CN101727570B - Tracking method, track detection processing unit and monitor system - Google Patents

Abstract

An embodiment of the invention discloses a tracking method, a track detection processing unit and a monitor system. The tracking method comprises: predicting the position of the movement of a target to obtain the predicted position of the target; extracting the prospect according to the obtained predicted position and determining a connected domain, making statistic to obtain the characteristic parameters of the connected domain; judging that the target characteristic update requirements are met by the result of the comparison between the characteristic parameters of the connected domain and the original characteristic parameters of the target, updating the original characteristic parameters of the target through the characteristic parameters of the connected domain matched during the comparison, and determining the position of the matched connected domain as the matched position for tracking the target. The invention is capable of well solving the problem that the characteristics of the target change in the process of tracking the target.

Description

Tracking, detection tracking processing equipment and supervisory system

Technical field

The present invention relates to the computer vision research technical field, be specifically related to a kind of tracking, detection tracking processing equipment and supervisory system.

Background technology

It is a research topic in the computer vision that objective self-adapting is followed the tracks of, how effectively interesting target effectively to be followed the tracks of, and be the gordian technique in the video monitoring system.Occurred many algorithm of target tracking at present, can roughly be divided into several types: based on the track algorithm of " filtering, data allocations ", based on the track algorithm of " Target Modeling, location " with based on the track algorithm of " motion detection " etc.

(1) track algorithm based on " filtering, data allocations " comprises Kalman filtering algorithm, expanded Kalman filtration algorithm, particle filter algorithm etc.This type of track algorithm adopts the method for " state space " that discrete dynamic system is carried out modeling mostly.For example, Kalman filtering algorithm can be according to next dbjective state constantly of historic state prediction of target.(2) based on the track algorithm of " Target Modeling, location " comprise algorithm based on invariant moments, based on the algorithm of template matches and Mean-Shift algorithm (average drifting algorithm) etc.This type track algorithm generally is made up of three parts: the modeling of target, measuring similarity, search matched.Wherein, the Mean-Shift algorithm need not carry out exhaustive search, and the algorithm real-time is good, is a single characteristic parameter algorithm; Its adopts kernel function histogram-modeling, to the edge stop, the rotation of target, distortion be all insensitive, also is a kind of high performance pattern matching algorithm.(3) track algorithm based on motion detection comprises Pfinder algorithm and W ⁴Algorithms etc., this type track algorithm depends on motion detection algorithm.

In the process that target is followed the tracks of, can there be the target signature variation issue.

In the prior art,, can adopt W for the target signature variation issue ⁴Algorithm is followed the tracks of.W ⁴In the algorithm be adopt minimum, maximum intensity value and maximum time difference value be that each pixel is carried out statistical modeling in the scene; Extract moving target; Realization is to detection, the tracking of target, and the line period property of going forward side by side ground context update therefore can processing target changing features problem on a certain degree.

In research and practice process to prior art, the inventor finds that there is following problem in prior art:

The W that prior art is adopted for the target signature variation issue ⁴Algorithm; Be to rely on motion detection algorithm fully, when in prospect is extracted, more noise spot occurring, follow the tracks of frame violent variation can take place; If the situation of division takes place in the detected target of detection algorithm; Then may follow the tracks of a target as two targets this moment, thereby and when moving target blocked the mutual adhesion of the prospect that causes extracting each other, can be used as a target to two targets this moment.

Therefore, prior art side does not have well to solve object appearing changing features problem in the process that target is followed the tracks of.

Summary of the invention

The embodiment of the invention provides a kind of tracking, detection tracking processing equipment and supervisory system, can solve preferably target is carried out the target signature variation issue in the tracing process.

According to an aspect of the present invention, a kind of tracking is provided, comprises:

The position of target of prediction motion obtains the predicted position of said target;

Carry out foreground extraction according to the said predicted position that obtains, and definite connected domain, statistics obtains the characteristic parameter of connected domain;

The result who compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain judges and meets target signature more after the new demand; The characteristic parameter of the said connected domain that is matched during through comparison upgrades the original characteristic parameter of said target, and the position of confirming the said connected domain that matches is as the matched position that target is followed the tracks of;

The result who compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain judges and does not meet target signature more after the new demand; With said predicted position is starting point; Adopt the track algorithm of based target modeling, location to obtain tracing area, statistics obtains the characteristic parameter of tracing area;

According to the characteristic parameter of said tracing area and result that the original characteristic parameter of said target compares judge meet similar requirement after, confirm with said tracing area as the matched position that target is followed the tracks of.

According to a further aspect in the invention, a kind of detection tracking processing equipment is provided also, comprises:

The target prodiction module is used for the position that target of prediction moves, and obtains the predicted position of said target;

Target detection and characteristics analysis module are used for carrying out foreground extraction according to the said predicted position that obtains, and definite connected domain, and statistics obtains the characteristic parameter of connected domain;

Target signature is upgraded decision-making module, is used for the result that characteristic parameter and the original characteristic parameter of said target according to said connected domain compare and judges whether to meet more new demand of target signature;

The target signature update module; Being used for upgrading decision-making module in said target signature judges and meets target signature more after the new demand; The characteristic parameter of the said connected domain that is matched during through comparison upgrades the original characteristic parameter of said target, and the position of confirming the said connected domain that matches is as the matched position that target is followed the tracks of;

Follow the tracks of and the target judging module; Being used for upgrading decision-making module in said target signature judges and does not meet target signature more after the new demand; With said predicted position is starting point; Adopt the track algorithm of based target modeling, location to obtain tracing area; Statistics obtains the characteristic parameter of tracing area, and according to the characteristic parameter of said tracing area and result that the original characteristic parameter of said target compares judge meet similar requirement after, definite with said tracing area as the matched position that target is followed the tracks of.

