CN104835178B - A kind of tracking of low signal-to-noise ratio moving small target is with knowing method for distinguishing - Google Patents

Abstract

The invention discloses a kind of tracking of low signal-to-noise ratio moving small target and knowledge method for distinguishing, including：The extraction mesh calibration method from the single-frame images of video sequence is provided, weakens or eliminate the influence of background and noise；Provide Weak target extraction of motion information and status predication modeling；Set up the incidence matrix of two inter frame image moving small targets；The information fusion being superimposed based on multiple image, track algorithm and recognition methods of the large-scale image with video image motion Small object under network environment are proposed using fuzzy pushdown automata chain bullet stack recursive operation.The present invention contributes to target identification to understand the detection target characteristics of motion, active degree and its influence to other targets with image procossing personnel, so as to provide corresponding decision-making, seek to suppress or eliminate influence of the undesirable element to itself or other important goal to be all very important, important reference and reference role are played in the development to military, civil, public security system, all target recognition and trackings based on video system of road traffic.

Description

A kind of tracking of low signal-to-noise ratio moving small target is with knowing method for distinguishing

Technical field

The invention belongs to pattern recognition and classification technical field, more particularly to a kind of tracking of low signal-to-noise ratio moving small target With knowing method for distinguishing.

Background technology

With science and technology development and the mankind security protection consciousness lifting, video monitoring system is in every field under network environment Increasingly it is widely applied, such as military affairs, traffic, bank, factory, community.And the moving target based on video monitoring Identification is a particularly useful job, can be applied in space flight, military affairs, the tracking of guided missile track identification, break in traffic rules and regulations detection etc. Various fields.But in some occasions such as countries in the world in the environmental surveillance of surrounding area, it is desirable to be able to intercept and capture and lock as soon as possible Track target.So to the accurate detection and tracking of moving small target, to seeming more in the application of the every field such as military, civil Come more important, it is also more and more urgent.The test problems of low signal-to-noise ratio Moving Small Targeties under the conditions of the strong clutter background of network environment The operating distance and detection performance of detection system are directly determined, its solution has very important for raising detection system performance Practical significance.

The multi-Dimensional parameters to the denoising of video image under complex background, moving target are extracted, at video image at present The processing of the problems such as tracking and identification of reason and moving target lacks solution, and these problems turn into image processing field One hot issue, this is also that present many departments are intractable and urgent problem to be solved.

Because different aerial video acquisition systems, such as illumination of different physical phenomenons are unable to substantially uniformity distribution etc. in many ways The reason for face, make the image border intensity of acquisition different.Moreover, in practical matter, view data toward contact by noise dirt Dye.Scenery characteristic mixes and subsequent explanation can be made to become extremely difficult simultaneously.Realize and aerial image picture is intended to It is accurate understand, it is necessary to the noncontinuity of image object intensity can be detected by studying, the accurate of them can be determined simultaneously again The target identification method of position.

Moving small target signal detection is that various advanced seeker systems need the pass solved at present with extracting under low signal-to-noise ratio One of key technology problem, now the image of target only occupy the area of one or several pixels, and because background environment is complicated, The inhomogeneities of atmospheric radiation, the internal noise of detector etc. factor are influenceed, and target is almost submerged in clutter varying background, Without shape and structural information, target is possibly even lost sometimes, and this just brings very big difficulty to Dim targets detection. The test problems of low signal-to-noise ratio Moving Small Targeties directly determine operating distance and the inspection of detection system under the conditions of strong clutter background Performance is surveyed, it solves have very important practical significance for improving detection system performance.Also will be to military, civil, public security The development of all target recognition and trackings based on video system such as system, road traffic plays important reference and with reference to making With.

The content of the invention

The purpose of the embodiment of the present invention is to provide a kind of tracking of low signal-to-noise ratio moving small target with knowing method for distinguishing, purport There is the problem of tracking real-time speed is compared with slow, tracking or poor recognition effect solving prior art.

The embodiment of the present invention is achieved in that a kind of tracking of low signal-to-noise ratio moving small target with knowing method for distinguishing, its It is characterised by, the tracking of the low signal-to-noise ratio moving small target stage by stage, is according to target provided not respectively with knowing method for distinguishing with one kind Same implementation, is easily blocked or is flooded by other objects or noise in complex background to moving small target in video image Situation, it is proposed that the elimination of opening and closing conversion or the algorithm for weakening background and noise；To the small and weak characteristic of Small object, it is proposed that The adaptive neural network competitive model of line study, the multidimensional characteristic parameter of Weak target is extracted using the active unit of competition； For the kinetic characteristic of video Small Target, using the mutation of gray scale, Small object motion state model and forecast model are given； Detection real-time to moving small target and tracking, employ fuzzy pushdown automata chain and carry out track identification and tracking, under fuzzy Push away automatic chain depth and carry out track judgement for threshold value；

Specifically include following steps：

Step one, the extraction mesh calibration method from the single-frame images of video sequence is provided, weakens or eliminates background and noise Influence；

Step 2, provides Weak target extraction of motion information and status predication modeling；

Step 3, sets up the incidence matrix of two inter frame image moving small targets；

Step 4, the information fusion being superimposed based on multiple image, is carried using fuzzy pushdown automata chain bullet stack recursive operation Track algorithm and recognition methods of the large-scale image with video image motion Small object under network environment are gone out.

Further, in step one, the extraction mesh calibration method from the single-frame images of video sequence is provided, weakens or eliminates During the influence of background and noise, concrete methods of realizing is：

Local maximum and minimum are asked for using mathematical morphology combinatorial operation, mitigates the amount of calculation of subsequent treatment, to the greatest extent Amount reduces false alarm rate and counted, and region growing, minimum point are carried out to each Local modulus maxima and implements to weaken or eliminates, to possible Target selected；

G=f-f ο B or closed operation conversion g=fB-f are converted using opening operation, single-frame images is filtered, detected The place changed greatly in image, that is, high fdrequency component, while it is comparatively gentle to filter off gray-value variation in image Place, equivalent to low frequency component, just a width single-frame images is filtered using this conversion, low-frequency component is filtered, quite In filtering extended background, the HFS including leaving comprising Small object；In formula, f is gradation of image frame, and B is structure, ο Opening operation is represented, closed operation is represented；

To accurately identify target or track, carry out the suppression of Small object enhancing and interference, due to Small object point each frame it Between move, multiframe superposition can be carried out to video image, in the last frame of superposition, Small object point shows as correlation very strong rail Mark point, but noise, it is possible to flooding Small object track, proposition multi-frame difference superposition algorithm, selection exists comprising moving small target point Interior image sequence, the superposition value of odd-numbered frame and each n frames difference of even frame, i.e.,：

In formula, f_iFor the i-th frame in image sequence, f_zFor last superposition frame；

Through the stack plus frame takes thresholding to handle, method is as follows:

δ is threshold value, is takenM, N are the size of superposition two field picture.

Further, in step 2, when providing Weak target extraction of motion information and status predication modeling, the side of implementing Method is：

The adaptive neural network competitive model of on-line study is constructed first, extracts weak using the active unit of its competition The multidimensional characteristic parameter of Small object：

The first step, initializes network：The dimension of fixed output nerve network grid is N × M, and input layer is quadravalence network, And random initializtion input neuron and the weight of output neuron connection, make t represent algorithm iteration number of times, put t=0；

Second step, selects victor：The gray scale of each frame Small object image, colourity, motion parameter X={ x₁,x₂,…, x_dThe input neuron in network is input to, to each input neuron value x_j, the output of the node i in competition layerForG is an activation primitive, is such as takenα ＞ 0 are constant, the slope of controlling curve；μ_i(t) It is p dimensional input vectors x_jWith p dimensional weights vector ω_ji(t) Euclidean distance between | | x_j-ω_ji(t) | | and, i.e.,ω_ji(t) it is from input layer node j to competition layer node i connection weight vector, j in t ∈ J, J={ 1 ..., d }, i ∈ I, I={ 1 ..., N_iBe competition layer certain regional area；

Select the output neuron i won^*, in competition layer, correspondenceMinimum node will win, if that is,The node so won in competition layer is i^*, then with i^*The weight of association and and i^*The power of the neighbouring point association of point Weight can be all adjusted；

3rd step, updates weight：N(i^*) it is triumph output neuron^i*Neighbour, the distance between output neuron is specific Specify, to each output neuron i ∈ { N (i^*),i^*, adjust renewal according to the following formula：

η (t)=η has determined that in advance；This rule only updates the neighbour of triumph output neuron；

4th step, standardized weight：Update to standardization after weight, so as to be consistent with input measurement standard；

5th step, is continued cycling through：The first step is repeated to the 4th step, the number of times of iteration is set to t=t+1, stopped until meeting Machine criterion, shutting down criterion is | | x_j-ω_ji(t) | | ＜ ε, take ε=0.5, or untill having exceeded the cycle-index of maximum.

Further, in step 3, the implementation method for setting up the incidence matrix of two inter frame image moving small targets is：

M × n object matching matrix M are set up, here, m is the number of the moving small target of present frame, and n is the fortune of previous frame The number of dynamic Small object, the value of element M (i, j) is given by：

R in formula_iFor the radius of the i-th target；r_jFor the radius of jth target；(x_i,y_i) be the i-th target center-of-mass coordinate； (x_j,y_j) be jth target center-of-mass coordinate；∞ represents a very big numerical value；

First, selective value is minimum in matrix M and is not ∞ element, and the row and column corresponding to the element is current respectively The numbering of moving small target and previous frame moving small target, the corresponding moving small target of row moving small target phase corresponding with row Match somebody with somebody, all elements value for the row and column for completing matching is then changed into ∞；Minimum value is found in continuation in matrix M, completes motion The matching of Small object, until all values in matrix are all changed into ∞；After search terminates, the row for not finding matching target is represented There is the appearance of new moving small target in present image, the row for not finding matching target represent certain small mesh of motion in present image Mark disappears.

