherein, under the extreme case, starting point and this element are positioned at delegation or same row, are that cornerwise rectangular area is the zone of i * 1 or 1 * j with starting point O and this element then.

For the picture element matrix of 7 * 7 shown in Fig. 2 (a), h _s=7, w _s=7, then can obtain 87 * 7 domain integral number of pixels matrix II as shown in Figure 4 _k

To calculate the 0th group domain integral number of pixels matrix II ₀Be example, calculate since first row, at first initialize, i.e. II ₀(0,0)=RI ₀(0,0)=0 is carried out recursion afterwards and is calculated II ₀(0,1)=II ₀(0,0)+RI ₀(0,1)=0+0=0, II ₀(0,2)=II ₀(0,1)+RI ₀(0,2)=0+0=0 ..., II ₀(0,6)=II ₀(0,5)+RI ₀(0,6)=0+0=0 begins to calculate secondary series then, and process is identical with first row, and up to having calculated the 6th row, the 0th group domain integral number of pixels is calculated and finished.

Domain integral number of pixels for other group is calculated according to the computation process identical with the 0th group domain integral number of pixels, obtains the domain integral number of pixels of each group at last, promptly obtains each C _{I, j}In the domain integral number of pixels of each group.

Referring to Fig. 5 (a), Fig. 5 (a) is a kind of area dividing synoptic diagram of picture element matrix shown in Fig. 2 (a).Wherein, the corresponding domain integral number of pixels II of H point _k(1,1) is the number of pixels in the area I that OAHG represents among the figure, the domain integral number of pixels II that the C point is corresponding _k(5,1) are the number of pixels among the area I I that OBCG represents among the figure, the domain integral number of pixels II that the D point is corresponding _k(5,4) are the number of pixels among the area I V that OBDF represents among the figure, the domain integral number of pixels II that the E point is corresponding _k(1,4) is the number of pixels among the area I II that OAEF represents among the figure.Wherein, area I can be expressed as C _1,1, area I I can be expressed as C _5,1, area I V can be expressed as C _5,4, area I II can be expressed as C _1,4

According to set of histograms number and each zone C _{I, j}In the number of pixels of each group, can obtain each zone C _{I, j}Histogram, i.e. domain integral histogram h _{I, j}Wherein, if adopt each Color Channel with color all as feature passage compute histograms, then each zone C _{I, j}Histogram h _{I, j}The histogram that comprises each Color Channel, i.e. h _{I, j}=[h _r, h _g, h _b] _{I, j}, or h _{I, j}=[h _y, h _u, h _y] _{I, j}Wherein, h _r, h _g, h _bBe respectively the histogram of R, G, B feature passage; In like manner, h _y, h _u, h _vBe respectively the histogram of Y, U, V feature passage.

Step 103 is determined the current search window in whole tracing area.

In the present embodiment, can adopt the exhaustive search method, the ferret out zone that may occur, i.e. search window one by one in whole tracing area.Wherein, the search window that target may occur also comprises the regional window under the possibility yardstick, the hypothetical target zone is w * h, possible yardstick c is 0.9 times, 1.0 times, 1.1 times etc., and then the target search window that may occur comprises the regional window of sizes such as 0.9w * 0.9h, w * h, 1.1w * 1.1h.

In this step, when definite current search window, can be under each yardstick, according to a certain direction, and as from left to right, direction from top to bottom, or from top to bottom, direction from left to right etc. are determined the current search window successively.

The specific implementation process can for: under each possible yardstick c, calculate yardstick c the width w of corresponding target area _c=c * w and height h _c=c * h determines according to a certain direction that afterwards (x y), obtains current search window (x, y, w in each possible position, the search window upper left corner _c, h _c).C ∈ C wherein, C be might yardstick set, as C={0.8,0.9,1.0,1.1,1.2} etc.

Step 104 is utilized the domain integral histogram, calculates the histogram of current search window.

In this step, when calculating the histogram of current search window, after the domain integral histogram addition that the domain integral histogram of this search window lower right corner correspondence is corresponding with this search window upper left corner, deduct the domain integral histogram of this search window upper right corner correspondence and the domain integral histogram of lower left corner correspondence, obtain the histogram of this search window.As: for the current search window is (x, y, w _c, h _c) time, then this search window is C _{X+wc, y+hc}+ C _{X, y}-C _{X+wc, y}-C _{X, y+hc}, the histogram of this search window is h _{X+wc, y+hc}+ h _{X, y}-h _{X+wc, y}-h _{X, y+hc}

Referring to Fig. 5, suppose the regional window that the current search window is represented for the HCDE shown in Fig. 5 (a), then during the histogram of zoning window HCDE, need to calculate the number of pixels that belongs to each group of histogram in this regional window, can utilize the domain integral number of pixels that calculates in the step 102 to carry out plus and minus calculation, be specially: II _k(5,4)+II _k(1,1)-II _k(5,1)-II _k(1,4).

Shown in Fig. 5 (b), the number of pixels that belongs to the 0th group of histogram in the regional window that HCDE represents is: II ₀(5,4)+II ₀(1,1)-II ₀(5,1)-II ₀(1,4)=9+1-5-2=3; The number of pixels that belongs to the 1st group of histogram is II ₁(5,4)+II ₁(1,1)-II ₁(5,1)-II ₁(1,4)=8+2-4-4=2; The number of pixels that belongs to the 7th group of histogram is II ₇(5,4)+II ₇(1,1)-II ₇(5,1)-II ₇(1,4)=4+0-0-0=4.

According to the number of pixels in each histogram group in set of histograms number and this search window, obtain the histogram h of this search window _WinWherein, if adopt each Color Channel with color all as feature passage compute histograms, the histogram h of search window then _WinThe histogram that comprises each Color Channel, i.e. h _Win=[h _r, h _g, h _b], or h _Win=[h _y, h _u, h _y].Wherein, h _r, h _g, h _bBe respectively the histogram of R, G, B feature passage; In like manner, h _y, h _u, h _vBe respectively the histogram of Y, U, V feature passage.

Step 105 is mated the histogram of the current search window that calculates and the standard histogram of target, the record matching result.

In this step, before the standard histogram of the histogram of the current search window that calculates and target mated, at first the histogram of different scale search window is converted to the histogram of standard scale search window, so that can mate with the standard histogram of target.Concrete transfer process is: each group pixel number in the different scale search window histogram is obtained the pixel number of each group of standard scale histogram divided by this scale-value, thereby obtain the histogram of standard scale search window.As in the step 103, when scale-value c is 0.9, only need pixel number in each group at this moment when being converted to standard scale then, thereby obtain the pixel number of each group of standard scale histogram divided by 0.9.

