CN108320305A - A kind of multispectral image Dim target tracking method based on DS evidence theories - Google Patents
A kind of multispectral image Dim target tracking method based on DS evidence theories Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
- G06T7/344—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving models
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- G—PHYSICS
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- G06F18/00—Pattern recognition
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- G06F18/25—Fusion techniques
- G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
Abstract
The present invention is based on multispectral images, provide a kind of method for realizing Dim target tracking under the sky background of face with DS evidence theories, are related to target following, image processing field.The present invention establishes triangle fuzzy model to target, cloud, sky, classifies to pixel after model is broadened, and obtains tracking of the target location realization to target according to pixel classification results, pixel classification results is used in combination to update current Triangular Fuzzy Number model.The present invention classifies to pixel using DS evidence theories, this sorting technique has preferably merged the image information of different-waveband, can realize target following, has the advantages that calculating is simple, real-time is good；Interference elimination method proposed by the present invention eliminates erroneous judgement caused by random disturbances well, can choose true tracking target.
Description
Technical field
It is that one kind realizing more under the sky background of face based on DS evidence theories the present invention relates to target following, image processing field
The method of spectrum picture Dim target tracking.
Background technology
Target following is one of the hot spot in computer vision research field, and is used widely.In simple terms, target with
Track is exactly to establish the position relationship for the object of being tracked in continuous video sequence, obtain the complete movement locus of object.It gives
Determine the target coordinate position of image first frame, calculates the accurate location of the target in next frame image.In modernized war, in order to
Increase operational distance, usually require that remote tracking lock target, realizes that quickly and effectively target is hit.However, for long distance
From imaging the useful target such as target shape, texture is unable to get since target imaging area is smaller, signal noise ratio (snr) of image is relatively low
Feature, therefore the detecting and tracking of Weak target is relatively difficult.
Information fusion technology is that collaboration utilizes multi-source information, to obtain more objective to things or target, more essential understanding
Informix treatment technology is one of the key technology of intelligence science research.In many Fusion Models and method, D-S cards
It is maximally efficient one of algorithm according to theoretical algorithm.Evidence theory widens the space of elementary events in probability theory for elementary event
Power set, also known as framework of identification establishes Basic probability assignment function (Basic Probability on framework of identification
Assignment, BPA).In addition, evidence theory additionally provides a Dempster rule of combination, which can be in no elder generation
The fusion of evidence is realized in the case of testing information.Particularly, when BPA is only allocated in the list collection proposition of framework of identification
When, BPA is converted to the probability in probability theory, and the fusion results of rule of combination are identical as the Bayes formula in probability theory.From
From the point of view of this angle, DS evidence theories more effectively can indicate and handle uncertain information than probability theory, these features make it
It is widely used in information fusion field.Just because of DS evidence theories with excellent in terms of uncertain knowledge expression
Performance, so its theoretical and application development was very fast in recent years, the theory is in multi-sensor information fusion, medical diagnosis, military affairs
Important function has been played in terms of commander, target identification.
There has been no the researchs that multispectral image Dim target tracking is realized based on evidence theory at present.Evidence theory has many
Advantage, applied has important military value on multispectral image Dim target tracking.
Invention content
In order to realize Dim target tracking, the present invention is based on multispectral images, provide a kind of DS evidence theories realization face
The method of Dim target tracking under empty background.There is important military-civil value using the Dim target tracking that this method is realized.