Another aspect of the present invention also provides a kind of supervisory system, comprises the IMAQ input equipment and detects tracking processing equipment, and said IMAQ input equipment is used for to the image of said detection tracking processing equipment input to the target collection; Said detection tracking processing equipment comprises:

The target prodiction module is used for the image according to the target of said IMAQ input equipment input, and the position of target of prediction motion obtains the predicted position of said target;

Target detection and characteristics analysis module are used for carrying out foreground extraction according to the said predicted position that obtains, and definite connected domain, and statistics obtains the characteristic parameter of each connected domain;

Target signature is upgraded decision-making module, is used for the result that characteristic parameter and the original characteristic parameter of said target according to said connected domain compare and judges whether to meet more new demand of target signature;

The target signature update module; Being used for upgrading decision-making module in said target signature judges and meets target signature more after the new demand; The characteristic parameter of the said connected domain that is matched during through comparison upgrades the original characteristic parameter of said target, and the position of confirming the said connected domain that matches is as the matched position that target is followed the tracks of;

Follow the tracks of and the target judging module; Being used for upgrading decision-making module in said target signature judges and does not meet target signature more after the new demand; With said predicted position is starting point; Adopt the track algorithm of based target modeling, location to obtain tracing area; Statistics obtains the characteristic parameter of tracing area, and according to the characteristic parameter of said tracing area and result that the original characteristic parameter of said target compares judge meet similar requirement after, definite with said tracing area as the matched position that target is followed the tracks of.

The technical scheme that the embodiment of the invention provides; Owing to obtained the predicted position of target; Be to utilize said predicted position when foreground extraction; And counted the characteristic parameter of each connected domain; The result who further compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain then judges whether to meet more new demand of target signature, meets target signature more after the new demand judging so, just can utilize the characteristic parameter of said connected domain that the original characteristic parameter of said target is upgraded; The clarification of objective parameter information is upgraded in time; Thereby can obtain the latest features situation of target, so just can more effectively keep tracking, avoid when detecting target generation division, can following the tracks of a target as two targets or when moving target blocks each other, can being used as two targets the problem appearance of a target target.

Description of drawings

In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art; To do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below; Obviously, the accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills; Under the prerequisite of not paying creative work property, can also obtain other accompanying drawing according to these accompanying drawings.

Fig. 1 is the embodiment of the invention one a tracking process flow diagram;

Fig. 2 is the embodiment of the invention two tracking process flow diagrams;

Fig. 3 is the treatment scheme synoptic diagram of the single detection tracking processing of embodiment of the invention equipment;

Fig. 4 is that the embodiment of the invention detects tracking processing device structure synoptic diagram.

Embodiment

To combine the accompanying drawing in the embodiment of the invention below, the technical scheme in the embodiment of the invention is carried out clear, intactly description, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills are not making the every other embodiment that is obtained under the creative work prerequisite, all belong to the scope of the present invention's protection.

The embodiment of the invention provides a kind of tracking, can solve preferably target is carried out the target signature variation issue in the tracing process.The embodiment of the invention is mainly through combining utilization with several kinds of track algorithms, and corresponding proposition decision mechanism and disposal route, thereby more effectively solves the problem of the object appearing changing features in the process of monitoring and tracking target.

Seeing also Fig. 1, is the embodiment of the invention one tracking process flow diagram, comprises step:

The position of step 101, target of prediction motion obtains the predicted position of said target;

In this step, can adopt track algorithm, for example, adopt the position of Kalman filtering algorithm target of prediction motion wherein, obtain the predicted position of said target based on " filtering, data allocations ".

Step 102, carry out foreground extraction according to the said predicted position that obtains, and definite connected domain, statistics obtains the characteristic parameter of connected domain;

In this step, can adopt track algorithm based on motion detection.Predicted position according to step 101 obtains is carried out local foreground extraction, obtains a bianry image, in this bianry image, carries out connected component labeling, confirms connected domain, and statistics obtains the characteristic parameter of each connected domain.Here said characteristic parameter comprises eigenwert probability distribution, pixel count, length breadth ratio, the dispersion degree of color of object, describes target through these characteristic parameters.

Step 103, the result who compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain judge and meet target signature more after the new demand; The characteristic parameter of the said connected domain that is matched during through comparison upgrades the original characteristic parameter of said target, and the position of confirming the said connected domain that matches is as the matched position that target is followed the tracks of.

In this step, the result who compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain judge meet target signature more new demand comprise:

The determined coefficient of similarity value of eigenwert probability distribution according to the color of object of the eigenwert probability distribution of the color of object of each connected domain and said target matches a similar area;

Whether the pixel count, length breadth ratio, dispersion degree of judging said similar area belongs to setting range with the ratio of the pixel count of said target, length breadth ratio, dispersion degree respectively; If not; Confirm not meet more new demand of target signature, if confirm to meet more new demand of target signature.

Can find out from this embodiment; Owing to obtained the predicted position of target; Be to utilize said predicted position when foreground extraction; And counted the characteristic parameter of each connected domain; The result who further compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain then judges whether to meet more new demand of target signature, meets target signature more after the new demand judging so, just can utilize the characteristic parameter of said connected domain that the original characteristic parameter of said target is upgraded; The clarification of objective parameter information is upgraded in time; Thereby can obtain the latest features situation of target, so just can more effectively keep tracking, avoid when detecting target generation division, can following the tracks of a target as two targets or when moving target blocks each other, can being used as two targets in the prior art problem appearance of a target target.

Seeing also Fig. 2, is the embodiment of the invention two tracking process flow diagrams.Embodiment two has introduced the tracking processing process more in detail than embodiment one, comprises step among Fig. 2:

Step 201, adopt based on the track algorithm target of prediction of " filtering, data allocations " position at present frame;

Based on the track algorithm of " filtering, data allocations ", be that example describes but is not limited to this in this step to adopt Kalman filtering algorithm.

For video monitoring scene, the position of the target in the scene in each two field picture constituted the track of target travel.Adopt the purpose of Kalman filter in this step; Be for possible position according to target in the prediction of the positional information before the target present frame; So state variable that obtains in the Kalman filter and observed reading are the positional information of target, the relevant information of the centre coordinate of the target of more precisely being followed the tracks of.