During specific implementation, also the histogram of different scale search window and the standard histogram of target all can be carried out normalization, wherein carry out normalized process can for: with all pixels in the histogram as radix, pixel in each group of histogram is removed radix as divisor, obtain the histogram after the normalization.

When specifically mating, can adopt the standard of Euclidean distance, if use h as the histogram coupling _StdThe standard histogram of expression target is with M (h _Win, h _Std) expression h _WinWith h _StdMatching degree, suppose wherein h _Win, h _Std=[h _r, h _g, h _b] (or h _Win, h _Std=[h _y, h _u, h _v), M (h then _Win, h _Std) computation process can for: $M(h_{\mathrm{win}},h_{\mathrm{std}})=\sqrt{\underset{d}{\mathrm{\&Sigma;}}D(h_{\mathrm{win}.d},h_{\mathrm{std}.d})},$ Wherein, d ∈ D, D={r, g, b} (or D={y, u, v}); $D(h_{\mathrm{win}.d},h_{\mathrm{std}.d})=\underset{k=0}{\overset{K-1}{\mathrm{\&Sigma;}}}(h_{\mathrm{win}.d}\left(k\right)-h_{\mathrm{std}.d}\left(k\right){)}^2.$ Wherein, K is a histogrammic group of number, and k is histogrammic group, D (h _Win.d, h _Std.d) be the deviation size between the standard histogram of search window histogram and target in the d feature passage, M (h _Win, h _Std) be h _WinWith h _StdComprehensive deviation.

Preferably, more accurate in the aforementioned calculation process in order to make calculating, can in computing formula, add weight, as D (h _Win.d, h _Std.d) can for: $D(h_{\mathrm{win}.d},h_{\mathrm{std}.d})=\underset{k=0}{\overset{K-1}{\mathrm{\&Sigma;}}}w(d,k){(h_{\mathrm{win}.d}\left(k\right)-h_{\mathrm{std}.d}\left(k\right))}^2,$ Wherein, (d k) is the weight of histogram k group in the d feature passage to w; M (h _Win, h _Std) also can for: $M(h_{\mathrm{win}},h_{\mathrm{std}})=\sqrt{\underset{d}{\mathrm{\&Sigma;}}{w}_{d}D(h_{\mathrm{win}.d},h_{\mathrm{std}.d})},$ Wherein, w _dWeight for d feature passage.

Wherein, and weight w (d, k) and w _dCan obtain by empirical value or actual needs.

At last, with the search window matching degree M (h that obtains _Win, h _Std) carry out record.

Step 106 judges whether that target may occur in the whole tracing area all search windows have all mated to finish, if then execution in step 107; Otherwise, return execution in step 103.

Step 107 according to the matching result of record, obtains the tracing positional of target.

In the present embodiment, the matching result of record is h in the step 105 _WinWith h _StdBetween matching degree M (h _Win, h _Std), and M (h _Win, h _Std) that in fact obtain is h _WinWith h _StdBetween the deviation size, therefore, M (h _Win, h _Std) value more little, the expression h _Win, with h _StdMate more.M (the h of all search windows that may occur according to target in the whole tracing area of noting in the step 105 _Win, h _Std), therefrom choose M (h _Win, h _Std) the minimum search window zone of value is as the tracing positional of target.

If need to continue tracking target, then extract the next frame image, return execution in step 101.

Above-mentioned image tracking method in the embodiment of the invention one is described in detail, again image tracking system in the embodiment of the invention one is described in detail below.

Referring to Fig. 6 (a), Fig. 6 (a) is the structural representation of image tracking system in the embodiment of the invention one.Shown in Fig. 6 (a), this system comprises: tracing area is provided with unit 610, domain integral computing unit 620 and histogram coupling tracking cell 630.

Wherein, tracing area is provided with unit 610, is used for being provided with at current frame image the tracing area of target, and set tracing area is notified to domain integral computing unit 620 and histogram coupling tracking cell 630.

Domain integral computing unit 620, be used to by integral operation, calculating in the whole tracing area with whole tracing area predefined one jiao is the histogram of the All Ranges of starting point, obtain the domain integral histogram, and the domain integral histogram that obtains is offered histogram coupling tracking cell 630.

Histogram coupling tracking cell 630, the domain integral histogram that utilizes domain integral computing unit 610 to provide is provided, calculate the histogram of each search window that target may occur in the whole tracing area, the histogram of each search window of calculating and the standard histogram of target are mated, obtain the tracing positional of target according to matching result.

During specific implementation, histogram coupling tracking cell 630 can have multiple way of realization, can be an independent module, also can be to be made of several modules.Be example only below, the specific implementation of histogram coupling tracking cell 630 be described in detail with a kind of way of realization wherein, referring to Fig. 6 (b), Fig. 6 (b) be shown in Fig. 6 (a) in the system histogram mate a kind of inner structure synoptic diagram of tracking cell.Shown in Fig. 6 (b), histogram coupling tracking cell 630 can comprise: search window determination module 631, histogram calculation module 632, histogram matching module 633 and tracing positional determination module 634.

Wherein, search window determination module 631 is used for from tracing area the definite current search window of tracing area that unit 610 provides being set, and determined current search window is notified to histogram calculation module 632.

Histogram calculation module 632, be used to utilize the histogram of the domain integral histogram calculation current search window that domain integral computing unit 620 provides, the histogram of the current search window that calculates is offered histogram matching module 633, notify search window determination module 631 that next current search window is provided simultaneously.

Histogram matching module 633 is used for the histogram of current search window that histogram calculation module 622 is provided and the standard histogram of precalculated target and mates, and matching result is offered tracing positional determination module 634.

Tracing positional determination module 634, the matching result of all search windows that the target that is used for providing according to histogram matching module 633 may occur, the search window zone that coupling is best is as the tracing positional of target.

Wherein, the detailed implementation procedure of each functional unit and module can be consistent with the description in the method flow shown in Figure 1 in the system shown in Figure 6.

Embodiment two:

Image tracking method among image tracking method in the present embodiment and the embodiment one is roughly the same, and its difference is:

In the step 102 of method flow shown in Figure 1, what embodiment one adopted is color histogram, but in this case, when in tracing area, having the object similar to color of object, because color histogram is a kind of description to color, therefore be subjected to the interference of the object similar easily, make tracking inaccurate, for this reason to color of object, in the step 102 of present embodiment, on the basis of adopting color histogram, added a kind of histogram again, i.e. gradient orientation histogram.