Further, in step 4, the information fusion being superimposed based on multiple image utilizes fuzzy pushdown automata chain bullet stack The track algorithm that recursive operation proposes large-scale image and video image motion Small object under network environment is with recognition methods：

The first step, each fuzzy pushdown automata recognizes fusion in time to moving small target：

f_ipAnd Ξ (t)_i(t) represent that t is belonged to by obscuring the identified moving small target that pushdown automata i is measured respectively The fuzzy membership and Fuzzy Distribution of pth class,Represent untill the l moment to be merged by i-th of fuzzy pushdown automata accumulation To identified target belong to the fuzzy membership of pth class,Represent untill the l moment by i-th of fuzzy pushdown automata product The tired Fuzzy Distribution for merging obtained identified target, here, l=1,2 ..., t, i.e.,

With

o_p(p ∈ U) is moving small target, by the accumulation fusion Fuzzy Distribution and the measurement Fuzzy Distribution of t at t-1 moment Merged, obtain target identification accumulation fusion Fuzzy Distribution of i-th of fuzzy pushdown automata untill tFor：

Wherein,S₂It is Fuzzy Integration Function, usual S₂Remove formula：

Now, with Fuzzy DistributionThe motion state of corresponding Small object isIt is the small mesh of current t Target state estimation：

F_i(t) it is the motion state transfer matrix from last moment to current time, selects metastasis model,For upper a period of time The state estimation at quarter,For the state estimation at current time；

Second step, the Space integration that fuzzy pushdown automata is recognized to Small object：

Obtaining the accumulation Fuzzy Distribution of each fuzzy pushdown automata target identification of tAfterwards, i=1 ... here, N, is merged using Fuzzy Integration Function to this N number of Fuzzy Distribution, just obtained untill t to target identification when- Fuzzy Distribution is merged in sky accumulation：

It is theoretical using Fuzzy Integration Function, it can obtain

S_NAlso illustrate that Fuzzy Integration Function；If

Now, with Fuzzy Distribution Ξ^tThe motion state of corresponding Small object isIt is all small of current kth frame The state estimation of target, motion state fusion results are： It is by the motion of the 1st frame to kth frame The state estimation of i-th of Small object of the current kth frame of information prediction,It is the motion of all Small objects of current kth frame State estimation,It is the fuzzy membership of model；The key of tracking be by measurement be carved into from the outset the k-1 moment predict ought The fuzzy membership of preceding i-th of tracking system model of k momentHereIt is the fuzzy membership of k-1 moment tracking system models,Be it is known, π_ji=Pr { m_k=m⁽ⁱ⁾|m_k-1=m^(j)It is from model m_k-1To model m_kState transfer fuzzy membership.

Further, after multiframe is merged, Small object point is further enhanced, and most of noise spot is cut, and is filtered out Random noise disturbance is made decisions by fuzzy pushdown automata chain length, in order to subtract as far as possible while Small object point is retained Pushdown automata chain length is obscured less, thresholding processing is taken to fusion frame, and method is as follows:

Threshold value T is the length of fuzzy pushdown automata chain, but this chain must assure that containing Small object point, f_AFor total fusion Frame, on last fusion frame, Small object point shows as a correlation very strong track；

Each component f of the characteristic vector of moving small target to be identified or track is calculated according to blending algorithm_AFuzzy membership Spend μ_ij, that is, the characteristic vector for obtaining Unknown Motion Small object or track is U_i=[μ_i1,μ_i2,…,μ_ik]^T；It with trained Good known i-th₀The multi-Dimensional parameters characteristic vector U of classification_i0Compare, and if only ifWhen, adjudicate fortune to be identified Dynamic Small object or track belong to i-th₀Class；EvenSo thatThen judge the small mesh of motion to be identified Mark or track belong to i-th₀Class；Here, δ is threshold value, and B is the index set of target or track class.

The tracking of the low signal-to-noise ratio moving small target that the present invention is provided is with knowing method for distinguishing, it is proposed that one kind based on single frames with The small target tracking algorithm of the time-space domain fused filtering of multiframe and recognition methods, are easily answered moving small target in video image The situation that other objects or noise in miscellaneous background are blocked or flooded, it is proposed that the elimination of opening and closing conversion weakens background and noise Algorithm；To the small and weak characteristic of Small object, it is proposed that the adaptive neural network competitive model of on-line study, its competition is utilized Active unit extracts the multidimensional characteristic parameter of Weak target；For the kinetic characteristic of video Small Target, using the mutation of gray scale, Give Small object motion state model and forecast model；Detection real-time to moving small target and tracking, employ and are pushed away under obscuring Automatic chain carries out track identification and tracking, track judgement is carried out using fuzzy pushdown automata chain depth as threshold value, so as to propose A kind of moving small target track algorithm and recognition methods based under complex environment, the present invention contribute to target identification and image Treatment people understands the characteristics of motion, active degree and its influence to other targets of detection target, so as to provide corresponding determine Plan, seeks to suppress or eliminates influence of the undesirable element to itself or other important goal to be all very important；Will be to the military, people The development of all target recognition and trackings based on video system such as thing, public security system, road traffic play important reference and Reference role.The average correct recognition rata highest of the feature extraction method of identification of the present invention, in experiment, with increasing for sample number, Average correct recognition rata constantly increases, and reaches that increasing sample curve during certain sample size again gradually tends to be steady.

Brief description of the drawings

Fig. 1 is the realization stream of tracking and the recognition methods of the moving small target under low signal-to-noise ratio provided in an embodiment of the present invention Cheng Tu；

Fig. 2 is schematic diagram of the opening and closing conversion provided in an embodiment of the present invention to the filter result of moving small target image；

In figure：(a) the 5th frame of image sequence；(b) the 5th frame opening and closing becomes scaling method filter effect；

Fig. 3 is set up tracking system state fusion provided in an embodiment of the present invention to moving small target in image or video Tracking；

Fig. 4 is pursuit path image of the fuzzy pushdown automata chain provided in an embodiment of the present invention to moving small target；

Fig. 5 is proposition provided in an embodiment of the present invention with existing method of identification correct recognition rata comparison schematic diagram.

Embodiment

In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to Limit the present invention.

Below in conjunction with the accompanying drawings and specific embodiment to the present invention application principle be further described.

As shown in figure 1, the tracking of the low signal-to-noise ratio moving small target of the embodiment of the present invention is with knowing method for distinguishing including following Step：

Step S101, provides the extraction mesh calibration method from the single-frame images of video sequence, weakens or eliminates background with making an uproar The influence of sound；

Step S102, provides Weak target extraction of motion information and status predication modeling；

Step S103, sets up the incidence matrix of two inter frame image moving small targets；

Step S104, the information fusion being superimposed based on multiple image, utilizes fuzzy pushdown automata chain bullet stack recursive operation Propose track algorithm and recognition methods of the large-scale image with video image motion Small object under network environment.

In embodiments of the present invention, in step S101, the side that target is extracted from the single-frame images of video sequence is provided Method, during the influence of decrease or elimination background and noise, concrete methods of realizing is：

On the basis of based on mathematical morphology, it is proposed that the image object detection algorithm of opening and closing conversion.The master of the algorithm It is to ask for local maximum and minimum using mathematical morphology combinatorial operation to want thinking, mitigates the amount of calculation of subsequent treatment, to the greatest extent Amount reduces false alarm rate points.Carry out region growing, minimum point to each Local modulus maxima to implement to weaken or eliminate, to possible Target selected.

Make to reach that it shows as the zero of large area by the filtered image of opening and closing conversion for each frame using this algorithm High fdrequency component including background and comprising pinpoint target, and random spotted noise.Then moved according between consecutive frame The correlation of target, carries out difference multiframe superposition, and Small object is due to its motility, and it is very strong on superposition frame to show as correlation Tracing point, and high frequency spotted noise, then because its randomness is cancelled out each other a part, unmatched part is showed in geometric area For the random distribution noise spot that correlation is very poor.

For example, using a kind of simple opening operation conversion g=f-f ο B, being filtered to single-frame images, figure can be detected The place changed greatly as in, that is, high fdrequency component, while it is comparatively gentle to filter off gray-value variation in image Place, equivalent to low frequency component.A width single-frame images can be just filtered using this conversion, filter low-frequency component, phase When in filtering extended background, the HFS including leaving comprising Small object.In formula, f is gradation of image frame, and B is structure, ο represents opening operation.Using opening and closing conversion, the wherein frame result to the video image of moving small target is as shown in Figure 2.

In embodiments of the present invention, in step s 102, Weak target extraction of motion information and status predication modeling are provided When, concrete methods of realizing is：

Target is detected and tracked, a reference template is initially set up as standard form.

Given known moving target, i.e., in the two field picture containing this target under the conditions of certain video, detect Its movement velocity, current location, direction of motion o, height h, gray average μ and the variances sigma of imaging.It assign the image of this frame as ginseng Examine template.

Set up reference template as follows：Using target location central point as the center of circle, to clap frame time and target in imaging plane Projection speed product for radius preceding half-circle area in, scan for.Using the change frequency of gray scale as thresholding, threshold is set Value.Search the change frequency of gray scale respectively along image width and image height direction, if the change frequency of both direction is both less than or equal to 2, Frame time is clapped in extension, until the grey scale change number of times at least one direction is more than 3 or 3 times.Then, using the center of circle as starting point, Calculate the distance between continuous three adjacent grey scale changes d₁And d₂, can obtain the motion amplitude of target and target a frame into Image width degree, Target Motion Character parameter s during using this amplitude and width as current location₁And s₂.It can be positioned by the saltus step of gray scale The position of targetAnd the direction of motion, speed and height of imaging etc. are calculated with it.The gray average of target in half-circle area It is another two characteristic parameter with variance, obtains a principal eigenvector V used in successive image processing special as standard Levy vector.