The technical solution adopted by the present invention to solve the technical problems includes the following steps：
Step 1：Input each wave band cloud (C), sky (S) and target under a pattern sky multispectral image and current environment
(T) minimum gray value, intermediate value and maximum value, according to the cloud, sky and current goal of input imaging minimum gray value, intermediate value,
Maximum value, establishes corresponding Triangular Fuzzy Number model, and framework of identification is Θ={ C, S, T }, and C indicates that cloud, S indicate in framework of identification
Sky, T indicate that target, the method for C, S, T Triangular Fuzzy Number model foundations are：
The minimum gray value Cmin that wave band i clouds are imagedi, intermediate value CaveiAnd maximum value CmaxiRespectively as wave band i clouds three
The minimum value of angle Fuzzy Math Model, intermediate value, maximum value, then the Triangular Fuzzy Number of wave band i clouds be
The minimum gray value Smin that wave band i skies are imagedi, intermediate value SaveiAnd maximum value SmaxiRespectively as wave band i sky Triangle Modules
The minimum value of paste exponential model, intermediate value, maximum value, the then Triangular Fuzzy Number that wave band i skies are established are
By the minimum gray value Tmin of wave band i target imagingsi, intermediate value TaveiAnd maximum of T maxiWork as respectively as wave band i
The minimum value of preceding target Triangular Fuzzy Number model, intermediate value, maximum value, the then Triangular Fuzzy Number that wave band i current goals are established are
Step 2：The upper bound of fuzzy number is increased, lower bound reduction, is remembered respectively by the fuzzy number broadening that step 1 is obtained
Cloud, sky, current goal broadening after fuzzy number beThe method for widening is：
Step 3：Classify to each pixel p in input picture, classification results may be cloud, sky, target：
1) to each pixel p, its wave band i gray values G is takeni, use GiWithGenerate basic probability assignment
Function mi, describedThe Triangular Fuzzy Number of respectively aforementioned wave band i clouds, sky, target, it is described substantially general
Rate partition function is defined as belonging to any one subset A, m (A) ∈ [0,1] of Θ in evidence theory, and meetsThen m is 2ΘOn Basic probability assignment function, wherein 2ΘFor the power set of framework of identification,The Basic probability assignment function miGeneration method
For：By GiWith fuzzy numberThe high point (not least point) of intersection point is assigned to the reliability of corresponding single subset elements, by Gi
With fuzzy numberThe minimum point of intersection point is assigned to the reliability of corresponding double subset elements or more subsets, wherein described
Single subset elements refer to that the subset { C } of framework of identification Θ in step 1, { S } or { T }, double subset elements refer to walking
The subset { C, S } of framework of identification Θ, { C, T } or { S, T }, more subset elements refer to framework of identification in step 1 in rapid one
The subset { C, S, T } of Θ；Remember that the sum of reliability generated is Sum, normalizes the reliability of generation to obtain m with Sumi；
2) the Basic probability assignment function m for generating 25 wave bandsiIt merges to obtain m using average fusion method, it is described flat
Equal fusion method is：Whereinmi(i=1,2 ..., 25) it is the base generated in step 2
This probability distribution function；
3) m after fusion is converted into probability point using Pignistic probability transformation methods
Cloth P, the conversion method are：Wherein
4) classify to pixel p according to obtained probability distribution P, take P ({ C }), maximum probability in P ({ S }), P ({ T })
Corresponding classification as pixel p classification results (if maximum probability more than one and include P ({ T }), using T as pixel
The classification results of point p), C indicates that cloud, S indicate that sky, T indicate target in classification results；
Step 4：Interference elimination is carried out to accidentally knowing point, to choose true tracking target, the principle of interference elimination is to utilize
The position correlation of consecutive frame target area is realized：
In all target areas of present frame in selected distance in frame image the nearest region in target location as mesh to be selected
Mark, if distance is in rational threshold interval, then it is assumed that target to be selected is the target currently tracked, otherwise it is assumed that being random dry
It disturbs, threshold interval can be chosen according to target speed (such as can be selected as threshold interval 0.5 times of target single frames displacement~1.5
Times target single frames displacement)；
Step 5：According to the pixel classification results of present frame update previous frame target, the Triangular Fuzzy Number mould of cloud, sky
Type, using updated model as next frame image cloud, sky, the other Fuzzy Math Model of three type of target, the model modification
Method is：
For all pixels for being identified as cloud, the maximum value max and minimum value min of these pixel wave bands i are found out,
If max>Cmaxi, willThe upper bound is updated to max, if min<Cmini, willLower bound is updated to min；It is identified as day for all
Empty pixel, finds out the maximum value max and minimum value min of these pixel wave bands i, if max>Smaxi, willThe upper bound updates
For max, if min<Smini, willLower bound is updated to min；For all pixels for being identified as target, these pixels are found out
The maximum value max and minimum value min of point wave band i, if max>Tmaxi, willThe upper bound is updated to max, if min<Tmini, willUnder
Boundary is updated to min.
The beneficial effects of the present invention are the present invention to classify to pixel using DS evidence theories, can be according to pixel point
Class result selects target, realizes Dim target tracking, therefore sorting technique proposed by the present invention has preferably merged different waves
The image information of section has the advantages that calculating is simple, real-time is good；Triangular Fuzzy Number modeling method proposed by the present invention, very well
The problem of representation for solving fuzzy message；Basic probability assignment function generation method proposed by the present invention, realizes well
Processing to fuzzy message；Interference elimination method proposed by the present invention eliminates erroneous judgement caused by random disturbances well.
Description of the drawings
The general flow chart that Fig. 1 present invention realizes.
Fig. 2 is the other fuzzy number of 1 three type of wave band.
Fig. 3 is the fuzzy number after 1 three kinds of classification broadenings of wave band.