Gradient orientation histogram is meant: the angular interval of determining gradient direction according to the set of histograms number; Calculate the affiliated angular interval of gradient direction of each pixel, obtain the affiliated histogram group of this pixel.

Concrete computation process is as follows:

Suppose that (i, j) (i j) locates gray values of pixel points, uses G in expression with I _x(i, the j) Grad on the expression x direction, G _y(i, the j) Grad on the expression y direction, G _Dir(i, j) the expression gradient direction then has: G _x(i, j)=I (i+1, j)-I (i-1, j), G _y(i, j)=I (i, j+1)-I (i, j-1), $G_{\mathrm{dir}}(i,j)=\arctan \frac{G_y(i,j)}{G_x(i,j)}.$ Because $-\frac{\mathrm{\&pi;}}{2}<\arctan \frac{G_y(i,j)}{G_x(i,j)}<\frac{\mathrm{\&pi;}}{2},$ Therefore gradient direction can be existed

To the histogram grouping,, then will in the scope if the set of histograms number is K

Be divided into K angular interval.As carrying out following interval division:

$\left\{\begin{array}{c}{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="A20071009897000413.png,A20071009897000423.png"\; state="\{\{state\}\}">R</figure-callout>}}_0=[-\frac{\mathrm{\&pi;}}{2},-\frac{(2K-1)\mathrm{\&pi;}}{2K})\&cup;[\frac{(2K-1)\mathrm{\&pi;}}{2K},\frac{\mathrm{\&pi;}}{2})\\ R_i=[-\frac{\mathrm{\&pi;}}{2}+\frac{\mathrm{i\&pi;}}{K}-\frac{\mathrm{\&pi;}}{2K},-\frac{\mathrm{\&pi;}}{2}+\frac{\mathrm{i\&pi;}}{K}+\frac{\mathrm{\&pi;}}{2K}),i=\mathrm{1,2}......,K-1\end{array}\right.$

Calculate the G of each pixel _Dir(i, j), according to G _Dir(i, the j) scope in interval of living in obtain the affiliated histogrammic group of this pixel.

In the practical application, for fear of carrying out arctangent cp cp operation, the tangent value b that puts on the computation interval border at first _i, calculate then that (i j) locates the gradient ratio G of pixel _y(i, j)/G _x(i, j) residing interval (b _I-1, b _i), obtain the affiliated histogram group of this pixel then.If still use f _k(i, j), k ∈ (0,1 ..., K-1) expression (i j) locates group information under the pixel, then concrete computation process can for:

$f_k(i,j)=\left\{\begin{array}{cc}1,& b_{i-1}<G_y(i,j)/G_x(i,j)<{\mathrm{<figure-callout\; id="0"\; label="b\; i"\; filenames="A20071009897000413.png,A20071009897000423.png"\; state="\{\{state\}\}">b</figure-callout>}}_i\\ 0& \end{array}\right.,.$

Statistics to the number of pixels in each gradient orientation histogram group is identical with the statistical method of color histogram afterwards, repeats no more herein.

At last according to gradient orientation histogram group number and each zone C _{I, j}In number of pixels in each group, obtain each zone C _{I, j}Gradient orientation histogram h _DirIf what adopt when calculating color histogram is to each Color Channel of color all compute histograms, then each zone C _{I, j}Total histogram h _{I, j}For: h _i, j=[h _r, h _g, h _b, h _Dir] _{I, j}, or h _{I, j}=[h _y, h _u, h _v, h _Dir] _{I, j}, promptly this moment, the feature passage also comprised the gradient direction passage.

Correspondingly, in step 104, the search window histogram h that calculates _WinFor: h _Win=[h _r, h _g, h _b, h _Dir], (or h _Win=[h _y, h _u, h _v, h _Dir]), the standard histogram h of same precalculated target _StdFor: h _Std=[h _r, h _g, h _b, h _Dir], (or h _Std=[h _y, h _u, h _v, h _Dir]).In step 105, D={r, g, b, G _Dir(or D={y, u, v, G _Dir).

The structure of image tracking system and annexation are identical with image tracking system among the embodiment one in the present embodiment, function is also similar, and the description in the image tracking method in the detailed implementation procedure that difference only is each functional unit and module and the present embodiment is consistent.

Gradient orientation histogram described in the foregoing description two also can use separately, only adopts gradient orientation histogram in the step 102 promptly shown in Figure 1, then this moment h _{I, j}=h _Dir, correspondingly, in the step 104, h _Win=h _Dir, h _Std=h _Dir, in the step 105, D=G _Dir

Embodiment three:

Image tracking method in the present embodiment can adopt step 101 among the embodiment one to step 106, also can adopt step 101 among the embodiment two to step 106.The difference of present embodiment and embodiment one or embodiment two is:

First point: for edge feature and the locus feature that embodies target, in advance target is carried out piecemeal according to default piece number, and calculate the blocked histogram of target.Then also comprise blocked histogram in the standard histogram of target.

Wherein, the standard histogram of target is the overall histogram of target among embodiment one, the embodiment two, not only comprises the overall histogram of target in the present embodiment in the standard histogram of target, also comprises the blocked histogram of target.Suppose target is divided into M * N piece, and histogram comprises the histogram and the gradient orientation histogram of each Color Channel in the color that then the standard histogram of target constitutes as shown in Figure 7, wherein b ₁..., b _KHistogram part for each group in the histogram.Wherein (m, blocked histogram n) can be used h to piece _StmnExpression.

Second point: the specific implementation process difference in the step 107, referring to Fig. 8, Fig. 8 is the specific implementation process in the step 107 in the present embodiment, comprising:

Step 107.a according to the matching result of record, according to preset condition, chooses qualified a plurality of search window.

In this step, according to the matching result M (h of record in the step 105 _Win, h _Std), according to preset condition, choose qualified M (h _Win, h _Std) the less a plurality of search windows of value.The wherein pre-conditioned search window number N that can be select then pre-conditionedly chooses N M (h according to this _Win, h _Std) the less search window of value; Perhaps pre-conditionedly can be M (h _Win, h _Std) the value thresholding, then according to this pre-conditioned M (h that chooses _Win, h _Std) value is less than the search window of this thresholding.

Can a plurality of search windows of choosing being numbered in this step, supposing to have chosen N search window, can be 1,2 with N search window number consecutively then ..., N.

Step 107.b determines the current search window from a plurality of search windows of choosing.

Suppose that search window selected among the step 107.a is N, then in this step, can determine in N one,, then can determine the current search window according to numeral order in this step if among the step 107.a N search window compiled number as the current search window.