By the movable information of Small object, will provide motion state forecast model is

Here, F_i(k) it is the motion state transfer matrix from previous frame to present frame, suitable turn must be selected it Shifting formwork type,For the state estimation of previous frame,For the state estimation of present frame.

In embodiments of the present invention, in step s 103, the reality of the incidence matrix of two inter frame image moving small targets is set up Now method is：

Set up m × n object matching matrixes M.Here, m is the number of the moving small target of present frame, and n is the fortune of previous frame The number of dynamic Small object.The value of element M (i, j) is given by.

R in formula_iFor the radius of the i-th target；r_jFor the radius of jth target；(x_i,y_i) be the i-th target center-of-mass coordinate； (x_j,y_j) be jth target center-of-mass coordinate；∞ represents a very big numerical value.

Of the moving small target and moving small target in previous frame image in present image is realized using matrix is matched Match somebody with somebody.First, selective value is minimum in matrix M and is not ∞ element, and the row and column corresponding to the element is current kinetic respectively The numbering of Small object and previous frame moving small target, the corresponding moving small target of such row moving small target phase corresponding with row Match somebody with somebody.Then all elements value for the row and column for completing matching is changed into ∞.Minimum value is found in continuation in matrix M, completes motion The matching of Small object, until all values in matrix are all changed into ∞.After search terminates, the row for not finding matching target is represented There is the appearance of new moving small target in present image, the row for not finding matching target represent certain small mesh of motion in present image Mark disappears.

In embodiments of the present invention, in step S104, the information fusion being superimposed based on multiple image is pushed away using under fuzzy Automatic chain bullet stack recursive operation proposes large-scale image and the track algorithm of video image motion Small object under network environment It is with recognition methods：

Based on multiple features fusion, it is proposed that feature and the connection of motion state based on present frame and previous frame moving small target Close probability and coarseness data association algorithm, hypothesis testing method.Estimated using joint probability and coarseness data association algorithm Count multimode small target tracking algorithm.The information updating in rear frame is utilized to assume state using multihypothesis test method.Proposing While these algorithms and method, provide suitable " thresholding ", only calculate the pass of the observation being located in " thresholding " and Small object Join probability and roughness, this greatly reduces amount of calculation, solve Small object with the distance of predicted position with observation position and conflict Problem.

The moving small target tracking and identification of image are realized, a kind of suitable recognizer must be just proposed, you can recognize Moving small target characteristic vector for known class that is to be identified and having trained all is fuzzy number vector, by its feature to Amount compares, defined feature membership function, degree of membership and matched rule, provides fuzzy object recognizer.It is embodied as：

To implement to track to the track of moving small target in image or video, the present invention proposes the motion shape of multiframe superposition State blending algorithm：According to the motion state of moving small target in image or video, a kind of detect and tracking system will be designed Blending algorithm, to realize the tracking to moving small target.This algorithm is made up of three parts：(a) mapping tracking system, which enters, has N In the neutral net of individual feedback neural member；(b) N number of feedback neural member completion tracking system i.e. in multiframe fusion in time is allowed State fusion；(c) M non-feedback output neurons are made to complete spatially the state of the tracking system in i.e. single frames fusion again Fusion, finally calculates the Space Time accumulation fusion of tracking system state, come complete tracking system to the fusion of moving small target with Track algorithm.

Designing tracking system state fusion algorithm is：Here,It is by the 1st frame to kth frame The state estimation of i-th of Small object of the current kth frame of motion information prediction,It is all Small objects of current kth frame State estimation,It is the fuzzy membership of model.The key of tracking is that to be carved into the k-1 moment from the outset pre- by measurement Survey the fuzzy membership of current i-th of tracking system model of k momentHereIt is the fuzzy membership of k-1 moment tracking system models,Be it is known, π_ji=Pr { m_k=m⁽ⁱ⁾|m_k-1=m^(j)It is from model m_k-1To model m_kState transfer fuzzy membership.The tracking system of foundation State fusion of uniting is as shown in Figure 3 to the tracking process of moving small target.

On last superposition frame, Small object point shows as a correlation very strong track.But identify Small object point The still non-easy thing in track, mainly some discrete noise spots it is possible in the presence of.In addition it is also possible that tracing point and noise spot It is interlaced.Therefore it can only proceed from the situation as a whole to be made decisions according to overall relevancy under certain hypothesis.Therefore, proposing to be based on Fuzzy pushdown automata chain, track judgement is carried out by thresholding of the correlation length of track using the method for playing stack Yu popping.Tracking As a result it is as shown in Figure 4.From fig. 4, it can be seen that explanation fuzzy pushdown automata D-chain trace algorithm proposed by the present invention is known in pattern Larger success is achieved in other system.

The fuzzy membership of each component of the characteristic vector of moving small target to be identified or track is calculated according to blending algorithm μ_ij, you can the characteristic vector for obtaining Unknown Motion Small object or track is U_i=[μ_i1,μ_i2,…,μ_ik]^T.It with trained Good known i-th₀The multi-Dimensional parameters characteristic vector U of classification_i0Compare, and if only ifWhen, adjudicate fortune to be identified Dynamic Small object or track belong to i-th₀Class.EvenSo thatThen judge the small mesh of motion to be identified Mark or track belong to i-th₀Class.Here, δ is threshold value, and B is the index set of target or track class.

The present invention's concretely comprises the following steps：

Step one, the extraction mesh calibration method from the single-frame images of video sequence is provided, weakens or eliminates background and noise Influence；

Step 2, provides Weak target extraction of motion information and status predication modeling；

Step 3, sets up the incidence matrix of two inter frame image moving small targets；

Step 4, the information fusion being superimposed based on multiple image, is carried using fuzzy pushdown automata chain bullet stack recursive operation Track algorithm and recognition methods of the large-scale image with video image motion Small object under network environment are gone out.

Further, in step one, the extraction mesh calibration method from the single-frame images of video sequence is provided, weakens or eliminates During the influence of background and noise, concrete methods of realizing is：

Because complexity, the degree of stability of scene affect the effect of target following.For example, target image by illumination not Uniformly, influence of the variation of object etc. in background.Will from image effective Ground Split, extract target, it is necessary to propose that one kind subtracts Algorithm that is weak or eliminating background or influence of noise.The present invention will propose opening and closing conversion on the basis of based on mathematical morphology Image object detection algorithm.The main thought of the algorithm be using mathematical morphology combinatorial operation ask for local maximum with it is minimum Value, mitigates the amount of calculation of subsequent treatment, and false alarm rate points are reduced as far as possible.Region growing, pole are carried out to each Local modulus maxima Small value point is implemented to weaken or eliminated, and possible target is selected.

Make to reach that it shows as the zero of large area by the filtered image of opening and closing conversion for each frame using this algorithm High fdrequency component including background and comprising pinpoint target, and random spotted noise.Then moved according between consecutive frame The correlation of target, carries out difference multiframe superposition, and Small object is due to its motility, and it is very strong on superposition frame to show as correlation Tracing point, and high frequency spotted noise, then because its randomness is cancelled out each other a part, unmatched part is showed in geometric area For the random distribution noise spot that correlation is very poor.

For example, using a kind of simple opening operation conversion g=f-f ο B, being filtered to single-frame images, figure can be detected The place changed greatly as in, that is, high fdrequency component, while it is comparatively gentle to filter off gray-value variation in image Place, equivalent to low frequency component.A width single-frame images can be just filtered using this conversion, filter low-frequency component, phase When in filtering extended background, the HFS including leaving comprising Small object.In formula, f is gradation of image frame, and B is structure, ο represents opening operation.Using opening and closing conversion, the wherein frame result to the video image of moving small target is as shown in Figure 2.

Because opening and closing conversion filtering is relevant with structure size, the size of structure size decides high-pass filtering effect. Structure size is smaller, filters out that low frequency background is more thorough, and the target size that can retain is just smaller.

Further, in step 2, Weak target extraction of motion information and status predication modeling, concrete methods of realizing are implemented For：

Target is detected and tracked, a reference template is initially set up as standard form.

Given known moving target, i.e., in the two field picture containing this target under the conditions of certain video, detect Its movement velocity, current location, direction of motion o, height h, gray average μ and the variances sigma of imaging.It assign the image of this frame as ginseng Examine template.

Set up reference template as follows：Using target location central point as the center of circle, to clap frame time and target in imaging plane Projection speed product for radius preceding half-circle area in, scan for.Using the change frequency of gray scale as thresholding, threshold is set Value.Search the change frequency of gray scale respectively along image width and image height direction, if the change frequency of both direction is both less than or equal to 2, Frame time is clapped in extension, until the grey scale change number of times at least one direction is more than 3 or 3 times.Then, using the center of circle as starting point, Calculate the distance between continuous three adjacent grey scale changes d₁And d₂, can obtain the motion amplitude of target and target a frame into Image width degree, Target Motion Character parameter s during using this amplitude and width as current location₁And s₂.It can be positioned by the saltus step of gray scale The position of targetAnd the direction of motion, speed and height of imaging etc. are calculated with it.The gray average of target in half-circle area It is another two characteristic parameter with variance, obtains a principal eigenvector V used in successive image processing special as standard Levy vector.