Specific implementation mode
The present invention is further described with example below in conjunction with the accompanying drawings.
Step 1：Input each wave band cloud (C), sky under the face sky multispectral image and current environment of 25 wave band of a frame
(S) and target (T) image space, minimum gray value, intermediate value and maximum value it, is imaged according to the cloud, sky and current goal of input
Minimum gray value, intermediate value, maximum value, establish corresponding Triangular Fuzzy Number model, framework of identification is Θ={ C, S, T }, identification
C indicates that cloud, S indicate that sky, T indicate that target, the method for C, S, T Triangular Fuzzy Number model foundations are in frame：
Minimum gray value 97, intermediate value 121.5 and the maximum value 146 for inputting the imaging of 1 cloud of wave band, respectively as 1 cloud three of wave band
The minimum value of angle Fuzzy Math Model, intermediate value, maximum value, then the Triangular Fuzzy Number of 1 cloud of wave band beBy wave
Minimum gray value 99, intermediate value 109 and the maximum value 119 of section 1 sky imaging are respectively as 1 sky Triangular Fuzzy Number model of wave band
Minimum value, intermediate value, maximum value, the then Triangular Fuzzy Number that 1 sky of wave band is established areBy 1 target of wave band at
The minimum gray value 113 of picture, 119 maximum value 125 of intermediate value respectively as 1 target Triangular Fuzzy Number model of wave band minimum value, in
Value, maximum value, the then Triangular Fuzzy Number that 1 current goal of wave band is established are
Step 2：The upper bound of fuzzy number is increased, lower bound reduction, is remembered respectively by the fuzzy number broadening that step 1 is obtained
Cloud, sky, current goal broadening after fuzzy number beThe method for widening is：
WithBroadening for, the result after broadening is：
Step 3：Classify to any pixel point p in input picture, classification results may be cloud, sky, target：
1) to any classified pixels point p, its wave band i gray values G is takeni, use GiWithGenerate elementary probability
Partition function mi, describedThe Triangular Fuzzy Number of respectively aforementioned wave band i clouds, sky, target, it is described basic
Probability distribution function is defined as belonging to any one subset A, m (A) ∈ [0,1] of Θ in evidence theory, and meetsThen m is 2ΘOn Basic probability assignment function, wherein 2ΘFor the power set of framework of identification,The Basic probability assignment function miGeneration method
For：By GiWith fuzzy numberThe high point of intersection point is assigned to the reliability of corresponding single subset elements, by GiWith fuzzy numberThe minimum point of intersection point is assigned to the reliability of corresponding double subset elements or more subsets, wherein the list element of set
Element refers to that the subset { C } of framework of identification Θ in step 1, { S } or { T }, double subset elements refer to distinguishing in step 1
Know the subset { C, S } of frame Θ, { C, T } or { S, T }, more subset elements refer to the subset of framework of identification Θ in step 1
{C,S,T}；Remember that the sum of reliability generated is Sum, normalizes the reliability of generation to obtain mi；
The pixel p of input is 101 in the gray value of wave band 1, then m1Generation method is as follows：
Such as Fig. 3, gray value 101 withIntersection point, respectively 0.3077 there are one each, 0.4058,
0.6000, by 0.3077 reliability as complete or collected works { C, S, T }, by 0.4058 reliability as cloud (C), day is used as by 0.6000
The reliability of empty (S).The normalization of obtained reliability is obtained m by Sum=1.3135 at this time1：
m1{ S }=0.6000/1.3135=0.4568, m1{ C }=0.4056/1.3135=0.3089,
m1{ C, S, T }=0.3077/1.3135=0.2343
With same method, m is obtained2~m25It is as follows：
m2{ T }=0.4495, m2{ C }=0.4054, m2{ C, S, T }=0.1451
m3{ C }=0.3405, m3{ S }=0.3602, m3{ C, S, T }=0.2993
m4{ T }=0.3731, m4{ C }=0.3531, m4{ C, S, T }=0.2738
m5{ T }=0.3591, m5{ C }=0.4025, m5{ C, S, T }=0.2384
m6{ T }=0.3667, m6{ C }=0.3566, m6{ C, S, T }=0.2767
m7{ T }=0.3375, m7{ C }=0.3674, m7{ C, S, T }=0.2952