Step 107.c carries out piecemeal with the current search window according to the method for partition identical with target, obtains a plurality of subwindows.

In this step, hypothetical target is divided into M * N piece, then also the current search window is divided into the corresponding M * N piece of size.

Step 107.d utilizes integration histogram to calculate the histogram of each subwindow, and the subwindow histogram that calculated and the blocked histogram of target corresponding blocks are mated, and obtains each subwindow matching result.

In this step, for with embodiment one, embodiment two in the histogram of the search window that calculated distinguish, the overall histogram that the histogram of the search window that calculated among embodiment one, the embodiment two can be called search window, with the overall histogram of search window and the overall histogrammic matching result (matching degree) of target, be called global registration result's (matching degree) of search window.

The concrete computation process of subwindow matching result can for:

for?m＝1→M

{for?n＝1→N

{ utilize integration histogram to calculate (m, n) the subwindow histogram of piece;

With calculated (m, n) of piece subwindow histogram and target (m, n) the piece piecemeal is straight

Side figure mates, the record matching result;

}

}

The aforementioned calculation procedural representation, according to from top to bottom, direction is from left to right chosen current block one by one, and the subwindow histogram of calculating current block, the subwindow histogram of the current block that calculated and the blocked histogram of target corresponding blocks are mated, and record matching result (matching degree), next piece of choosing current block afterwards is as current block, continue said process, up to having mated all subwindows.

Wherein, utilize integration histogram calculate the (m, n) during the subwindow histogram of piece, computing method are identical with the histogrammic method of search window of calculating piecemeal not, as: (m, n) window area of piece is (mx, ny, w to suppose the _MN, h _MN) time, then this subwindow is C _{Mx+wMN, ny+hMN}+ C _{Mx, ny}-C _{Mx+wMN, ny}-C _{Mx, ny+hMN}, the histogram of this subwindow is h _{Mx+wMN, ny+hMN}+ h _{Mx, ny}-h _{Mx+wMN, ny}-h _{Mx, ny+hMN}

With calculated the (m, n) the of piece subwindow histogram and target (m when n) the piece blocked histogram mates, can adopt the standard of Euclidean distance as the histogram coupling equally, if use h _MnExpression (m, n) the subwindow histogram of piece, usefulness M (h _Mn, h _Stmn) expression h _MnWith h _StmnMatching degree, then $M(h_{\mathrm{mn}},h_{\mathrm{stmn}})=\sqrt{\underset{d}{\mathrm{\&Sigma;}}D(h_{\mathrm{mn}.d},h_{\mathrm{stmn}.d})},$ $D(d_{\mathrm{mn}.d},h_{\mathrm{stmn}.d})=\underset{k=0}{\overset{K-1}{\mathrm{\&Sigma;}}}{(h_{\mathrm{mn}.d}\left(k\right)-h_{\mathrm{stmn}.d}\left(k\right))}^2.$ Wherein, if adopt step 101 among the embodiment two to step 106, d ∈ D then, D={r, g, b, G _Dir(or D={y, u, v, G _Dir), or d ∈ D, D=G _DirIf adopt step 101 among the embodiment one to step 106, d ∈ D then, D={r, g, b} (or D={y, u, v}).Wherein, K is a histogrammic group of number, and k is histogrammic group, D (h _Mn.d, h _Stmn.d) be (m, n) (m, n) the deviation size between the piece blocked histogram, the M (h of piece subwindow histogram and target in the d feature passage _Mn, h _Stmn) be (m, n) (m, n) the comprehensive deviation of piece blocked histogram of piece subwindow histogram and target.

Preferably, more accurate in the aforementioned calculation process in order to make calculating, also can in computing formula, add weight, as D (h _Mn.d, h _Stmn.d) can for: $D(h_{\mathrm{mn}.d},h_{\mathrm{stmn}.d})=\underset{k=0}{\overset{K-1}{\mathrm{\&Sigma;}}}{w}_{(d,k)}{(h_{\mathrm{mn}.d}\left(k\right)-h_{\mathrm{stmn}.d}\left(k\right))}^2,$ Wherein, (d k) is the weight of histogram k group in the d feature passage to w; And M (h _Mn, h _Stmn) also can for: $M(h_{\mathrm{mn}},h_{\mathrm{stmn}})=\sqrt{\underset{d}{\mathrm{\&Sigma;}}{w}_{d}D(h_{\mathrm{mn}.d},h_{\mathrm{stmn}.d})},$ Wherein, w _dWeight for d feature passage.

At last, with the piecemeal matching degree M (h that obtains _Mn, h _Stmn) carry out record.

Step 107.e according to current search window matching result and subwindow matching result, calculates the comprehensive matching result of current search window and target.

Wherein, if use M _zThe comprehensive matching result of expression current search window and target, then comprehensive matching M as a result _zCan for: $M_z=w_{1}M(h_{\mathrm{win}},h_{\mathrm{std}})+\underset{1\&le;m\&le;M,1\&le;n\&le;N}{\mathrm{\&Sigma;}}{w}_{\mathrm{mn}}M(h_{\mathrm{mn}},h_{\mathrm{stmn}}),$ Wherein, w ₁Be search window global registration M (h as a result _Win, h _Std) weight, w _MnBe search window piecemeal matching result M (h _Mn, h _Stmn) weight.Weight w ₁And w _MnCan rule of thumb be worth setting, also can be provided with according to actual needs.

In addition, in this step, also can calculate the comprehensive matching result of current search window and target, even use M only according to all the subwindow matching results in the current search window _zThe comprehensive matching result of expression current search window and target, then comprehensive matching M as a result _zCan for: $M_z=\underset{1\&le;m\&le;M,1\&le;n\&le;N}{\mathrm{\&Sigma;}}{w}_{\mathrm{mn}}M(h_{\mathrm{mn}},h_{\mathrm{stmn}}),$

Wherein, w _MnBe search window piecemeal matching result M (h _Mn, h _Stmn) weight.Weight w _MnCan rule of thumb be worth setting, also can be provided with according to actual needs.

Step 107.f, judge whether selected search window all piecemeal coupling finish, if, execution in step 107.g then; Otherwise, return execution in step 107.b.

Step 107.g according to the comprehensive matching result, obtains the tracing positional of target.

In this step, according to comprehensive matching M as a result _z, therefrom choose M _zThe search window zone of value minimum is as the tracing positional of target.

If need to continue tracking target, then extract the next frame image, return execution in step 101.

Image tracking system in the present embodiment among the structure of image tracking system and annexation and embodiment one or the embodiment two is similar, function is also similar, and the description in the detailed implementation procedure that difference only is the tracing positional determination module 634 in the histogram coupling tracking cell 630 and the present embodiment in the step 107 is consistent.