By the movable information of Small object, will provide motion state forecast model is

Here, F_i(k) it is the motion state transfer matrix from previous frame to present frame, suitable turn must be selected it Shifting formwork type,For the state estimation of previous frame,For the state estimation of present frame.

Further, in step 3, the implementation method for setting up the incidence matrix of two inter frame image moving small targets is：

Set up m × n object matching matrixes M.Here, m is the number of the moving small target of present frame, and n is the fortune of previous frame The number of dynamic Small object.The value of element M (i, j) is given by.

R in formula_iFor the radius of the i-th target；r_jFor the radius of jth target；(x_i,y_i) be the i-th target center-of-mass coordinate； (x_j,y_j) be jth target center-of-mass coordinate；∞ represents a very big numerical value.

Of the moving small target and moving small target in previous frame image in present image is realized using matrix is matched Match somebody with somebody.First, selective value is minimum in matrix M and is not ∞ element, and the row and column corresponding to the element is current kinetic respectively The numbering of Small object and previous frame moving small target, the corresponding moving small target of such row moving small target phase corresponding with row Match somebody with somebody.Then all elements value for the row and column for completing matching is changed into ∞.Minimum value is found in continuation in matrix M, completes motion The matching of Small object, until all values in matrix are all changed into ∞.After search terminates, the row for not finding matching target is represented There is the appearance of new moving small target in present image, the row for not finding matching target represent certain small mesh of motion in present image Mark disappears.

Further, in step 4, the information fusion being superimposed based on multiple image utilizes fuzzy pushdown automata chain bullet stack The track algorithm that recursive operation proposes large-scale image and video image motion Small object under network environment is with recognition methods：

Based on multiple features fusion, the present invention proposes description moving small target and calculated with tracking the data correlation of its movement locus Method.Feature and the joint probability and coarseness data of motion state based on present frame Yu previous frame moving small target are proposed simultaneously Association algorithm, hypothesis testing method.Utilize joint probability and coarseness data association algorithm pre-estimation multimode small target tracking Algorithm.The information updating in rear frame is utilized to assume state using multihypothesis test method.Proposing the same of these algorithms and method When, provide suitable " thresholding ", only calculate the association probability and roughness of the observation being located in " thresholding " and Small object, this is just Amount of calculation is greatly reduced, Small object collision problem is solved with the distance of observation position and predicted position.

The moving small target tracking and identification of image are realized, a kind of suitable recognizer must be just proposed, you can recognize Moving small target characteristic vector for known class that is to be identified and having trained all is fuzzy number vector, by its feature to Amount compares, defined feature membership function, degree of membership and matched rule, provides fuzzy object recognizer.It is embodied as：

To implement to track to the track of moving small target in image or video, the present invention proposes the motion shape of multiframe superposition State blending algorithm：According to the motion state of moving small target in image or video, a kind of detect and tracking system will be designed Blending algorithm, to realize the tracking to moving small target.This algorithm is made up of three parts：(a) mapping tracking system, which enters, has N In the neutral net of individual feedback neural member；(b) N number of feedback neural member completion tracking system i.e. in multiframe fusion in time is allowed State fusion；(c) M non-feedback output neurons are made to complete spatially the state of the tracking system in i.e. single frames fusion again Fusion, finally calculates the Space Time accumulation fusion of tracking system state, come complete tracking system to the fusion of moving small target with Track algorithm.

Designing tracking system state fusion algorithm is：Here,It is by the 1st frame to kth frame The current kth frame of motion information prediction i-th of Small object state estimation,It is all Small objects of current kth frame State estimation,It is the fuzzy membership of model.The key of tracking is to be carved into the k-1 moment from the outset by measurement Predict the fuzzy membership of current i-th of tracking system model of k momentHereIt is the fuzzy membership of k-1 moment tracking system models,Be it is known, π_ji=Pr { m_k=m⁽ⁱ⁾|m_k-1=m^(j)It is from model m_k-1To model m_kState transfer fuzzy membership.The tracking system of foundation State fusion of uniting is as shown in Figure 3 to the tracking process of moving small target.

On last superposition frame, Small object point shows as a correlation very strong track.But identify Small object point The still non-easy thing in track, mainly some discrete noise spots it is possible in the presence of.In addition it is also possible that tracing point and noise spot It is interlaced.Therefore it can only proceed from the situation as a whole to be made decisions according to overall relevancy under certain hypothesis.Therefore, proposing to be based on Fuzzy pushdown automata chain, track judgement is carried out by thresholding of the correlation length of track using the method for playing stack Yu popping.Tracking As a result it is as shown in Figure 4.From fig. 4, it can be seen that explanation fuzzy pushdown automata D-chain trace algorithm proposed by the present invention is known in pattern Larger success is achieved in other system.

The fuzzy membership of each component of the characteristic vector of moving small target to be identified or track is calculated according to blending algorithm μ_ij, you can the characteristic vector for obtaining Unknown Motion Small object or track is U_i=[μ_i1,μ_i2,…,μ_ik]^T.It with trained Good known i-th₀The multi-Dimensional parameters characteristic vector U of classification_i0Compare, and if only ifWhen, adjudicate fortune to be identified Dynamic Small object or track belong to i-th₀Class.EvenSo thatThen judge the small mesh of motion to be identified Mark or track belong to i-th₀Class.Here, δ is threshold value, and B is the index set of target or track class.

The tracking of moving small target under the low signal-to-noise ratio that the present invention is provided and identification problem, existing document also has similar Research, but all there is the weakness such as tracking real-time speed is poor compared with slow, tracking or recognition effect in these literature methods.However, The present invention with it is a kind of stage by stage, according to target provide different implementations respectively, it is proposed that it is a kind of based on single frames and multiframe when The small target tracking algorithm of spatial domain fused filtering and recognition methods.To moving small target in video image easily by complex background Other objects or the noise situation blocking or flood, it is proposed that the elimination of opening and closing conversion or the algorithm for weakening background and noise； It is single using the activity of its competition to the small and weak characteristic of Small object, it is proposed that the adaptive neural network competitive model of on-line study Member extracts the multidimensional characteristic parameter of Weak target；For the kinetic characteristic of video Small Target, using the mutation of gray scale, give Small object motion state model and forecast model；Detection real-time to moving small target and tracking, employ fuzzy pushdown automata Chain carries out track identification and tracking, track judgement is carried out using fuzzy pushdown automata chain depth as threshold value, so as to propose one kind Based on the moving small target track algorithm under complex environment and recognition methods.The present invention these research contribute to target identification with Image procossing personnel understand the characteristics of motion, active degree and its influence to other targets of detection target, so as to provide corresponding Decision-making, seek to suppress or eliminate influence of the undesirable element to itself or other important goal to be all very important.This will be to army Important borrow is played in the development of all target recognition and trackings based on video system such as thing, civil, public security system, road traffic Mirror and reference role.

This moving small target tracking proposed by the present invention is with recognition methods compared with existing recognition methods, and emulation is tied Fruit is as shown in Fig. 5 and table 1.

To every width moving small target image in video, selected in emulation to being dimensioned so as to 2⁸Wavelet basis function according to this hair The method of bright proposition carries out 10 repetitions and tested, and experiment every time takes the sample of different numbers respectively.The inventive method and text at present That dedicates to out is compared with more two kinds of moving small targets tracking and method of identification, correct average when emulating 500 times Discrimination is respectively 95.14%, 92.45%, 88.17%.Moreover, the recognition speed of the inventive method is also recognized compared with mesh first two Method is very fast, as a result as shown in Fig. 5 and table 1.

Know from Fig. 5, the average correct recognition rata highest of the feature extraction method of identification based on the present invention.In experiment, with sample This number increases, and average correct recognition rata constantly increases, and reaches that increasing sample curve during certain sample size again gradually tends to be flat Surely.

In order to evaluate the combination property of each algorithm, we are according to calculating speed, amount of storage, the traffic, correct recognition rata etc. Several aspects, using the method for fixed guantity combining with fixed quality, Integrated comparative.It has rated the quality of different personal recognition methods.Table 1 is given Comprehensive comparison is gone out.

The Integrated comparative of the different motion Small object method of identification of table 1

Algorithm	Correct recognition rata average	Calculating speed	Amount of storage	The traffic
					Existing method 1	0.8817	0.82s	In	In
Existing method 2	0.9245	0.49s	It is relatively low	It is relatively low
					Proposition method of the present invention	0.9514	0.426s	It is low	It is low

Calculating speed in table 1 is that algorithm often walks the average computations all calculated used in 10 repetition experiments under simulated environment Time, simply the calculating time of algorithm in itself.It is the internal memory to 4,2G, programming language used to emulate the computer used It is MATLAB.Storage and traffic demands in table 1 are the calculating process according to various algorithms, what complexity was substantially estimated. Can be seen that amount of storage and the traffic from the result of table is closely linked system.Correct average recognition rate is that each algorithm exists in table Under given emulation experiment ambient conditions, after 500 emulation experiments are averaged, then the average value of 10 time steps is taken.It is real They are correct recognition rata being averaged on room and time on border, thus are the population means of correct recognition rata.

Know that faster processing speed, relatively low is not only had based on the method for identification that feature of present invention is extracted from simulation result Amount of storage and the traffic, and also preferable recognition effect.

The implementation of the present invention is as follows：

(1) the extraction mesh calibration method from the single-frame images of video sequence is provided, weakens or eliminate the shadow of background and noise Ring on the basis of based on mathematical morphology, it is proposed that the image object detection algorithm of opening and closing conversion.The main thought of the algorithm It is to ask for local maximum and minimum using mathematical morphology combinatorial operation, mitigates the amount of calculation of subsequent treatment, reduce as far as possible False alarm rate is counted.Carry out region growing, minimum point to each Local modulus maxima to implement to weaken or eliminate, to possible target Selected.