m8{ T }=0.3935, m8{ C }=0.4062, m8{ C, S, T }=0.2003
m9{ T }=0.3585, m9{ C }=0.4092, m9{ C, S, T }=0.2323
m10{ T }=0.3624, m10{ C }=0.3746, m10{ C, S, T }=0.2630
m11{ T }=0.4116, m11{ C }=0.3667, m11{ C, S, T }=0.2217
m12{ T }=0.5951, m12{ C, T }=0.4049
m13{ T }=0.4866, m13{ C }=0.2940, m13{ C, S, T }=0.2194
m14{ T }=0.5612, m14{ C }=0.2935, m14{ C, S, T }=0.1453
m15{ S }=0.3336, m15{ C }=0.3364, m15{ C, S, T }=0.3300
m16{ T }=0.3071, m16{ C }=0.0411, m16{ C, S, T }=0.2818
m17{ T }=0.5552, m17{ C }=0.5552, m17{ C, S, T }=0.4448
m18{ S }=0.3644, m18{ C }=0.3712, m18{ C, S, T }=0.2373
m19{ T }=0.3743, m19{ C }=0.3290, m19{ C, S, T }=0.2967
m20{ T }=0.3699, m20{ S }=0.3562, m20{ C, S, T }=0.2739
m21{ T }=0.3938, m21{ S }=0.3574, m21{ C, S, T }=0.2488
m22{ T }=0.3486, m22{ S }=0.3841, m22{ C, S, T }=0.2673
m23{ T }=0.4085, m23{ S }=0.3066, m23{ C, S, T }=0.2849
m24{ T }=0.3777, m24{ S }=0.3372, m24{ C, S, T }=0.2851
m25{ C }=0.3650, m25{ S }=0.3581, m25{ C, S, T }=0.2769
2) the Basic probability assignment function m for generating 25 wave bandsiIt merges to obtain m using average fusion method, it is described flat
Equal fusion method is：Whereinmi(i=1,2 ..., 25) it is the base generated in step 2
This probability distribution function；
Fusion results are：M { T }=0.3210, m { C }=0.2753, m { S }=0.1446, m { C, T }=0.0162,
M { C, S, T }=0.2429
3) m after fusion is converted into probability point using Pignistic probability transformation methods
Cloth P, the conversion method are：Wherein
The result that m is converted to probability distribution P is
P { T }=0.4104, P { S }=0.2255, P { C }=0.3644
4) classify to pixel p according to obtained probability distribution P, take P ({ C }), maximum probability in P ({ S }), P ({ T })
Corresponding classification as pixel p classify as a result, in classification results C indicate cloud, S indicate sky, T indicate target；
According to probability distribution P, maximum probability classification is T, therefore pixel p is classified as target.
Step 4：Region to being classified as unknown object carries out interference elimination, to choose true movement unknown object, does
The principle for disturbing exclusion is realized using the position correlation of consecutive frame zone of ignorance：
If accidentally knowing caused by random disturbances, then the change in location of consecutive frame zone of ignorance is larger, sets rational threshold value
Random disturbances can be excluded, threshold value can be chosen according to target speed and (such as be selected as 1.5 times of target single frames displacements)；If static
Target, after 5-10 frames its total displacement can equally be excluded close to 0；
Step 5：According to the pixel classification results of present frame update previous frame target, the Triangular Fuzzy Number mould of cloud, sky
Type, using updated model as next frame image cloud, sky, the other Fuzzy Math Model of three type of target, the model modification
Method is：
For all pixels for being identified as cloud, the maximum value max and minimum value min of these pixel wave bands i are found out,
If max>Cmaxi, willThe upper bound is updated to max, if min<Cmini, willLower bound is updated to min；It is identified as day for all
Empty pixel, finds out the maximum value max and minimum value min of these pixel wave bands i, if max>Smaxi, willThe upper bound updates
For max, if min<Smini, willLower bound is updated to min；For all pixels for being identified as target, these pixels are found out
The maximum value max and minimum value min of point wave band i, if max>Tmaxi, willThe upper bound is updated to max, if min<Tmini, willUnder
Boundary is updated to min.
By taking 1 model modification of wave band as an example：For all pixels for being identified as cloud, these pixel wave bands 1 are found out
Maximum value is 146, minimum value 97,Upper bound lower bound does not update；For all pixels for being identified as sky, this is found out
The maximum value of a little pixel wave bands 1 is 119, minimum value 99, thereforeUpper bound lower bound does not update；It is identified as all
The pixel of target, the maximum value for finding out these pixel wave bands 1 are 125, minimum value 101,101<113, therefore willUnder
Boundary is updated to 101,The upper bound does not update.