For Kalman filtering algorithm, generally handle by following formula:

Signal model: X (k)=A (k-1) X (k-1)+B (k) W (k) (1)

Observation model: Y (k)=C (k) X (k)+V (k) (2)

Wherein: X (k), Y (k) are respectively state vector and observation vector; A (k-1), B (k), C (k) are respectively state-transition matrix, input matrix, observing matrix; W (k) and V (k) are signal noise and observation noise, and W (k) and V (k) are white Gaussian noise, and the obedience average is 0 multivariate normal distribution; Each component variance equates, is respectively σ _v=σ _w=5.

Suppose the motion of moving target center on X, Y axle all be one by quickening at random by the rectilinear motion of disturbance; Acceleration alpha is to satisfy a random quantity of Gaussian distribution; Average is 0; To be that

α satisfies

distribute variance, here acceleration alpha signal noise w (k) just.

So, make signal vector X (k)=[x (k) y (k) x ' (k) y ' (k)] ^T, x (k) wherein, y (k) is respectively the location components of target's center on x, y axle, x ' is (k), y ' is respectively speed on x, y axle (k).Observation vector Y (k)=[xc (k) yc (k)] ^T, x wherein _c(k), y _c(k) be the observed reading of target's center position on X, Y axle respectively, observation noise v (k) satisfies Distribute.

According to above-mentioned definition, above-mentioned two models mentioning can be expressed as:

$\left[\begin{array}{c}x\left(k\right)\\ y\left(k\right)\\ x^{\′}\left(k\right)\\ y^{\′}\left(k\right)\end{array}\right]=\left[\begin{array}{cccc}1& 0& t& 0\\ 0& 1& 0& t\\ 0& 0& 1& 0\\ 0& 0& 0& 0\end{array}\right]\left[\begin{array}{c}x(k-1)\\ y(k-1)\\ x^{\′}(k-1)\\ y^{\′}(k-1)\end{array}\right]+\left[\begin{array}{c}{t}^2/2\\ t^2/t2\\ t\\ t\end{array}\right]w\left(k\right)---\left(3\right)$

$\left[\begin{array}{c}{x}_c\left(k\right)\\ y_c\left(k\right)\end{array}\right]=\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& 1& 0& 0\end{array}\right]\left[\begin{array}{c}x\left(k\right)\\ y\left(k\right)\\ x^{\′}\left(k\right)\\ y^{\′}\left(k\right)\end{array}\right]+\left[\begin{array}{c}1\\ 1\end{array}\right]v\left(k\right)---\left(4\right)$

Can set the constant empirical value is: t=1, σ _v=σ _w=5; And initial value X (1)=[x _Sy _S0 0] ^T, x wherein _S, y _SThe centre coordinate of target in the expression start frame.

With the value substitution of correlation parameter above-mentioned (3) and (4), then this step can obtain the position of target at present frame according to the position prediction before the target, promptly obtains a predicted position.

Step 202, employing are carried out the clarification of objective parametric statistics based on the track algorithm of motion detection;

Adopt track algorithm to carry out the clarification of objective parametric statistics in this step, before introducing this step in detail, introduce relevant characteristic parameter earlier and describe based on motion detection.

For the target of following the tracks of, can use the characteristic parameters such as eigenwert probability distribution

pixel count C, length breadth ratio R, dispersion degree D of color of object to describe target.Wherein, Dispersion degree is defined as the area of object in the image and the ratio of girth, that is:

is used to represent the degree of scatter of target object on image.

Below introducing the eigenwert probability distribution

of color of object in detail hypothetical target is centered close to wherein has the expression with

of n pixel; The pixel color rgb value is m through the number of the eigenwert bin after quantizing; Bin is meant an interval quantity of value, and then the eigenwert probability distribution of this color of object can be expressed as:

${\hat{q}}_u=\mathrm{Const}*\underset{i=1}{\overset{n}{\mathrm{\Σ}}}k\left({\left|\right|\frac{{\stackrel{\→}{x}}_i-{\stackrel{\→}{x}}_0}{h}\left|\right|}^2\right)\mathrm{\δ}[b\left({\stackrel{\→}{x}}_i\right)-u]---\left(5\right)$

Wherein, k (x) is the profile function of gaussian kernel function, k (x)=e ^xOwing to block or the influence of background, near the pixel the object module center is more reliable than peripheral pixel, and k (x) is to distribute big weights to the pixel at center, and is to distribute little weights for deep pixel.The effect of

is the influence in order to eliminate different big or small targets and to calculate among the function k (x), and the target of ellipse representation is normalized to a unit circle.δ (x) is the Delta function;

is pixel color value; Total effect of

is whether the pixel color quantized value of judging position in the target area

belongs between u chromatic zones; If belong to; Then

value is 1, otherwise is 0.Const is a standardized constant factor, makes

therefore:

$\mathrm{Const}=\frac{1}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}k\left({\left|\right|\frac{{\stackrel{\→}{x}}_i-{\stackrel{\→}{x}}_0}{h}\left|\right|}^2\right)}---\left(6\right)$

After the description introduction of relevant feature parameters is intact, below introduce the specific operation process of this step:

1) target is carried out local foreground extraction;

The algorithm that carries out foreground extraction for target in this step can be to adopt based on any one target detection in the track algorithm of motion detection and foreground extraction algorithm, for example background subtraction algorithm, mixed Gauss model algorithm, scheme partitioning algorithm or the like.

Because moving target has the continuity in time and space between adjacent two frames; Therefore need in the scope of the overall situation, not carry out motion target detection in this step; Be benchmark only with the predicted position that obtains through step 201; In the subrange of 1.5 to 2 times of target sizes of this place-centric, carry out just passablely, so just accelerated the speed of moving object detection greatly.

Use the mixed Gauss model algorithm in the embodiment of the invention,, target is carried out local foreground extraction, obtain a bianry image according to existing mixed Gauss model algorithm process process.