Make to reach that it shows as the zero of large area by the filtered image of opening and closing conversion for each frame using this algorithm High fdrequency component including background and comprising pinpoint target, and random spotted noise.Then moved according between consecutive frame The correlation of target, carries out difference multiframe superposition, and Small object is due to its motility, and it is very strong on superposition frame to show as correlation Tracing point, and high frequency spotted noise, then because its randomness is cancelled out each other a part, unmatched part is showed in geometric area For the random distribution noise spot that correlation is very poor.

For example, using a kind of simple opening operation conversion g=f-f ο B, being filtered to single-frame images, figure can be detected The place changed greatly as in, that is, high fdrequency component, while it is comparatively gentle to filter off gray-value variation in image Place, equivalent to low frequency component.A width single-frame images can be just filtered using this conversion, filter low-frequency component, phase When in filtering extended background, the HFS including leaving comprising Small object.In formula, f is gradation of image frame, and B is structure, ο represents opening operation.Using opening and closing conversion, the wherein frame result to the video image of moving small target is as shown in Figure 2.

(2) Weak target extraction of motion information and status predication modeling are provided

Target is detected and tracked, a reference template is initially set up as standard form.

Given known moving target, i.e., in the two field picture containing this target under the conditions of certain video, detect Its movement velocity, current location, direction of motion o, height h, gray average μ and the variances sigma of imaging.It assign the image of this frame as ginseng Examine template.

Set up reference template as follows：Using target location central point as the center of circle, to clap frame time and target in imaging plane Projection speed product for radius preceding half-circle area in, scan for.Using the change frequency of gray scale as thresholding, threshold is set Value.Search the change frequency of gray scale respectively along image width and image height direction, if the change frequency of both direction is both less than or equal to 2, Frame time is clapped in extension, until the grey scale change number of times at least one direction is more than 3 or 3 times.Then, using the center of circle as starting point, Calculate the distance between continuous three adjacent grey scale changes d₁And d₂, can obtain the motion amplitude of target and target a frame into Image width degree, Target Motion Character parameter s during using this amplitude and width as current location₁And s₂.It can be positioned by the saltus step of gray scale The position of targetAnd the direction of motion, speed and height of imaging etc. are calculated with it.The gray average of target in half-circle area It is another two characteristic parameter with variance, obtains a principal eigenvector V used in successive image processing special as standard Levy vector.

By the movable information of Small object, will provide motion state forecast model is

Here, F_i(k) it is the motion state transfer matrix from previous frame to present frame, suitable turn must be selected it Shifting formwork type,For the state estimation of previous frame,For the state estimation of present frame.

(3) incidence matrix of two inter frame image moving small targets is set up

Set up m × n object matching matrixes M.Here, m is the number of the moving small target of present frame, and n is the fortune of previous frame The number of dynamic Small object.The value of element M (i, j) is given by.

R in formula_iFor the radius of the i-th target；r_jFor the radius of jth target；(x_i,y_i) be the i-th target center-of-mass coordinate；(x_j, y_j) be jth target center-of-mass coordinate；∞ represents a very big numerical value.

Of the moving small target and moving small target in previous frame image in present image is realized using matrix is matched Match somebody with somebody.First, selective value is minimum in matrix M and is not ∞ element, and the row and column corresponding to the element is current kinetic respectively The numbering of Small object and previous frame moving small target, the corresponding moving small target of such row moving small target phase corresponding with row Match somebody with somebody.Then all elements value for the row and column for completing matching is changed into ∞.Minimum value is found in continuation in matrix M, completes motion The matching of Small object, until all values in matrix are all changed into ∞.After search terminates, the row for not finding matching target is represented There is the appearance of new moving small target in present image, the row for not finding matching target represent certain small mesh of motion in present image Mark disappears.

(4) information fusion being superimposed based on multiple image, is proposed using fuzzy pushdown automata chain bullet stack recursive operation Track algorithm and recognition methods of the large-scale image with video image motion Small object under network environment

Based on multiple features fusion, the present invention proposes description moving small target and calculated with tracking the data correlation of its movement locus Method.Propose feature and the joint probability and the coarse number of degrees of motion state based on present frame Yu previous frame moving small target simultaneously According to association algorithm, hypothesis testing method.Using joint probability and coarseness data association algorithm pre-estimation multimode Small object with Track algorithm.The information updating in rear frame is utilized to assume state using multihypothesis test method.Proposing these algorithms and method Meanwhile, provide suitable " thresholding ", only calculate the association probability and roughness of the observation being located in " thresholding " and Small object, this Amount of calculation is considerably reduced, Small object collision problem is solved with the distance of observation position and predicted position.

The moving small target tracking and identification of image are realized, a kind of suitable recognizer must be just proposed, you can recognize Moving small target characteristic vector for known class that is to be identified and having trained all is fuzzy number vector, by its feature to Amount compares, defined feature membership function, degree of membership and matched rule, provides fuzzy object recognizer.It is embodied as：

To implement to track to the track of moving small target in image or video, the present invention proposes the motion shape of multiframe superposition State blending algorithm：According to the motion state of moving small target in image or video, a kind of detect and tracking system will be designed Blending algorithm, to realize the tracking to moving small target.This algorithm is made up of three parts：(a) mapping tracking system, which enters, has N In the neutral net of individual feedback neural member；(b) N number of feedback neural member completion tracking system i.e. in multiframe fusion in time is allowed State fusion；(c) M non-feedback output neurons are made to complete spatially the state of the tracking system in i.e. single frames fusion again Fusion, finally calculates the Space Time accumulation fusion of tracking system state, come complete tracking system to the fusion of moving small target with Track algorithm.

Designing tracking system state fusion algorithm is：Here,It is by the 1st frame to kth frame The state estimation of i-th of Small object of the current kth frame of motion information prediction,It is all Small objects of current kth frame State estimation,It is the fuzzy membership of model.The key of tracking is that to be carved into the k-1 moment from the outset pre- by measurement Survey the fuzzy membership of current i-th of tracking system model of k momentHereIt is the fuzzy membership of k-1 moment tracking system models,It is known, π_ji =Pr { m_k=m⁽ⁱ⁾|m_k-1=m^(j)It is from model m_k-1To model m_kState transfer fuzzy membership.The tracking system shape of foundation State merges as shown in Figure 3 to the tracking process of moving small target.

On last superposition frame, Small object point shows as a correlation very strong track.But identify Small object point The still non-easy thing in track, mainly some discrete noise spots it is possible in the presence of.In addition it is also possible that tracing point and noise spot It is interlaced.Therefore it can only proceed from the situation as a whole to be made decisions according to overall relevancy under certain hypothesis.Therefore, proposing to be based on Fuzzy pushdown automata chain, track judgement is carried out by thresholding of the correlation length of track using the method for playing stack Yu popping.Tracking As a result it is as shown in Figure 4.From fig. 4, it can be seen that explanation fuzzy pushdown automata D-chain trace algorithm proposed by the present invention is known in pattern Larger success is achieved in other system.

The fuzzy membership of each component of the characteristic vector of moving small target to be identified or track is calculated according to blending algorithm μ_ij, you can the characteristic vector for obtaining Unknown Motion Small object or track is U_i=[μ_i1,μ_i2,…,μ_ik]^T.It with trained Good known i-th₀The multi-Dimensional parameters characteristic vector U of classification_i0Compare, and if only ifWhen, adjudicate fortune to be identified Dynamic Small object or track belong to i-th₀Class.EvenSo thatThen judge the small mesh of motion to be identified Mark or track belong to i-th₀Class.Here, δ is threshold value, and B is the index set of target or track class.

The innovative point of the present invention is as follows：

(1) how to set up data correlation to two inter frame image Small objects in video sequence is moving small target track algorithm Major Difficulties.The complexity of background under network environment, ratio that Small object is occupied in the picture is small, color of object and background color Similarity degree, the degree of stability of background, the interaction of multiple target and the generation of various special circumstances all can to moving small target with Track brings difficulty.The information such as Small object external appearance characteristic such as target shape and texture, because the generation for the process of blocking is several in the picture It is submerged, and the uncertainty that Small object is moved, cause the loss of Small object information, it is easy to occur tracking failure.Such as What effective processing is blocked, and particularly serious blocks, and is always a difficult point in moving small target tracking.In monitor video In, the Small object outward appearance in each frame is often closely similar, how to choose suitable feature and is transported with preferably distinguishing different Small objects Dynamic state realizes accurate data correlation, is the key issue that this project will be studied.And at present both at home and abroad to this The research of problem is almost a blank.

Discussion to this problem, the imaging characteristicses and movable information of present invention analysis Small object in the picture are right first The small and weak and kinetic characteristic of target, removes background using opening and closing conversion, eliminates or reduces the processing of noise scheduling algorithm.Then according to phase The motion relevance of moving small target between adjacent frame, carries out difference multiframe superposition.

(2) using moving small target characteristic in itself, movable information vector is set up, to extract the motion of Small object multi-characteristic points small It is the intractable and urgently to be resolved hurrily problem of present many departments that the multi-Dimensional parameters of target, which are extracted,.Present invention utilizes moving small target sheet The characteristic of body, excavates the characteristic point of each characteristic parameter of moving small target and characteristic parameter, sets up kinematics character vector, gives The multi-Dimensional parameters of moving small target are extracted.

(3) track is carried out using the information fusion and fuzzy pushdown automata chain recursive operation that are superimposed based on multiple image to sentence Certainly, set up model algorithm and feature based chooses parallel mechanism and carries out image recognition.