At this moment, as shown in Figure 9, can comprise in the tracing positional determination module 634: search window is chosen submodule, search window and is determined that submodule, piece histogram calculation matched sub-block, search window comprehensive matching calculating sub module and tracing positional determine submodule.

Wherein, search window is chosen submodule, and the search window matching result that provides according to histogram matching module 633 is provided, and according to pre-conditioned, therefrom chooses qualified a plurality of search window, offers search window and determines submodule.

Search window is determined submodule, be used for choosing the definite current search window of search window that submodule provides from search window, and determined current search window is notified to piece histogram calculation matched sub-block, the global registration degree of current search window is offered search window comprehensive matching calculating sub module.

Piece histogram calculation matched sub-block is used for search window is determined that the current search window that submodule provides carries out piecemeal according to the method for partition corresponding with target, obtains subwindow; To the subwindow of each, utilize integration histogram to calculate the subwindow histogram, the subwindow histogram that calculated and the blocked histogram of target corresponding blocks are mated, obtain the piecemeal matching degree of each subwindow, resulting piecemeal matching degree is offered search window comprehensive matching calculating sub module.After all subwindow couplings were finished, the notice search window determined that submodule provides next current search window.

Search window comprehensive matching calculating sub module, be used for determining the piecemeal matching degree of all subwindows of the global registration degree of the current search window that submodule provides and the current search window that piece histogram calculation matched sub-block provides according to search window, calculate the comprehensive matching degree of current search window and target, and the comprehensive matching degree of the current search window that calculates is offered tracing positional determine submodule.

Wherein, the piecemeal matching degree of all subwindows in the current search window that search window comprehensive matching calculating sub module can be only provides according to piece histogram calculation matched sub-block is calculated the comprehensive matching result of current search window and target.At this moment, search window determines that submodule can need not to provide to search window comprehensive matching calculating sub module the global registration degree of current search window.

Tracing positional is determined submodule, and the comprehensive matching degree of all search windows of providing according to search window comprehensive matching calculating sub module is provided, and will coupling best search window zone is as the tracing positional of target.

Embodiment four:

In the present embodiment, on the basis of embodiment one, embodiment two or embodiment three, the combining target sorter carries out the Position Tracking of target.By the target classification device target is followed the tracks of roughly,, reduce operand, by the histogram coupling target is carried out meticulous tracking afterwards, to guarantee tracking effect with further raising real-time performance of tracking.

Referring to Figure 10, Figure 10 is the process flow diagram of image tracking method in the embodiment of the invention four.As shown in figure 10, this flow process comprises the steps:

Step 1001 is provided with tracing area in current frame image.

In the present embodiment, the method that tracing area is set can be consistent with the description in the step 101 among the embodiment one.

Step 1002, each search window that utilizes sorter may occur target in the whole tracing area are carried out the degree of confidence description, if exist degree of confidence to satisfy the search window of tracer request, then execution in step 1003; If do not exist degree of confidence to satisfy the search window of tracer request, then execution in step 1005.

Wherein, the size of hypothetical target is w _p* h _p, then under each possible yardstick c, yardstick c the width of corresponding search window be: w _c=c * w _p, highly be h _c=c * h _p, c ∈ C wherein, C be might yardstick set, as C={0.8,0.9,1.0,1.1,1.2} etc.

The normal window of hypothetical target sorter size is w again _Std* h _StdWhen then carrying out the tracking of target classification device, at first need each search window scaling that target in the tracing area may be occurred normal window size to the target classification device, judge whether this search window may be target, to storing for the search window of target, utilizing the target classification device to carry out degree of confidence to the search window of being stored then describes, if degree of confidence is greater than the threshold value that sets in advance, the search window that then exists degree of confidence to meet the demands, and can select the window of degree of confidence maximum, follow the tracks of successfully this moment; If cannot target exist, less than preset threshold value, then the search window that does not exist degree of confidence to meet the demands is followed the tracks of failure this moment as if degree of confidence.

Step 1003, the search window that degree of confidence is satisfied tracer request carries out the histogram coupling, and the match is successful as if histogram, and then execution in step 1004; Otherwise, further, but execution in step 1005.

In this step, can be that the search window that all degree of confidence satisfy tracer request is carried out the histogram coupling, also can be that the search window that the partial belief degree satisfies tracer request is carried out the histogram coupling, as choose the search window that several bigger degree of confidence of degree of confidence satisfy tracer request and carry out the histogram coupling, or the search window that a degree of confidence choosing the degree of confidence maximum satisfies tracer request carries out the histogram coupling.

Wherein, when the search window that degree of confidence is met the demands carries out the histogram coupling, can adopt the blocked histogram coupling, also can adopt overall histogram coupling, also the method that can adopt overall histogram to combine with blocked histogram is mated.When for example adopting overall histogram to mate, can for: after determining the set of histograms number, degree of confidence is satisfied the search window of tracer request, calculate the histogram group under the pixel in this search window, add up the number of pixels in each group, according to the number of pixels in set of histograms number and each group, obtain the histogram of this search window, the search window histogram that calculates and the standard histogram of target are mated, if the search window that exists the histogram matching result to meet the demands, then the match is successful for histogram, is called the search window that the match is successful with wherein mating best search window, otherwise it fails to match.

Wherein, the standard histogram of search window histogram and target can be a color histogram, also can be that color histogram adds gradient orientation histogram, can also be gradient orientation histogram.Concrete which kind of histogram that adopts is decided according to actual conditions.

Step 1004, with the search window that the match is successful the tracing positional as target, execution in step 1006 afterwards.

Step 1005 is carried out integration histogram calculating and histogram coupling following calculation, obtains the tracing positional of target.

Specific implementation process in this step can be identical to step 107 with the step 101 among the embodiment one, also can be identical to step 107 with the step 101 among the embodiment two, and can also be identical to step 107 with the step 101 among the embodiment three.

Step 1006 judges whether to need to continue tracking target, if, then extract the next frame image, and return execution in step 1001, otherwise, process ends.

So far, the image tracking method flow process among the embodiment four finishes.

In the above-mentioned flow process, step 1003 and step 1004 also can be omitted, then in the step 1002 if sorter is followed the tracks of successfully, the search window that degree of confidence is the highest in the then direct search window that degree of confidence is satisfied tracer request is as the tracing positional of target, execution in step 1006 then.

Above-mentioned image tracking method in the embodiment of the invention four is described in detail, again image tracking system in the embodiment of the invention four is described in detail below.