2) the provincial characteristics parametric statistics of target signature.

Above-mentionedly obtain a bianry image, in this bianry image, carried out connected component labeling so.The connected domain here is to adopt four mode of communicating, and for two pixels, if there is a place to link to each other in the four direction of upper and lower, left and right, then two points are connectivity points, and connected domain is the set of the point that is interconnected.

Through behind the connected component labeling, obtain n connected domain { Q _iI=1 ... N, the central point of each connected domain does

At this moment, carry out the provincial characteristics parametric statistics of target signature, comprise the pixel count C that adds up each connected domain _i, length breadth ratio R _i, dispersion degree D _iWith the eigenwert probability distribution of statistics original image at the color of object of each connected domain

According to the formula in above-mentioned (5), can obtain

${\hat{p}}_{\mathrm{ui}}\left({\stackrel{\→}{y}}_i\right)=\mathrm{Const}*\underset{j=1}{\overset{n}{\mathrm{\Σ}}}k\left({\left|\right|\frac{{\stackrel{\→}{x}}_j-{\stackrel{\→}{y}}_i}{h}\left|\right|}^2\right)\mathrm{\δ}[b\left({\stackrel{\→}{x}}_i\right)-u]---\left(7\right)$

Through 1) and 2) processing, then step 202 has obtained the ASSOCIATE STATISTICS value of target signature parameter.

Step 203, judge whether to meet more new demand of target signature, if, get into step 204, if not, get into step 206;

In this step, suppose that the color feature value probability distribution of tracking target does

Pixel count is C _Target, length breadth ratio R _Target, dispersion degree D _Target

At first, utilize tracking target

With the eigenwert probability distribution of original image at the color of object of each connected domain

Match the highest regional O of similarity _i

similarity with

is with Bhattacharrya coefficient (coefficient of similarity; Be also referred to as Pasteur's coefficient)

measure distribution, promptly

$\widehat{\mathrm{\ρ}}\left(\stackrel{\→}{y}\right)\≡\mathrm{\ρ}[p\left(\stackrel{\→}{y}\right),q]=\underset{u=1}{\overset{m}{\mathrm{\Σ}}}\sqrt{{\hat{p}}_{\mathrm{ui}}\left(\stackrel{\→}{y}\right){\hat{q}}_{\mathrm{ut}\arg \mathrm{et}}}---\left(8\right)$

Coefficient of similarity Big more, show that then similarity is high more, thereby, draw the highest regional O of similarity according to each comparative result _i

Then, according to the highest O of similarity that matches _iThe zone, check O _iThe pixel count C in zone _i, length breadth ratio R _i, dispersion degree D _iDeng the character pair compared with parameters of characteristic parameter and target whether within an acceptable scope.If satisfy following three conditions, then be true, otherwise be false.

${\mathrm{\&sigma;}}_C<\frac{C_i}{C_{t\arg \mathrm{et}}}<1/{\mathrm{\&sigma;}}_C$

${\mathrm{\&sigma;}}_R<\frac{R_i}{R_{t\arg \mathrm{et}}}<1/{\mathrm{\&sigma;}}_R$

${\mathrm{\&sigma;}}_D<\frac{D_i}{D_{t\arg \mathrm{et}}}<1/{\mathrm{\&sigma;}}_D$

Wherein, σ _CBe O _iProportion threshold value is counted, σ with object pixel in the zone _DBe O _iZone and target dispersion degree proportion threshold value, σ _RBe O _iZone and target length breadth ratio proportion threshold value.σ _C, σ _D, σ _RCan be the value between the 0-1, specifically different settings can be arranged according to condition of different.σ in case study on implementation of the present invention _C, σ _D, σ _RIn all value be 0.6, can certainly get other values.

If O _iThe character symbol in zone closes and states decision mechanism, then assert O _iBe the position of tracking target, confirm to meet more new demand of target signature, get into next step and adopt O _iThe characteristic in zone is upgraded the characteristic parameter of tracking target.

Step 204, carry out target signature and upgrade; According to the decision mechanism in the step 203, assert O _iBe tracking target, confirm to meet target signature more after the new demand, in this step, adopt O _iThe characteristic in zone is upgraded the characteristic parameter of tracking target, also is equivalent to simultaneously carry out the self-adaptation adjustment to following the tracks of frame, and concrete operations are following:

${\hat{q}}_{\mathrm{ut}\arg \mathrm{et}}={\mathrm{\&gamma;}}_Q*{\hat{p}}_{\mathrm{ui}}\left({\stackrel{\&RightArrow;}{y}}_i\right)+(1-{\mathrm{\&gamma;}}_Q)*{\hat{q}}_{\mathrm{ut}\arg \mathrm{et}}$

C _target＝γ _C*C _i+(1-γ _C)*C _target

R _target＝γR*R _i+(1-γ _R)*R _target

D _target＝γ _D*D _i+(1-γ _D)*D _target

Wherein, γ _Q, γ _C, γ _R, γ _DBe respectively and follow the tracks of the color feature value probability distribution

Pixel count C _Target, length breadth ratio R _Target, dispersion degree D _TargetTurnover rate.In case study on implementation of the present invention with γ _QValue is 0.05, can be with γ _C, γ _RAnd γ _DAll value is 0.2, can certainly get other values.

Through above-mentioned processing; The clarification of objective parameter information is upgraded in time; Just can obtain the latest features situation of target; Thereby can more effectively keep tracking, avoid when detecting target generation division, can following the tracks of a target as two targets or when moving target blocks each other, can being used as two targets in the prior art problem appearance of a target target.

Step 205, confirm that the connected domain position that matches is the matched position of target, get into step 209;

Because confirmed to meet more new demand of target signature, and be to adopt O _iThe characteristic in zone is upgraded the characteristic parameter of tracking target, therefore confirms that the connected domain that matches is O _iThe zone is the matched position of target, and this position is the optimal location of tracking, gets into step 209 then and carries out tracking results output.