With science and technology development and the mankind security protection consciousness lifting, video monitoring system is in every field under network environment Increasingly it is widely applied., can be with and the identification of the moving target based on video monitoring is a particularly useful job Apply in space flight, military affairs, the tracking of guided missile track identification, the various fields such as break in traffic rules and regulations detection.But in for example infrared system of some occasions In leading, it is desirable to be able to intercept and capture as soon as possible and locking tracking target.So to the accurate detection and tracking of moving small target, in army The application of the every field such as thing, civil seems more and more important, also more and more urgent.It is low under the conditions of the strong clutter background of network environment The test problems of signal to noise ratio moving small target directly determine the operating distance and detection performance of detection system, and it is solved for improving Detection system performance has very important practical significance.

For the research of these problems, the present invention analyzes the various features of polymorphic target, utilizes tracking system state Fusion, fuzzy pushdown automata chain bullet stack recursive operation carry out track following to the moving small target of video image, provide motion Small object recognizer.

The specific embodiment of the present invention：

The tracking of moving small target under the low signal-to-noise ratio that the present invention is provided and identification problem, existing document also has similar Research, but all there is the weakness such as tracking real-time speed is poor compared with slow, tracking or recognition effect in these methods.However, this hair It is bright with it is a kind of stage by stage, according to target provide different implementations respectively, it is proposed that a kind of time-space domain based on single frames and multiframe The small target tracking algorithm of fused filtering and recognition methods (being based on such as Fig. 3).1. to the easy quilt of moving small target in video image The situation that other objects or noise in complex background are blocked or flooded, it is proposed that the elimination of opening and closing conversion weakens background with making an uproar The algorithm of sound；2. it is competing using its to the small and weak characteristic of Small object, it is proposed that the adaptive neural network competitive model of on-line study The active unit striven extracts the multidimensional characteristic parameter of Weak target；3. for the kinetic characteristic of video Small Target, gray scale is utilized Mutation, give Small object motion state model and forecast model；4. detection real-time to moving small target and tracking, are employed Fuzzy pushdown automata chain carries out track identification and tracking, and track judgement is carried out using fuzzy pushdown automata chain depth as threshold value, So as to propose a kind of moving small target track algorithm based under complex environment and recognition methods.

It is to the implementation of technical scheme 1.:

G=f-f ο B or closed operation conversion g=fB-f are converted using opening operation, single-frame images is filtered, detected The place changed greatly in image, that is, high fdrequency component, while it is comparatively gentle to filter off gray-value variation in image Place, equivalent to low frequency component, just a width single-frame images is filtered using this conversion, low-frequency component is filtered, quite In filtering extended background, the HFS including leaving comprising Small object；In formula, f is gradation of image frame, and B is structure, ο Opening operation is represented, closed operation is represented.

It is inadequate only to single frames processing, it is necessary to carry out Small object enhancing and interference to accurately identify target or track Suppress, because Small object point is moved between each frame, with very strong correlation, therefore it is folded that multiframe can be carried out to video image Plus.In the last frame of superposition, Small object point shows as correlation very strong tracing point, but noise is it is possible to flood Small object Track, therefore propose multi-frame difference superposition algorithm.Its algorithm is to choose the image sequence comprising including moving small target point, and its is strange Number frame and the superposition value of each n frames difference of even frame.I.e.

In formula, f_iFor the i-th frame in image sequence, f_zFor last superposition frame.

So after multi-frame difference is superimposed, Small object point is further enhanced, and extended background can also be obtained simultaneously Suppress to further, but noise spot can also may be strengthened, and therefore, it can through the stack plus frame takes thresholding to handle. Its method is as follows

Here, δ is threshold value, is taken according to many experimentsM, N are the chi of superposition two field picture It is very little.

After so handling, Small object point is further enhanced, and random noise point further can be suppressed or be disappeared Remove.

It is to the implementation of technical scheme 2.

The adaptive neural network competitive model of on-line study is constructed first, extracts weak using the active unit of its competition The multidimensional characteristic parameter of Small object.Its method is as follows：

P1. network is initialized：The dimension of fixed output nerve network grid is N × M, and input layer is quadravalence network, and Random initializtion inputs the weight of neuron and output neuron connection.Make t represent algorithm iteration number of times, put t=0.

P2. victor is selected：The parameter value X={ x such as gray scale, colourity, motion each frame Small object image₁,x₂, …, x_dThe input neuron in network is input to, to each input neuron value x_j, the output of the node i in competition layer ForHere G is an activation primitive, is such as takenα ＞ 0 are constants, can be with controlling curve Slope.μ_i (t) it is p dimensional input vectors x_jWith p dimensional weights vector ω_ji (t) Euclidean distance between | | x_j-ω_ji (t) | | and, i.e.,ω_ji (t) it is from input layer node j to competition layer node i connection weight in t Weight is vectorial, here j ∈ J, J={ 1 ..., d }, i ∈ I, I={ 1 ..., N_iBe competition layer certain regional area.

Select the output neuron i won^* .In competition layer, correspondenceMinimum node will win, if that is,The node so won in competition layer is i^*, then with i^* The weight of association and and i^* The neighbouring point association of point Weight can be all adjusted.

P3. weight is updated：If N (i^*) it is triumph output neuron i^*Neighbour, the distance between output neuron is specific Specify.To each output neuron i ∈ { N (i^*),i^*, weight adjusts renewal according to the following formula.

Here η (t)=η has determined that in advance.This rule only updates the neighbour of triumph output neuron.

P4. standardized weight：It is standardized after updating weight, so that they and input measurement standard are consistent.

P5. continue cycling through：Repeat step P1 to P4, the number of times of iteration is set to t=t+1, and criterion is shut down until meeting, Here shutting down criterion is | | x_j-ω_ji (t) | | ＜ ε, take ε=0.5, or untill having exceeded the cycle-index of maximum.

It is to the implementation of technical scheme 3.:

Gray scale mutation algorithm is as follows：

If being bianry image original image pretreatment, the threshold value utilized is 1, calculates transition times.To horizontally and vertically side The image Small object separation calculation implemented to Gray Level Jump is as follows：

It is determined by experiment the high m and width n of image.On the one hand, in the case of a certain fixing point of image height, i.e. height=m_i When, look for transition times c along image width direction_i, i=1 ..., m.Known according to the experiment to Small object and background area, Small object The transition times in region than it is larger the characteristics of, by transition times c_i, give a threshold value M₁.Any transition times are more than this threshold Value, i.e. c_i＞ M₁, Gao Yukuan at this moment is recorded, y is designated as respectively_k1And x_k1, k₁∈N .Obtain ordered pair collection

On the other hand, in the case of a certain fixing point of image width, i.e. width=n_jWhen, look for transition times along image height direction d_j, j=1 ..., n.By transition times d_j, give a threshold value M₂.Any transition times are more than this threshold value, i.e. d_j＞ M₂ , width and height at this moment is recorded, is designated as respectivelyWithk₂∈N .Obtain ordered pair collection

By (I) and (II), ordered pair collection I=I can be tried to achieve₁ ∪I₂={ (x_k,y_k),k ∈N}.According to (x_k,y_k), can Obtain x_kAnd y_kThe curve of satisfaction, implements the modeling of Small object curve movement, can obtain Small object i curve movements s_i.Claim this motion bent The algorithm of line modeling is Gray Level Jump algorithm.

Using the mutation of gray scale, Small object motion state model s is given_i, while can also provide forecast model V_iIt is as follows： The characteristics of present invention is proposed using gray value saltus step, in the horizontal direction, vertical direction, 1/4 arc direction, is determined fortune Direction, distance and the motion feature of dynamic Small object.Using pixel i as summit, the window that a size is r × r is opened in its neighborhood Mouthful.Calculate the gray average μ of this window_i With variances sigma_i, it is used as pixel i two characteristic parameters.Using this pixel i as circle The heart, r is that radius makees a quarter turn, labeled as Θ_i (1/4) .Along image width radial direction, image height radial direction, 1/4 camber line side To the change frequency c for finding out gray scale respectively_h、c_v、c_1/4, these three parameters are defined as pixel i direction character vector, are designated as dir_i=(c_ih,c_iv,c_i,1/4) .Again along 1/4 arc direction, the position Q of Gray Level Jump is marked_kl(x_k,y_l), k, l ∈ N are calculated The distance of adjacent two trip pointK, l=1,2 ..., and the gray value at any point between adjacent two trip point is calculated simultaneously G, G '.Calculate the close distance of gray valueAverage valueIn conjunction with motion state parameterses s_i, then picture can be obtained Vegetarian refreshments i characteristic vector is

It is to the implementation of technical scheme 4.:

Tracking system is mapped to enter in the neutral net of the fuzzy pushdown automata with N number of feedback neural member；Make N individual Feedback neural member is the state fusion that tracking system is completed in multiframe fusion in time；Make M non-feedback output neurons again To complete spatially the state fusion of the tracking system in i.e. single frames fusion, the Space Time for finally calculating tracking system state is tired out Product fusion, to complete fusion tracking algorithm of the tracking system to moving small target：

1. each fuzzy pushdown automata recognizes fusion in time to moving small target

If f_ipAnd Ξ (t)_i (t) represent that t is belonged to by obscuring the identified moving small target that pushdown automata i is measured respectively In the fuzzy membership and Fuzzy Distribution of pth class,Represent untill the l moment by i-th of fuzzy pushdown automata accumulation fusion Obtained identified target belongs to the fuzzy membership of pth class,Represent untill the l moment by i-th of fuzzy pushdown automata The Fuzzy Distribution for the identified target that accumulation fusion is obtained, here, l=1,2 ..., t, i.e.,

With

Here, o_p(p ∈ U) is moving small target.By the accumulation fusion Fuzzy Distribution at t-1 moment and the measurement at t moment Fuzzy Distribution is merged, and can obtain target identification accumulation fusion Fuzzy Distribution of i-th of fuzzy pushdown automata untill tFor：

Wherein,S₂It is Fuzzy Integration Function, usual S₂Remove formula.