Referring to Figure 11, Figure 11 is the structural representation of image tracking system in the embodiment of the invention four.As shown in figure 11, this system has added target classification device tracking cell 1101 and histogram matching unit 1102 on the basis of system shown in Fig. 6 (a).

At this moment, tracing area is provided with unit 610, is further used for set tracing area is notified to target classification device tracking cell 1101.

Target classification device tracking cell 1101, each search window that is used for utilizing sorter may occur whole tracing area target carries out the degree of confidence description, if exist degree of confidence to satisfy the search window of tracer request, then follow the tracks of successfully, the search window that this degree of confidence is satisfied tracer request offers histogram matching unit 1102; If do not exist degree of confidence to satisfy the search window of tracer request, then follow the tracks of failure, send the notice of following the tracks of failure to domain integral computing unit 620.

Wherein, when the search window that target classification device tracking module satisfies tracer request with degree of confidence offers the histogram matching module, the search window that all degree of confidence can be satisfied tracer request offers the histogram matching module, the search window that also the partial belief degree can be satisfied tracer request offers the histogram matching module, as choose several bigger degree of confidence of degree of confidence and satisfy the search window of tracer request, or choose the search window that a degree of confidence of degree of confidence maximum satisfies tracer request and offer the histogram matching module.

Histogram matching unit 1102, the degree of confidence that being used to calculate target classification device tracking cell 1101 provides satisfies the histogram of the search window of tracer request, the search window histogram of calculating and the standard histogram of target are mated,, determine the tracing positional of target according to matching result.

Domain integral computing unit 620 is further used for: according to the notice of failing from the tracking of target classification device tracking cell, carry out the operation of described domain integral histogram calculation.

Wherein, also can need not histogram matching unit 1102 in this system, then target classification device tracking cell 1101 directly satisfies degree of confidence the tracing positional of the search window of degree of confidence maximum in the search window of tracer request as target when following the tracks of successfully.

In the foregoing description four, in the step 1004 of method flow shown in Figure 10, can further comprise: the search window that the match is successful is stored.

Then the method in the present embodiment can further comprise: when the number of the search window of storing reaches pre-conditioned, utilize the overall histogram of these search windows, calculate each group of histogram in each feature passage weight w (d, k), d ∈ D, wherein, if adopt color histogram, then feature passage D={r, g, b} (or D={y, u, v}); If adopt color histogram to add gradient orientation histogram, feature passage D={r then, g, b, G _Dir(or D={y, u, v, G _Dir); If only adopt gradient orientation histogram, then feature passage D=G _Dir

The number of supposing the search window stored is N ₀, determined histogrammic group of number is K, the process of then specifically calculating weight is as follows:

1, calculates the overall histogram h of N search window each group in each feature passage of being stored _Dkn, n ∈ (0,1 ..., N ₀-1), and k ∈ (0,1 ..., K-1).

2, calculate the overall histogrammic inequality M of each group in each feature passage _Hdk, variance Var _Hdk

If N search window comprises the search window of different scale, then calculate before inequality and the variance, the histogram of each group is converted to the histogram of each group of standard scale at first separately.And then carry out inequality and variance and calculate.

Concrete computation process is as follows:

$M_{\mathrm{hdk}}=\frac{\underset{n=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{h}_{\mathrm{dkn}}}{N},$ ${\mathrm{Var}}_{\mathrm{hdk}}=\frac{\underset{n=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(h_{\mathrm{dkn}}-M_{\mathrm{hdk}})}^2}{N};$

3, utilize above-mentioned inequality and variance, and calculating w (d, k).

$w{(d,k)}_1=\frac{{\mathrm{<figure-callout\; id="2"\; label="M\; hdk"\; filenames="A20071009897000411.png,A20071009897000413.png"\; state="\{\{state\}\}">M</figure-callout>}}_{\mathrm{hdk}}^{}}{{\mathrm{Var}}_{\mathrm{hdk}}+{\mathrm{\&sigma;}}^2},$ Wherein, σ ²Adjust parameter for variance, be used to avoid Var _HdkBe 0 situation, σ ²Value very little, as being 10 ^-5Deng.

For the weight that makes each group adds up to 1, need carry out normalization to the weight of each group, that is: $W(d,K)=\underset{k=0}{\overset{K-1}{\mathrm{\&Sigma;}}}w{(d,k)}_1,$ $w(d,k)=\frac{w{(d,k)}_1}{W(d,K)},$ k∈(0，1，…，K-1)。

Further,, then can also utilize the blocked histogram of the search window of being stored, calculate the weight w of the global registration result among the process step 107.e shown in Figure 8 if step 1005 is identical to step 107 with step 101 among the embodiment three ₁Weight w with the piecemeal matching result _Mn

Equally, the number of supposing the search window stored is N ₀, determined histogrammic group of number is K, supposes that again each search window is divided into M * N piece, the process of then specifically calculating weight is as follows:

1, calculates the blocked histogram h of N search window each group in each feature passage of being stored _Dkn(m, n), 1≤m≤M, 1≤n≤N.

2, calculate the inequality M of the blocked histogram of each group in each feature passage _Hdk(m, n), variance Var _Hdk(m, n).

If N search window comprises the search window of different scale, then calculate before inequality and the variance, the histogram of each group is converted to the histogram of each group of standard scale at first separately.And then carry out inequality and variance and calculate.

Concrete computation process is as follows:

$M_{\mathrm{hdk}}(m,n)=\frac{\underset{n=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{h}_{\mathrm{dkn}}(m,n)}{N},$ ${\mathrm{Var}}_{\mathrm{hdk}}(m,n)=\frac{\underset{n=0}{\overset{N-1}{\mathrm{\&Sigma;}}}{(h_{\mathrm{dkn}}(m,n)-M_{\mathrm{hdk}}(m,n))}^2}{N}.$

3, utilize above-mentioned inequality and variance, calculate w ₁And w _Mn

3.1 calculate w ₁: the weighted sum of calculating each feature passage variance of overall histogram: $\mathrm{Var}=\underset{d\&Element;D}{\mathrm{\&Sigma;}}{w}_c\underset{k=0}{\overset{K-1}{\mathrm{\&Sigma;}}}{\mathrm{Var}}_{\mathrm{hdk}};$

Calculate ${\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="A20071009897000413.png,A20071009897000423.png"\; state="\{\{state\}\}">w</figure-callout>}}_1=1/\sqrt{\mathrm{Var}+{\mathrm{\&sigma;}}^2},$ Wherein, σ ²Be that variance adjusts parameter, being used to avoid Var is 0 situation, same, σ ²Value very little, as being 10 ^-5Deng.