Need to prove there is not inevitable ordinal relation between the step 204 and 205.

Step 206, be starting point, adopt and follow the tracks of, get into step 207 based on the track algorithm of " Target Modeling, location " with the predicted position;

According to the decision mechanism in the step 203, if do not assert O _iBeing tracking target, promptly not meeting target signature more after the new demand, then is starting point with the predicted position, adopts and follows the tracks of based on the track algorithm of " Target Modeling, location ".

Adopt track algorithm in this step, illustrate with the Mean-Shift algorithm but be not limited to this based on " Target Modeling, location ".

For making Bhattacharrya coefficient

maximum; Promptly will be in present frame; Seek the optimal objective position, in

neighborhood, seek optimal objective position

with the predicted position that adopts the Kalman filtering prediction to obtain in the step 201 as the center

of the position of present frame search window in this step

Mean-Shift algorithm iteration process is following:

If it is h that target has the size that the position of

Kalman filtering prediction is positioned at

target area, repeats following steps so and can obtain target reposition

A, estimate in the present frame with formula (5) The eigenwert probability distribution of the color of place's candidate target $p_u\left({\stackrel{\&RightArrow;}{y}}_0\right).$

B, use formula Each point in the zoning

Weights { ω _iI=1 ... N.

C, use Mean-Shift algorithm, calculate the target reposition:

${\stackrel{\&RightArrow;}{y}}_1=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&RightArrow;}{x}}_i{\mathrm{\&omega;}}_{i}g\left[{\left|\right|\frac{{\stackrel{\&RightArrow;}{y}}_0-{\stackrel{\&RightArrow;}{x}}_i}{h}\left|\right|}^2\right]}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\mathrm{\&omega;}}_{i}g\left[{\left|\right|\frac{{\stackrel{\&RightArrow;}{y}}_0-{\stackrel{\&RightArrow;}{x}}_i}{h}\left|\right|}^2\right]}$

In the formula, g (x) is for being similarly the profile function of kernel function, and g (x)=-k ' (x)=-e ^x

D if

then stop to calculate; Otherwise

changes steps A, and wherein choosing of ε should make between

and

distance less than a pixel.

After through the Mean-Shift algorithm convergence, obtain target area O _m, target area O _mCentral point do ${\stackrel{\&RightArrow;}{y}}_m.$

Step 207, judge whether similarity meets similar requirement, if, get into step 208, if not, get into step 209;

The target area O that in step 206, obtains _mAfter, calculate O according to formula (5) _mThe color feature value probability distribution do Calculate according to formula (8) With

The Bhattacharrya coefficient

This coefficient tolerance

With Similarity.

Judge whether the similarity of

and

meets similar requirement this moment; Concrete through

and pre-set threshold compare and obtain, this threshold value can be provided with different values as the case may be:

If

is greater than threshold value; Expression meets similar requirement; Get into step 208; If

is less than threshold value; Expression does not meet similar requirement, gets into step 209.

Step 208, to follow the tracks of the tracing positional that obtains based on the track algorithm of " Target Modeling, location " be the matched position of target to adopt, and gets into step 210;

Because

is greater than threshold value; Be to meet similar requirement; Therefore confirm in this step that adopting the Mean-Shift algorithm to follow the tracks of has obtained correct result; That is to say that following the tracks of the position that obtains with employing based on the track algorithm of " Target Modeling, location " is the matched position of target; This position is the optimal location of tracking, gets into step 210 then and carries out tracking results output.

Step 209, be the matched position of target, get into step 210 with the predicted position;

Because is less than threshold value; Be not meet similar requirement; Therefore the definite employing of this step Mean-Shift algorithm is followed the tracks of and is not obtained correct result; Possible reason is that target is blocked or blocked fully by large tracts of land, and the result who at this moment adopts the Mean-Shift algorithm to follow the tracks of is insecure.And under general situation, the motion of target is continuously, clocklike, do not have very strong maneuverability, so the predicted position that draws through Kalman filtering is relatively reliable.Therefore; Under

situation less than threshold value; The predicted position that definite directly employing Kalman filtering draws is as the target location; This position is the optimal location of tracking, gets into step 210 then and carries out tracking results output.

Step 210, according to the matched position output tracking result of target.

In this step, the matched position output tracking result of the target that obtains according to above-mentioned steps, thus effectively keep tracking to target.

Can find out from this embodiment; This embodiment has been integrated use several kinds of track algorithms; Utilized the advantage separately of these track algorithms; At first be utilize to adopt track algorithm target of prediction based on " filtering, data allocations " in the position of present frame, so just obtained the predicted position of target, then being employing advances the clarification of objective parametric statistics based on the track algorithm of motion detection; And a kind of decision mechanism proposed; Promptly behind the characteristic parameter that has counted each connected domain, the result who compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain judges whether to meet more new demand of target signature, meets target signature more after the new demand if judge; Just can utilize the characteristic parameter of said connected domain that the original characteristic parameter of said target is upgraded; Such renewal just can keep target is followed the tracks of more accurately and effectively, if judge that meet target signature upgrades request, can further utilize this moment to follow the tracks of based on the track algorithm of " Target Modeling, location " to obtain tracing area; And according to similarity confirm relatively that to adopt tracing area still be the predicted position that obtains before adopting as the matched position of target, also keep effective tracking more accurately thereby distinguish different situations to target.

The technical scheme of the embodiment of the invention can be used for the supervisory system to fixed scene, such as parking lot monitoring, bank monitoring, building monitoring or the like.Usually in the supervisory system, comprise IMAQ input equipment (like video camera), detect tracking processing equipment (computing machine or embedded device); If be the collaborative supervisory system of multiple-camera, control server in also comprising, the network equipments such as switch.The technical scheme of the embodiment of the invention specifically is can be used for can realizing that following the tracks of frame changes along with the size variation of target in the detection tracking processing equipment of supervisory system, and clarification of objective upgraded in time, realizes the target tracking of longer time.