Now, with Fuzzy DistributionThe motion state of corresponding Small object isIt is the small mesh of current t Target state estimation.

Here, F_i(t) it is the motion state transfer matrix from last moment to current time, must be suitable to selecting Metastasis model,For the state estimation of last moment,For the state estimation at current time.

2. the Space integration that pushdown automata is recognized to Small object is obscured

Obtaining the accumulation Fuzzy Distribution of t moment each fuzzy pushdown automata target identificationAfterwards, i=1 here, ..., N is merged to this N number of Fuzzy Distribution using Fuzzy Integration Function, just obtained untill the t moment to target identification Fuzzy Distribution is merged in Space Time accumulation

It is theoretical using Fuzzy Integration Function, it can obtain

Here, S_NAlso illustrate that Fuzzy Integration Function.IfP=1 ..., M.

Now, with Fuzzy Distribution Ξ^t The motion state of corresponding Small object isIt is all small of current kth frame The state estimation of target.Motion state fusion results are：Here,It is by the 1st frame to kth frame The current kth frame of motion information prediction i-th of Small object state estimation,It is all small mesh of current kth frame Target state estimation,It is the fuzzy membership of model；The key of tracking is when being carved into k-1 from the outset by measurement Carve the fuzzy membership of current i-th of the tracking system model of k moment of predictionHereIt is the fuzzy membership of k-1 moment tracking system models,Be it is known, π_ji=Pr { m_k=m⁽ⁱ⁾|m_k-1=m^(j)It is from model m_k-1To model m_kState transfer fuzzy membership.

So after multiframe is merged, Small object point is further enhanced, and most of noise spot is cut, but some with Machine noise jamming may be strengthened, in order to filter out these noises, can be sentenced by fuzzy pushdown automata chain length Certainly.In order to reduce fuzzy pushdown automata chain length as far as possible while Small object point is retained, threshold value is taken to fusion frame Change is handled.Its method is as follows:

Threshold value T is the length of fuzzy pushdown automata chain, but this chain must assure that containing Small object point.Here f_ATo be total Merge frame.On last fusion frame, Small object point shows as a correlation very strong track.

Each component f of the characteristic vector of moving small target to be identified or track is calculated according to blending algorithm_AFuzzy membership Spend μ_ij, that is, the characteristic vector for obtaining Unknown Motion Small object or track is U_i=[μ_i1 , μ_i2 , …, μ_ik ]^T；It with Trained good known i-th₀The multi-Dimensional parameters characteristic vector of classificationCompare, and if only ifWhen, judgement is treated Identification moving small target or track belong to i-th₀Class；EvenSo thatThen judge fortune to be identified Dynamic Small object or track belong to i-th₀Class；Here, δ is threshold value, and B is the index set of target or track class.

The tracking of moving small target under the low signal-to-noise ratio that the present invention is provided and identification problem, it is proposed that one kind is based on single frames Small target tracking algorithm and recognition methods with the time-space domain fused filtering of multiframe.To the easy quilt of moving small target in video image The situation that other objects or noise in complex background are blocked or flooded, it is proposed that the elimination of opening and closing conversion weakens background with making an uproar The algorithm of sound；To the small and weak characteristic of Small object, it is proposed that the adaptive neural network competitive model of on-line study, its competition is utilized Active unit extract Weak target multidimensional characteristic parameter；For the kinetic characteristic of video Small Target, the prominent of gray scale is utilized Become, give Small object motion state model and forecast model；Detection real-time to moving small target and tracking, are employed under obscuring Push away automatic chain and carry out track identification and tracking, track judgement is carried out using fuzzy pushdown automata chain depth as threshold value, so as to carry A kind of moving small target track algorithm based under complex environment and recognition methods are gone out.These researchs of the present invention contribute to mesh Mark does not understand the characteristics of motion, active degree and its influence to other targets of detection target with image procossing personnel, so that Corresponding decision-making is provided, seeks to suppress or eliminate influence of the undesirable element to itself or other important goal to be all very important. This plays the development to all target recognition and trackings based on video system such as military, civil, public security system, road traffic Important reference and reference role.

The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention Any modifications, equivalent substitutions and improvements made within refreshing and principle etc., should be included in the scope of the protection.

Claims (6)

1. a kind of tracking of low signal-to-noise ratio moving small target is with knowing method for distinguishing, it is characterised in that the low signal-to-noise ratio moves small mesh Target track with know method for distinguishing with it is a kind of stage by stage, according to target provide different implementations respectively, in video image fortune The situation that dynamic Small object is easily blocked or flooded by other objects or noise in complex background, it is proposed that the elimination of opening and closing conversion Or weaken the algorithm of background and noise；To the small and weak characteristic of Small object, it is proposed that the adaptive neural network competition of on-line study Model, the multidimensional characteristic parameter of Weak target is extracted using the active unit of competition；For the kinetic characteristic of video Small Target, Using the mutation of gray scale, Small object motion state model and forecast model are given；Detection real-time to moving small target and tracking, Employ fuzzy pushdown automata chain and carry out track identification and tracking, track is carried out using fuzzy pushdown automata chain depth as threshold value Judgement；

Specifically include following steps：

Step one, the extraction mesh calibration method from the single-frame images of video sequence is provided, weakens or eliminate the shadow of background and noise Ring；

Step 2, provides Weak target extraction of motion information and status predication modeling；

Step 3, sets up the incidence matrix of two inter frame image moving small targets；

Step 4, the information fusion being superimposed based on multiple image, is proposed using fuzzy pushdown automata chain bullet stack recursive operation Track algorithm and recognition methods of the large-scale image with video image motion Small object under network environment.

2. the tracking of low signal-to-noise ratio moving small target as claimed in claim 1 is with knowing method for distinguishing, it is characterised in that in step In one, the extraction mesh calibration method from the single-frame images of video sequence, during the influence of decrease or elimination background and noise, tool are provided Body implementation method is：

Local maximum and minimum are asked for using mathematical morphology combinatorial operation, mitigates the amount of calculation of subsequent treatment, subtracts as far as possible Few false alarm rate points, region growing, minimum point are carried out to each Local modulus maxima and implements to weaken or eliminates, to possible mesh Mark is selected；

Converted using opening operationOr closed operation conversion g=fB-f, single-frame images is filtered, figure is detected The place changed greatly as in, that is, high fdrequency component, while it is comparatively gentle to filter off gray-value variation in image Place, equivalent to low frequency component, is just filtered to a width single-frame images using this conversion, filters low-frequency component, equivalent to Extended background is filtered, the HFS including leaving comprising Small object；In formula, f is gradation of image frame, and B is structure,Table Show opening operation, represent closed operation；

To accurately identify target or track, the suppression of Small object enhancing and interference is carried out, because Small object point is transported between each frame It is dynamic, multiframe superposition can be carried out to video image, in the last frame of superposition, Small object point shows as correlation very strong track Point, but noise, it is possible to flooding Small object track, proposition multi-frame difference superposition algorithm is chosen comprising including moving small target point Image sequence, the superposition value of odd-numbered frame and each n frames difference of even frame, i.e.,：

<mrow> <msub> <mi>f</mi> <mi>z</mi> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>0</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>f</mi> <mrow> <mn>2</mn> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>f</mi> <mrow> <mn>2</mn> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow>

In formula, f_iFor the i-th frame in image sequence, f_zFor last superposition frame；

Through the stack plus frame takes thresholding to handle, method is as follows：

<mrow> <msub> <mi>f</mi> <mi>z</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>f</mi> <mi>z</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>f</mi> <mi>z</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <mi>&amp;delta;</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>f</mi> <mi>z</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mi>&amp;delta;</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>

δ is threshold value, is takenM, N are the size of superposition two field picture.

3. the tracking of low signal-to-noise ratio moving small target as claimed in claim 1 is with knowing method for distinguishing, it is characterised in that in step In two, when providing Weak target extraction of motion information and status predication modeling, concrete methods of realizing is：

The adaptive neural network competitive model of on-line study is constructed first, and small and weak mesh is extracted using the active unit of its competition Target multidimensional characteristic parameter：

The first step, initializes network：The dimension of fixed output nerve network grid is N × M, and input layer is quadravalence network, and with Machine initialization input neuron and the weight of output neuron connection, make t represent algorithm iteration number of times, put t=0；

Second step, selects victor：The gray scale of each frame Small object image, colourity, motion parameter X={ x₁,x₂,…,x_dDefeated Enter the input neuron into network, to each input neuron value x_j, the output of the node i in competition layerForG is an activation primitive, andα ＞ 0 are constant, the slope of controlling curve；μ_i(t) it is p Dimensional input vector x_jWith p dimensional weights vector ω_ji(t) Euclidean distance between | | x_j-ω_ji(t) | | and, i.e.,ω_ji(t) it is from input layer node j to competition layer node i connection weight vector, j in t ∈ J, J={ 1 ..., d }, i ∈ I, I={ 1 ..., N_iBe competition layer certain regional area；

Select the output neuron i won^*, in competition layer, correspondenceMinimum node will win, if that is, The node so won in competition layer is i^*, then with i^*The weight of association and and i^*The weight of the neighbouring point association of point can all be obtained Adjustment；

3rd step, updates weight：N(i^*) it is triumph output neuron i^*Neighbour, the distance between output neuron refers specifically to It is fixed, to each output neuron i ∈ { N (i^*),i^*, adjust renewal according to the following formula：

<mrow> <msub> <mi>&amp;omega;</mi> <mrow> <mi>a</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&amp;omega;</mi> <mrow> <mi>a</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>&amp;eta;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>a</mi> </msub> <mo>-</mo> <msub> <mi>&amp;omega;</mi> <mrow> <mi>a</mi> <mi>j</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mi>f</mi> <mi> </mi> <mi>j</mi> <mo>&amp;Element;</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>o</mi> <mi>t</mi> <mi>h</mi> <mi>e</mi> <mi>r</mi> <mi>w</mi> <mi>i</mi> <mi>s</mi> <mi>e</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>

η (t)=η has determined that in advance；This rule only updates the neighbour of triumph output neuron；

4th step, standardized weight：Standardized again after updating weight, so as to be consistent with input measurement standard；

5th step, is continued cycling through：The first step is repeated to the 4th step, the number of times of iteration is set to t=t+1, standard is shut down until meeting Then, shutting down criterion is | | x_j-ω_ji(t) | | ＜ ε, take ε=0.5, or untill having exceeded the cycle-index of maximum.