3.2 calculate w _Mn:

Calculate the weighted sum of each feature passage variance of blocked histogram:

$\mathrm{Var}(m,n)=\underset{d\&Element;D}{\mathrm{\&Sigma;}}{w}_c\underset{k=0}{\overset{K-1}{\mathrm{\&Sigma;}}}\mathrm{Va}{r}_{\mathrm{hdk}}(m,n);$

Calculate $w_{\mathrm{mn}.1}=1/\sqrt{\mathrm{Var}(m,n)+{\mathrm{\&sigma;}}^2},$ Equally, σ ²For variance is adjusted parameter.

For the weight that makes each piecemeal adds up to 1, need carry out normalization to the weight of each piecemeal, that is:

$W_{\mathrm{mn}}=\underset{k=0}{\overset{K-1}{\mathrm{\&Sigma;}}}{w}_{\mathrm{mn}.1},$ $w_{\mathrm{mn}}=\frac{w_{\mathrm{mn}.1}}{W_{\mathrm{mn}}},$ k∈(0，1，…，K-1)，1≤m≤M，1≤n≤N。

Correspondingly, in the system shown in Figure 11, also can further comprise: histogram weight updating block 1103, as shown in figure 12, Figure 12 is another structural representation of image tracking system in the embodiment of the invention four.As shown in figure 12, histogram matching unit 1102 is further used for the search window that the match is successful is offered histogram weight updating block 1103, histogram weight updating block 1103 is used for the histogram according to the search window of the predetermined number of storage, calculate the weight of each group of histogram in each feature passage, the weight of each group in each the feature passage that calculates is offered histogram coupling tracking cell 630, the weight of each group in each feature passage that histogram coupling tracking cell 630 is further used for providing according to histogram weight updating block 1103 is carried out the operation that the standard histogram to the histogram of search window and target mates.

During specific implementation, histogram weight updating block 1103 can offer the weight of each group in each the feature passage that calculates the histogram matching module 633 in the histogram coupling tracking cell 630, by the weight of histogram matching module 633, carry out the operation that the standard histogram to the histogram of search window and target mates according to each group in each feature passage.

In addition, further, histogram weight updating block 1103 also is used for the blocked histogram according to the search window of the predetermined number of storage, calculate search window global registration result's the weight and the weight of piecemeal matching result, the search window global registration result's that calculates the weight and the weight of piecemeal matching result are offered histogram coupling tracking cell 630, the search window global registration result's who is provided according to histogram weight updating block 1103 by histogram coupling tracking cell 630 the weight and the weight of piecemeal matching result are carried out the operation of calculating search window comprehensive matching result.

During specific implementation, histogram weight updating block 1103 can offer the weight of the global registration result's that calculated weight and piecemeal matching result the search window comprehensive matching calculating sub module in the tracing positional determination module 634, according to search window global registration result's the weight and the weight of piecemeal matching result, carry out the operation of calculating search window comprehensive matching result by search window comprehensive matching calculating sub module.

Above-described specific embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the above only is specific embodiments of the invention; and be not intended to limit the scope of the invention; within the spirit and principles in the present invention all, any modification of being made, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (19)

1, a kind of image tracking method is characterized in that, this method comprises:

By integral operation, calculating in the whole tracing area with whole tracing area predefined one jiao is the histogram of the All Ranges of starting point, obtains the domain integral histogram;

Utilize the domain integral histogram, calculate the histogram of each search window that target may occur in the whole tracing area, the histogram of each search window of calculating and the standard histogram of target are mated, obtain matching result, determine the tracing positional of target according to matching result.

2, method according to claim 1 is characterized in that, described integration histogram comprises: the color integration histogram, and/or, the gradient direction integration histogram.

3, method according to claim 1 and 2, it is characterized in that, the described domain integral histogram that utilizes, the histogram that calculates each search window that target may occur is: each search window that may occur target, domain integral histogram with four angle correspondences of this search window carries out plus and minus calculation, obtains the histogram of this search window.

4, method as claimed in claim 3, it is characterized in that, if predefined one jiao is the upper left corner, then described domain integral histogram with four angle correspondences of search window carries out plus and minus calculation and is specially: after the domain integral histogram addition that the domain integral histogram of this search window lower right corner correspondence is corresponding with this search window upper left corner, deduct the domain integral histogram of this search window upper right corner correspondence and the domain integral histogram of lower left corner correspondence, obtain the histogram of this search window.

5, method according to claim 1 and 2 is characterized in that, describedly comprises by calculating the domain integral histogram:

Determine the set of histograms number;

Calculate the histogram group under each pixel in the whole tracing area;

Add up in the whole tracing area with the All Ranges C of predefined one jiao of O as starting point _{I, j}In belong to other domain integral number of pixels of each set of histograms;

According to set of histograms number and each C _{I, j}In other domain integral number of pixels of each set of histograms, obtain each domain integral histogram.

6, method as claimed in claim 5 is characterized in that, all C in the whole tracing area of described statistics _{I, j}In belong to other domain integral number of pixels of each set of histograms and be specially:

According to the histogram group information under each pixel, on the direction on a limit under the whole tracing area O, the pixel number in each group is carried out the addition recursion calculate, obtain the direction integral number of pixels of each group on this direction;

To the described direction integral number of pixels of each group of obtaining, on the direction on another limit under the whole tracing area O, carry out the addition recursion and calculate, obtain each C _{I, j}In the domain integral number of pixels of each group.

7, method according to claim 5 is characterized in that, when integration histogram comprised the gradient direction integration histogram, the histogram group under each pixel of described calculating comprised:

Determine the angular interval of gradient direction according to the set of histograms number;

Calculate the affiliated angular interval of gradient direction of each pixel, obtain the affiliated histogram group of this pixel.

8, method according to claim 7 is characterized in that, the angular interval under the gradient direction of described each pixel of calculating comprises:

Calculate the tangent value of the frontier point of angular interval, obtain the tangent value interval;

Calculate the ratio of each pixel,, obtain the angular interval under the gradient direction of each pixel according to the residing tangent value of this ratio interval at the Grad of the Grad of the y of coordinate axis direction and x direction.

9, method according to claim 1 and 2 is characterized in that, the standard histogram of described target comprises: the overall histogram of target; Then described the histogram of each search window of being calculated and the standard histogram of target are mated, obtain matching result and comprise:

The overall histogram of each search window of being calculated and the overall histogram of target are mated, obtain the global registration degree of search window, with the global registration degree of resulting search window as matching result.