In concrete the application; No matter be the supervisory system or the collaborative supervisory system of multiple-camera of single camera; For single detection tracking processing equipment; Processing procedure all is the same, below use embodiment of the invention technical scheme with single detection tracking processing equipment treatment scheme illustrate.

Seeing also Fig. 3, is the treatment scheme synoptic diagram of the single detection tracking processing of embodiment of the invention equipment.

As shown in Figure 3, detect tracking processing equipment and comprise image capture module M01, target selection and analysis module M02, target prodiction module M03, target detection and characteristics analysis module M04, target signature renewal decision-making module M05, target signature update module M06, tracking and target judging module M07, output module M08.Cooperation processing procedure between each module is described as follows:

Image capture module M01, being used for obtaining the output result that target is carried out obtaining behind the IMAQ from IMAQ input equipment (for example video capture device such as video camera or video frequency collection card) is video sequence S01.S01 is if the first frame video needs through target selection and analysis module M02, otherwise directly sends to target prodiction module M03.

Target selection and analysis module M02 can also can be accomplished by computing machine according to the initial setting condition by the monitoring staff according to the manually selected tracking target of actual conditions the selection of target automatically.After target selection is accomplished; Target selection and analysis module M02 also need analyze target signature; Obtain characteristic parameters such as target location, color of object eigenwert probability distribution, pixel count, length breadth ratio, dispersion degree, the S02 as a result of output comprises these characteristic parameters.

Target prodiction module M03 can adopt Kalman filtering algorithm, according to the possible position of target in the information prediction present frame of target location point in the past.At video sequence is under the situation of first frame, and target prodiction module M03 is according to the position of the positional information target of prediction of target among the S02 as a result of output; And be not under the situation of first frame at video sequence, according to the position at the place of the position feature target of prediction in the tracking results (S07 or S08) of former frame.Except the predicted position of target, also comprise the characteristic parameter information of the target signature among S02 as a result, S07 or the S08 of output among the S03 as a result of target prodiction module M03 output.

Target detection and characteristics analysis module M04; Carry out local foreground extraction in the field near the future position in the S03 as a result of output and carry out connected component labeling; And calculate the center position of each connected domain, add up the color feature value probability distribution in pixel count, length breadth ratio, dispersion degree and the connected domain zone of each connected domain.Comprise the above-mentioned characteristic parameter of each connected domain among the S04 as a result of target detection and characteristics analysis module M04 output, and comprised the characteristic parameter information among the S03 as a result that exports.

Target signature is upgraded decision-making module M05, at first according to the color feature value probability distribution of the tracking target among the S04 as a result of output and original image in the eigenwert probability distribution of the color of object of each connected domain, match the highest regional O of similarity _i, check O then _iWhether the character pair compared with parameters of characteristic parameter such as the pixel count, length breadth ratio, dispersion degree in zone and target is within a receivable scope.If satisfy the condition of setting, then be true, show O _iThe characteristic in zone meets the decision mechanism that target signature is upgraded decision-making module M05, assert O _iBe tracking target, export S05 as a result to target signature update module M06; Otherwise be false, show O _iThe characteristic in zone does not meet the decision mechanism that target signature is upgraded decision-making module M05, does not assert O _iBe tracking target, follow the tracks of and target judging module M07 output S06 to Mean-Shift.The characteristic parameter information that has all still comprised S04 among the S05 as a result of output and the S06.

Target signature update module M06 is with O among the S05 as a result of output _iThe characteristic parameter in zone upgrades the characteristic of tracking target; The characteristic parameter that upgrades comprises color of object eigenwert probability distribution, pixel count, length breadth ratio and dispersion degree; Export S07 as a result to output module M08 then, wherein comprised the position of target, each characteristic parameter after the renewal.

Follow the tracks of and target judging module M07, can adopt the Mean-Shift algorithm to carry out relevant treatment and obtain the target location.Tracking and target judging module M07 are the center with the position of Kalman filtering prediction, adopt the Mean-Shift algorithm to carry out iteration and follow the tracks of.After the convergence of Mean-Shift track algorithm, obtain target area O _m, calculate O _mSimilarity with target.If similarity Bhattacharrya coefficient

is greater than threshold value, expression Mean-Shift track algorithm has obtained correct result.If less than threshold value, expression Mean-Shift track algorithm has not obtained correct result, and at this moment the Mean-Shift tracking results is that the predicted position that insecure definite Kalman filtering draws is reliable relatively.Therefore; Under

situation less than threshold value; The predicted position that direct employing Kalman filtering draws is as the target location; To output module M08 output S08 as a result, target location that tracking and target judging module M07 obtain and the clarification of objective parameter among the S06 have wherein been comprised.

Output module M08 is according to the result of target signature update module M06, tracking and target judging module M07 input, outwards output tracking result.

In this application implementation example, carry out above-mentioned flow process, can effectively solve target is carried out the target signature variation issue in the tracing process through detecting tracking processing equipment.

The introduction that foregoing is detailed the tracking of the embodiment of the invention, corresponding, below detailed introduce detection tracking processing equipment and the supervisory system that the embodiment of the invention provides.

Seeing also Fig. 4, is that the embodiment of the invention detects tracking processing device structure synoptic diagram.

As shown in Figure 4, detect tracking processing equipment and comprise: target prodiction module 11, target detection and characteristics analysis module 12, target signature are upgraded decision-making module 13, target signature update module 14.

Target prodiction module 11 is used for the position that target of prediction moves, and obtains the predicted position of said target.

Target detection and characteristics analysis module 12 are used for carrying out foreground extraction according to the said predicted position that obtains, and definite connected domain, and statistics obtains the characteristic parameter of connected domain.

Target signature is upgraded decision-making module 13, is used for the result that characteristic parameter and the original characteristic parameter of said target according to said connected domain compare and judges whether to meet more new demand of target signature.