4. the tracking of low signal-to-noise ratio moving small target as claimed in claim 1 is with knowing method for distinguishing, it is characterised in that in step In three, the implementation method for setting up the incidence matrix of two inter frame image moving small targets is：

M × n object matching matrix As are set up, here, m is the number of the moving small target of present frame, and n is that the motion of previous frame is small The number of target, the value of elements A (i, j) is given by：

<mrow> <mi>A</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mo>|</mo> <msub> <mi>r</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>r</mi> <mi>j</mi> </msub> <mo>|</mo> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mi>f</mi> <mi> </mi> <msub> <mi>r</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>r</mi> <mi>j</mi> </msub> <mo>&gt;</mo> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>x</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>y</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>&amp;infin;</mi> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>e</mi> <mi>l</mi> <mi>s</mi> <mi>e</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>

R in formula_iFor the radius of the i-th target；r_jFor the radius of jth target；(x_i,y_i) be the i-th target center-of-mass coordinate；(x_j,y_j) For the center-of-mass coordinate of jth target；∞ represents a very big numerical value；

First, selective value is minimum in matrix A and is not ∞ element, and the row and column corresponding to the element is current kinetic respectively The numbering of Small object and previous frame moving small target, the corresponding moving small target of row matches with the corresponding moving small target of row, Then all elements value for the row and column for completing matching is changed into ∞；Minimum value is found in continuation in matrix A, completes the small mesh of motion Target is matched, until all values in matrix are all changed into ∞；After search terminates, the row for not finding matching target is represented current There is the appearance of new moving small target in image, do not find the row representative of matching target has moving small target to disappear in present image Lose.

5. the tracking of low signal-to-noise ratio moving small target as claimed in claim 1 is with knowing method for distinguishing, it is characterised in that in step In four, the information fusion being superimposed based on multiple image proposes network rings using fuzzy pushdown automata chain bullet stack recursive operation The track algorithm of large-scale image and video image motion Small object is with recognition methods under border：

The first step, each fuzzy pushdown automata recognizes fusion in time to moving small target：

f_ipAnd Ξ (t)_i(t) represent that t belongs to pth class by obscuring the identified moving small target that pushdown automata i is measured respectively Fuzzy membership and Fuzzy Distribution,Represent untill the l moment what is obtained by i-th of fuzzy pushdown automata accumulation fusion Identified target belongs to the fuzzy membership of pth class,Represent untill the l moment to be melted by i-th of fuzzy pushdown automata accumulation The Fuzzy Distribution of obtained identified target is closed, here, l=1,2 ..., t, i.e.,

<mrow> <msub> <mi>&amp;Xi;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>U</mi> </mrow> </munder> <msub> <mi>f</mi> <mrow> <mi>i</mi> <mi>p</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>/</mo> <msub> <mi>o</mi> <mi>p</mi> </msub> </mrow>

With

<mrow> <msubsup> <mi>&amp;Xi;</mi> <mi>i</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msubsup> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>U</mi> </mrow> </munder> <msubsup> <mi>f</mi> <mrow> <mi>i</mi> <mi>p</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>/</mo> <msub> <mi>o</mi> <mi>p</mi> </msub> </mrow>

o_pIt is moving small target, wherein, p ∈ U, by the accumulation fusion Fuzzy Distribution and the measurement Fuzzy Distribution of t at t-1 moment Merged, obtain target identification accumulation fusion Fuzzy Distribution of i-th of fuzzy pushdown automata untill tFor：

<mrow> <msubsup> <mi>&amp;Xi;</mi> <mi>i</mi> <mi>t</mi> </msubsup> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>U</mi> </mrow> </munder> <msubsup> <mi>f</mi> <mrow> <mi>i</mi> <mi>p</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mo>/</mo> <msub> <mi>o</mi> <mi>p</mi> </msub> </mrow>

Wherein,S₂It is Fuzzy Integration Function, usual S₂Remove formula：

<mrow> <msub> <mi>S</mi> <mi>t</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>p</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mn>1</mn> <mi>t</mi> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>t</mi> </munderover> <msubsup> <mi>f</mi> <mrow> <mi>i</mi> <mi>p</mi> </mrow> <mi>q</mi> </msubsup> <mo>(</mo> <mi>l</mi> <mo>)</mo> <mo>)</mo> </mrow> <mfrac> <mn>1</mn> <mi>q</mi> </mfrac> </msup> <mo>,</mo> <mi>q</mi> <mo>&gt;</mo> <mn>0</mn> </mrow>

Now, with Fuzzy DistributionThe motion state of corresponding Small object is

<mrow> <msubsup> <mover> <mi>x</mi> <mi>&amp;Lambda;</mi> </mover> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>|</mo> <mi>t</mi> </mrow> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msup> <mover> <mi>x</mi> <mi>&amp;Lambda;</mi> </mover> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>|</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>F</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msup> <mover> <mi>x</mi> <mi>&amp;Lambda;</mi> </mover> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>|</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>

F_i(t) it is the motion state transfer matrix from last moment to current time, selects metastasis model,For last moment State estimation,For the state estimation at current time；

Second step, the Space integration that fuzzy pushdown automata is recognized to Small object：

Obtaining the accumulation Fuzzy Distribution of each fuzzy pushdown automata target identification of tAfterwards, i=1 ..., N here, profit This N number of Fuzzy Distribution is merged with Fuzzy Integration Function, has just obtained untill t tiring out the Space Time of target identification Product merges Fuzzy Distribution：

<mrow> <msup> <mi>&amp;Xi;</mi> <mi>t</mi> </msup> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>p</mi> <mo>&amp;Element;</mo> <mi>U</mi> </mrow> </munder> <msubsup> <mi>f</mi> <mi>p</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mo>/</mo> <msub> <mi>o</mi> <mi>p</mi> </msub> </mrow>

It is theoretical using Fuzzy Integration Function, it can obtain

<mrow> <msubsup> <mi>f</mi> <mi>p</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msub> <mi>S</mi> <mi>N</mi> </msub> <mo>&amp;lsqb;</mo> <msubsup> <mi>f</mi> <mrow> <mn>1</mn> <mi>p</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>f</mi> <mrow> <mn>2</mn> <mi>p</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mo>,</mo> <mo>...</mo> <mo>,</mo> <msubsup> <mi>f</mi> <mrow> <mi>N</mi> <mi>p</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mo>&amp;rsqb;</mo> </mrow> 3

S_NAlso illustrate that Fuzzy Integration Function；IfP=1 ..., M；

Now, with Fuzzy Distribution Ξ^tThe motion state of corresponding Small object isIt is all Small objects of current kth frame State estimation, motion state fusion results are： It is pre- by the movable information of the 1st frame to kth frame The state estimation of i-th of Small object of current kth frame is surveyed,It is that the motion states of all Small objects of current kth frame is estimated Meter,It is the fuzzy membership of model；The key of tracking is to be carved into the k-1 moment from the outset by measurement to predict the current k moment The fuzzy membership of i-th of tracking system modelHere It is the fuzzy membership of k-1 moment tracking system models,It is known, π_ji=Pr { m_k=m⁽ⁱ⁾|m_k-1 =m^(j)It is from model m_k-1To model m_kState transfer fuzzy membership.

6. the tracking of low signal-to-noise ratio moving small target as claimed in claim 5 is with knowing method for distinguishing, it is characterised in that through excessive After frame fusion, Small object point is further enhanced, and most of noise spot is cut, under filtering out random noise disturbance by obscuring Push away automatic machine chain length to make decisions, in order to reduce fuzzy pushdown automata chain length as far as possible while Small object point is retained Degree, thresholding processing is taken to fusion frame, and method is as follows:

<mrow> <msub> <mi>f</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>f</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>f</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <mi>T</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>f</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mi>T</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>

Threshold value T is the length of fuzzy pushdown automata chain, but this chain must assure that containing Small object point, f_AFor total fusion frame, On last fusion frame, Small object point shows as a correlation very strong track；

Each component f of the characteristic vector of moving small target to be identified or track is calculated according to blending algorithm_AFuzzy membership μ_ij, The characteristic vector for obtaining Unknown Motion Small object or track is U_i=[μ_i1,μ_i2,…,μ_ik]^T；It has been trained together Know i-th₀The multi-Dimensional parameters characteristic vector of classificationCompare, and if only ifWhen, adjudicate the small mesh of motion to be identified Mark or track belong to i-th₀Class；EvenSo thatThen judge moving small target to be identified or track Belong to i-th₀Class；Here, δ is threshold value, and λ is the index set of target or track class.