10, method according to claim 9 is characterized in that, the standard histogram of described target also comprises: with the blocked histogram behind the target piecemeal; Then obtain after the global registration degree of search window, the global registration degree of this search window as before the matching result, further comprised:

According to the global registration degree of search window, obtain a plurality of global registration degree and satisfy pre-conditioned search window;

Described a plurality of search windows are carried out piecemeal according to the partitioned mode corresponding with target respectively, obtain the subwindow of each search window;

Utilize the domain integral histogram, calculate the histogram of each subwindow, the histogram of each subwindow of obtaining and the blocked histogram of target corresponding blocks are mated, obtain the piecemeal matching degree of each subwindow;

According to the piecemeal matching degree of each subwindow in the global registration degree of each search window and this search window, obtain the matching result of each search window.

11, method according to claim 1 and 2 is characterized in that, calculates before the integration histogram of whole tracing area, further comprises:

Each search window that utilizes sorter may occur target in the whole tracing area carries out degree of confidence to be described, if do not exist degree of confidence to satisfy the search window of tracer request, then carries out the operation of the integration histogram of the whole tracing area of described calculating.

12, method according to claim 11, it is characterized in that, this method further comprises: if exist degree of confidence to satisfy the search window of tracer request, then the search window that degree of confidence is satisfied tracer request carries out the histogram coupling, it fails to match as if histogram, then carries out the operation of the integration histogram of the whole tracing area of described calculating; The match is successful as if histogram, and then general's search window that the match is successful is as the tracing positional of target.

13, method according to claim 12, it is characterized in that, this method further comprises: search window that will the match is successful is stored, and according to the histogram of the search window of the predetermined number of storage, calculates the weight of each group of histogram in each feature passage;

The then described histogram and the standard histogram of target with search window mates and is: according to the weight of each group in each feature passage, the histogram of search window and the standard histogram of target are mated.

14, a kind of image tracking system is characterized in that, this system comprises: tracing area is provided with unit, domain integral computing unit and histogram coupling tracking cell, wherein,

Tracing area is provided with the unit, is used for being provided with at current frame image the tracing area of target, and set tracing area is notified to domain integral computing unit and histogram coupling tracking cell;

The domain integral computing unit, be used to by integral operation, calculating in the whole tracing area with whole tracing area predefined one jiao is the histogram of the All Ranges of starting point, obtains the domain integral histogram, and the domain integral histogram that obtains is offered histogram coupling tracking cell;

Histogram coupling tracking cell, the domain integral histogram that utilizes the domain integral computing unit to provide is provided, calculate the histogram of each search window that target may occur in the whole tracing area, the histogram of each search window of calculating and the standard histogram of target are mated, obtain the tracing positional of target according to matching result.

15, system according to claim 14, it is characterized in that, this system further comprises: target classification device tracking cell, each search window that is used for utilizing sorter may occur whole tracing area target carries out the degree of confidence description, if do not exist degree of confidence to satisfy the search window of tracer request, then send the notice of following the tracks of failure to described domain integral computing unit;

Described domain integral computing unit according to the notice of failing from the tracking of target classification device tracking cell, is carried out the operation of described domain integral histogram calculation.

16, system according to claim 15 is characterized in that, this system further comprises: the histogram matching unit;

Then target classification device tracking cell is further used for: if exist degree of confidence to satisfy the search window of tracer request, the search window that then described degree of confidence is satisfied tracer request offers the histogram matching unit;

The histogram matching unit, the degree of confidence that being used to calculate target classification device tracking cell provides satisfies the histogram of the search window of tracer request, the search window histogram of calculating and the standard histogram of target are mated,, determine the tracing positional of target according to matching result.

17, system according to claim 16 is characterized in that, this system further comprises: histogram weight updating block;

Then the histogram matching unit is further used for: the histogram search window that the match is successful is offered histogram weight updating block;

Histogram weight updating block is used for the histogram of search window according to the predetermined number of storage, calculates the weight of each group of histogram in each feature passage, and the weight of each group in each the feature passage that calculates is offered histogram coupling tracking cell;

Histogram coupling tracking cell is further used for: the weight of each group in each the feature passage that provides according to histogram weight updating block, carry out described histogram and the operation of mating of the standard histogram of target to search window.

According to each described system in the claim 14 to 17, it is characterized in that 18, described histogram coupling tracking cell comprises: search window determination module, histogram calculation module, histogram matching module and tracing positional determination module, wherein,

The search window determination module is used for from tracing area the definite current search window of tracing area that the unit provides being set, and determined current search window is notified to the histogram calculation module;

The histogram calculation module is used to utilize the histogram of the domain integral histogram calculation current search window that the domain integral computing unit provides, and the histogram of the current search window that calculates is offered the histogram matching module; Notice search window determination module provides next current search window;

The histogram matching module is used for the histogram of current search window that the histogram calculation module is provided and the standard histogram of precalculated target and mates, and matching result is offered the tracing positional determination module;

The tracing positional determination module, the matching result of all search windows that the target that is used for providing according to the histogram matching module may occur, the search window zone that coupling is best is as the tracing positional of target.

19, system as claimed in claim 18, it is characterized in that, described tracing positional determination module comprises: search window is chosen submodule, search window and is determined that submodule, piece histogram calculation matched sub-block, search window comprehensive matching calculating sub module and tracing positional determine submodule, wherein

Search window is chosen submodule, and the matching result of all search windows of providing according to the histogram matching module is provided, and therefrom chooses and satisfies pre-conditioned a plurality of search windows, offers search window and determines submodule;

Search window is determined submodule, is used for choosing the definite current search window of search window that submodule provides from search window, and determined current search window is notified to piece histogram calculation matched sub-block;

Piece histogram calculation matched sub-block is used for search window is determined that the current search window that submodule provides carries out piecemeal according to the method for partition corresponding with target, obtains subwindow; To each subwindow, utilize integration histogram to calculate the subwindow histogram, the subwindow histogram that calculated and the blocked histogram of target corresponding blocks are mated, obtain the piecemeal matching degree of each subwindow, resulting piecemeal matching degree is offered search window comprehensive matching calculating sub module; The notice search window determines that submodule provides next current search window;

Search window comprehensive matching calculating sub module, the piecemeal matching degree that all subwindows of the current search window that provides according to piece histogram calculation matched sub-block are provided, calculate the comprehensive matching degree of current search window, and the comprehensive matching degree of the current search window that calculates is offered tracing positional determine submodule;

Tracing positional is determined submodule, and the comprehensive matching degree of all search windows of providing according to search window comprehensive matching calculating sub module is provided, and will coupling best search window zone is as the tracing positional of target.

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