Target signature update module 14; Being used for upgrading decision-making module 13 in said target signature judges and meets target signature more after the new demand; The characteristic parameter of the said connected domain that is matched during through comparison upgrades the original characteristic parameter of said target, and the position of confirming the said connected domain that matches is as the matched position that target is followed the tracks of.

Detecting tracking processing equipment also comprises: follow the tracks of and target judging module 15.

Follow the tracks of and target judging module 15; Being used for upgrading decision-making module 13 in said target signature judges and does not meet target signature more after the new demand; With said predicted position is starting point; Adopt the track algorithm of based target modeling, location to obtain tracing area; Statistics obtains the characteristic parameter of tracing area, and according to the characteristic parameter of said tracing area and result that the original characteristic parameter of said target compares judge meet similar requirement after, definite with said tracing area as the matched position that target is followed the tracks of.

Said tracking and target judging module 15 comprise: follow the tracks of and statistic unit 151, judging unit 152, first processing unit 153, second processing unit 154.

Follow the tracks of and statistic unit 151; Being used for upgrading decision-making module 13 in said target signature judges and does not meet target signature more after the new demand; With said predicted position is starting point, adopts the track algorithm of based target modeling, location to obtain tracing area, and statistics obtains the characteristic parameter of tracing area.

Judging unit 152 is used for judging whether to meet similar requirement according to the characteristic parameter of said tracing area with the result that the original characteristic parameter of said target compares.

First processing unit 153, be used for said judging unit 152 judge meet similar requirement after, confirm with said tracing area as the matched position that target is followed the tracks of.

Second processing unit 154, be used for said judging unit 152 judge do not meet similar requirement after, confirm with said predicted position as the matched position that target is followed the tracks of.

The characteristic parameter of the top connected domain of mentioning comprises eigenwert probability distribution, pixel count, length breadth ratio, the dispersion degree of color of object.

Said target signature is upgraded decision-making module 13 and is comprised: matching unit 131, decision package 132.

Matching unit 131 is used for matching a similar area according to the determined coefficient of similarity value of eigenwert probability distribution of the color of object of the eigenwert probability distribution of the color of object of each connected domain and said target.

Decision package 132; Whether the pixel count, length breadth ratio, dispersion degree that is used to judge said similar area belongs to setting range with the ratio of the pixel count of said target, length breadth ratio, dispersion degree respectively; If not; Confirm not meet more new demand of target signature, if confirm to meet more new demand of target signature.

The characteristic parameter of the top tracing area of mentioning is the eigenwert probability distribution of color of object.

Judging unit 152 in said tracking and the target judging module 15 comprises: comparing unit 1521, result confirm unit 1522.

Comparing unit 1521, whether the determined coefficient of similarity value of eigenwert probability distribution of color of object of eigenwert probability distribution and said target of color of object that is used for the comparison tracing area is greater than setting threshold.

The result confirms unit 1522; Be used for when said comparing unit 1521 compares said coefficient of similarity value less than setting threshold; Confirm not meet similar requirement, when said comparing unit 1521 compares said coefficient of similarity value greater than setting threshold, confirm to meet similar requirement.

The embodiment of the invention also provides a kind of supervisory system; Comprise the IMAQ input equipment and detect tracking processing equipment; The IMAQ input equipment is used for to the image of said detection tracking processing equipment input to the target collection; Said detection tracking processing equipment carries out tracking processing according to the image of the target of IMAQ input equipment input, and the concrete structure of said detection tracking processing equipment is as shown in Figure 4, no longer is described in detail here.

In sum; The technical scheme that the embodiment of the invention provides; Owing to obtained the predicted position of target, be to utilize said predicted position when foreground extraction, and counted the characteristic parameter of each connected domain; The result who further compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain then judges whether to meet more new demand of target signature; Meet target signature more after the new demand judging so, just can utilize the characteristic parameter of said connected domain that the original characteristic parameter of said target is upgraded, the clarification of objective parameter information is upgraded in time; Thereby can obtain the latest features situation of target; So just can more effectively keep tracking, avoid when detecting target generation division, can following the tracks of a target as two targets in the prior art, when moving target blocks each other, can be used as two targets the problem appearance of a target target.

Further; Embodiment of the invention technical scheme judge do not meet target signature and upgrade request after; Can further utilize to follow the tracks of and obtain tracing area based on the track algorithm of " Target Modeling, location "; And according to similarity confirm relatively that to adopt tracing area still be the predicted position that obtains before adopting as the matched position of target, also keep effective tracking more accurately thereby distinguish different situations to target.

More than a kind of tracking, detection tracking processing equipment and supervisory system that the embodiment of the invention provided have been carried out detailed introduction; Used concrete example among this paper principle of the present invention and embodiment are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, the part that on embodiment and range of application, all can change, in sum, this description should not be construed as limitation of the present invention.

Claims (9)

1. a tracking is characterized in that, comprising:

The position of target of prediction motion obtains the predicted position of said target;

Carry out foreground extraction according to the said predicted position that obtains, and definite connected domain, statistics obtains the characteristic parameter of connected domain;

The result who compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain judges and meets target signature more after the new demand; The characteristic parameter of the said connected domain that is matched during through comparison upgrades the original characteristic parameter of said target, and the position of confirming the said connected domain that matches is as the matched position that target is followed the tracks of;

The result who compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain judges and does not meet target signature more after the new demand; With said predicted position is starting point; Adopt the track algorithm of based target modeling, location to obtain tracing area, statistics obtains the characteristic parameter of tracing area;

According to the characteristic parameter of said tracing area and result that the original characteristic parameter of said target compares judge meet similar requirement after, confirm with said tracing area as the matched position that target is followed the tracks of.

2. tracking according to claim 1 is characterized in that, said method also comprises:

Said characteristic parameter according to said tracing area and the result that the original characteristic parameter of said target compares confirm with said predicted position as the matched position that target is followed the tracks of after judging and not meeting similar requirement.