Claims (1)
1. a kind of multispectral image Dim target tracking method based on DS evidence theories, it is characterised in that include the following steps：
Step 1：Input each wave band cloud (C) under a pattern sky multispectral image and current environment, sky (S) and target (T) ash
Minimum value, intermediate value and maximum value are spent, according to the minimum gray value, intermediate value, maximum of the imaging of the cloud, sky and current goal of input
Value, establishes corresponding Triangular Fuzzy Number model, and framework of identification is Θ={ C, S, T }, and C indicates that cloud, S indicate day in framework of identification
Sky, T indicate that target, the method for C, S, T Triangular Fuzzy Number model foundations are：
The minimum gray value Cmin that wave band i clouds are imagedi, intermediate value CaveiAnd maximum value CmaxiRespectively as wave band i cloud Triangle Modules
Paste exponential model minimum value, intermediate value, maximum value, then the Triangular Fuzzy Number of wave band i clouds beBy wave
The minimum gray value Smin of section i skies imagingi, intermediate value SaveiAnd maximum value SmaxiRespectively as wave band i sky Triangular Fuzzy Numbers
The minimum value of model, intermediate value, maximum value, the then Triangular Fuzzy Number that wave band i skies are established are
By the minimum gray value Tmin of wave band i target imagingsi, intermediate value TaveiAnd maximum of T maxiWork as respectively as wave band i
The minimum value of preceding target Triangular Fuzzy Number model, intermediate value, maximum value, the then Triangular Fuzzy Number that wave band i current goals are established are
Step 2：The upper bound of fuzzy number is increased, lower bound reduction, remembers cloud, day respectively by the fuzzy number broadening that step 1 is obtained
Fuzzy number empty, after current goal broadening isThe method for widening is：
Step 3：Classify to any pixel point p in input picture, classification results may be cloud, sky, target：
1) to any classified pixels point p, its wave band i gray values G is takeni, use GiWithGenerate basic probability assignment
Function mi, describedThe Triangular Fuzzy Number of respectively aforementioned wave band i clouds, sky, target, the elementary probability
Partition function is defined as belonging to any one subset A, m (A) ∈ [0,1] of Θ in evidence theory, and meetsThen m is 2ΘOn Basic probability assignment function, wherein 2ΘFor the power set of framework of identification,The Basic probability assignment function miGeneration method
For：By GiWith fuzzy numberThe high point of intersection point is assigned to the reliability of corresponding single subset elements, by GiWith fuzzy numberThe minimum point of intersection point is assigned to the reliability of corresponding double subset elements or more subsets, wherein the list element of set
Element refers to that the subset { C } of framework of identification Θ in step 1, { S } or { T }, double subset elements refer to distinguishing in step 1
Know the subset { C, S } of frame Θ, { C, T } or { S, T }, more subset elements refer to the subset of framework of identification Θ in step 1
{C,S,T}；Remember that the sum of reliability generated is Sum, normalizes the reliability of generation to obtain mi；
2) the Basic probability assignment function m for generating 25 wave bandsiIt merges to obtain m, the average fusion using average fusion method
Method is：WhereinFor the elementary probability generated in step 2
Partition function；
3) m after fusion is converted into probability distribution P using Pignistic probability transformation methods,
The conversion method is：Wherein
4) classify to pixel p according to obtained probability distribution P, take P ({ C }), maximum probability corresponds in P ({ S }), P ({ T })
Classification as pixel p classify as a result, in classification results C indicate cloud, S indicate sky, T indicate target；
Step 4：Interference elimination is carried out to accidentally knowing point, to choose true tracking target, the principle of interference elimination is using adjacent
The position correlation of frame target area is realized：
In all target areas of present frame in selected distance in frame image the nearest region in target location as target to be selected, if
Distance is in rational threshold interval, then it is assumed that target to be selected is the target currently tracked, otherwise it is assumed that being random disturbances, threshold value
Section can be chosen according to target speed (such as can be selected as threshold interval 0.5 times of target single frames displacement~1.5 times target list
Framing bit moves)；
Step 5：According to the pixel classification results of present frame update previous frame target, cloud, sky Triangular Fuzzy Number model,
Using updated model as next frame image cloud, sky, the other Fuzzy Math Model of three type of target, the model update method
For：
For all pixels for being identified as cloud, the maximum value max and minimum value min of these pixel wave bands i are found out, if
max>Cmaxi, willThe upper bound is updated to max, if min<Cmini, willLower bound is updated to min；It is identified as sky for all
Pixel, the maximum value max and minimum value min of these pixel wave bands i are found out, if max>Smaxi, willThe upper bound is updated to
Max, if min<Smini, willLower bound is updated to min；For all pixels for being identified as target, these pixels are found out
The maximum value max and minimum value min of wave band i, if max>Tmaxi, willThe upper bound is updated to max, if min<Tmini, willLower bound
It is updated to min.
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