3. tracking according to claim 1 and 2 is characterized in that:

The characteristic parameter of said connected domain comprises eigenwert probability distribution, pixel count, length breadth ratio, the dispersion degree of color of object;

The said result who compares according to the characteristic parameter and the original characteristic parameter of said target of said connected domain judge meet target signature more the step of new demand comprise:

The determined coefficient of similarity value of eigenwert probability distribution according to the color of object of the eigenwert probability distribution of the color of object of each connected domain and said target matches a similar area;

Whether the pixel count, length breadth ratio, dispersion degree of judging said similar area belongs to setting range with the ratio of the pixel count of said target, length breadth ratio, dispersion degree respectively; If not; Confirm not meet more new demand of target signature, if confirm to meet more new demand of target signature.

4. tracking according to claim 1 and 2 is characterized in that:

The characteristic parameter of said tracing area is the eigenwert probability distribution of color of object;

Said characteristic parameter according to said tracing area is judged the step that meets similar requirement with the result that the original characteristic parameter of said target compares and is comprised:

Whether the determined coefficient of similarity value of eigenwert probability distribution of color of object of eigenwert probability distribution and said target of color of object of judging tracing area if not, confirms do not meet similar requirement, if confirm meet similar requirement greater than setting threshold.

5. one kind is detected tracking processing equipment, it is characterized in that, comprising:

The target prodiction module is used for the position that target of prediction moves, and obtains the predicted position of said target;

Target detection and characteristics analysis module are used for carrying out foreground extraction according to the said predicted position that obtains, and definite connected domain, and statistics obtains the characteristic parameter of connected domain;

Target signature is upgraded decision-making module, is used for the result that characteristic parameter and the original characteristic parameter of said target according to said connected domain compare and judges whether to meet more new demand of target signature;

The target signature update module; Being used for upgrading decision-making module in said target signature judges and meets target signature more after the new demand; The characteristic parameter of the said connected domain that is matched during through comparison upgrades the original characteristic parameter of said target, and the position of confirming the said connected domain that matches is as the matched position that target is followed the tracks of;

Follow the tracks of and the target judging module; Being used for upgrading decision-making module in said target signature judges and does not meet target signature more after the new demand; With said predicted position is starting point; Adopt the track algorithm of based target modeling, location to obtain tracing area; Statistics obtains the characteristic parameter of tracing area, and according to the characteristic parameter of said tracing area and result that the original characteristic parameter of said target compares judge meet similar requirement after, definite with said tracing area as the matched position that target is followed the tracks of.

6. detection tracking processing equipment according to claim 5 is characterized in that, said tracking and target judging module comprise:

Follow the tracks of and statistic unit; Being used for upgrading decision-making module in said target signature judges and does not meet target signature more after the new demand; With said predicted position is starting point, adopts the track algorithm of based target modeling, location to obtain tracing area, and statistics obtains the characteristic parameter of tracing area;

Judging unit is used for judging whether to meet similar requirement according to the characteristic parameter of said tracing area with the result that the original characteristic parameter of said target compares;

First processing unit is used for after said judgment unit judges goes out to meet similar requirement, confirms with said tracing area as the matched position that target is followed the tracks of;

Second processing unit is used for after said judgment unit judges goes out not meet similar requirement, confirms with said predicted position as the matched position that target is followed the tracks of.

7. according to each described detection tracking processing equipment of claim 5 to 6; It is characterized in that; The characteristic parameter of said connected domain comprises eigenwert probability distribution, pixel count, length breadth ratio, the dispersion degree of color of object, and said target signature is upgraded decision-making module and comprised:

Matching unit is used for matching a similar area according to the determined coefficient of similarity value of eigenwert probability distribution of the color of object of the eigenwert probability distribution of the color of object of each connected domain and said target;

Decision package; Whether the pixel count, length breadth ratio, dispersion degree that is used to judge said similar area belongs to setting range with the ratio of the pixel count of said target, length breadth ratio, dispersion degree respectively, if not, confirms not meet more new demand of target signature; If confirm to meet more new demand of target signature.

8. detection tracking processing equipment according to claim 6 is characterized in that, the characteristic parameter of said tracing area is the eigenwert probability distribution of color of object, and the judging unit in said tracking and the target judging module comprises:

Whether comparing unit, the determined coefficient of similarity value of eigenwert probability distribution of color of object of eigenwert probability distribution and said target of color of object that is used for the comparison tracing area greater than setting threshold,

The result confirms the unit, is used for when said comparing unit compares said coefficient of similarity value less than setting threshold, confirming not meet similar requirement, when said comparing unit compares said coefficient of similarity value greater than setting threshold, confirms to meet similar requirement.

9. a supervisory system is characterized in that, comprises the IMAQ input equipment and detects tracking processing equipment, and said IMAQ input equipment is used for to the image of said detection tracking processing equipment input to the target collection, and said detection tracking processing equipment comprises:

The target prodiction module is used for the image according to the target of said IMAQ input equipment input, and the position of target of prediction motion obtains the predicted position of said target;

Target detection and characteristics analysis module are used for carrying out foreground extraction according to the said predicted position that obtains, and definite connected domain, and statistics obtains the characteristic parameter of each connected domain;

Target signature is upgraded decision-making module, is used for the result that characteristic parameter and the original characteristic parameter of said target according to said connected domain compare and judges whether to meet more new demand of target signature;

The target signature update module; Being used for upgrading decision-making module in said target signature judges and meets target signature more after the new demand; The characteristic parameter of the said connected domain that is matched during through comparison upgrades the original characteristic parameter of said target, and the position of confirming the said connected domain that matches is as the matched position that target is followed the tracks of;

Follow the tracks of and the target judging module; Being used for upgrading decision-making module in said target signature judges and does not meet target signature more after the new demand; With said predicted position is starting point, adopts the track algorithm of based target modeling, location to obtain tracing area, and statistics obtains the characteristic parameter of tracing area; According to the characteristic parameter of said tracing area and result that the original characteristic parameter of said target compares judge meet similar requirement after, confirm with said tracing area as the matched position that target is followed the tracks of.

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