CN109102520A - The moving target detecting method combined based on fuzzy means clustering with Kalman filter tracking - Google Patents

The moving target detecting method combined based on fuzzy means clustering with Kalman filter tracking Download PDF

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CN109102520A
CN109102520A CN201810549503.2A CN201810549503A CN109102520A CN 109102520 A CN109102520 A CN 109102520A CN 201810549503 A CN201810549503 A CN 201810549503A CN 109102520 A CN109102520 A CN 109102520A
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fuzzy
color difference
block
kalman filter
means clustering
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熊炜
贾锈闳
熊子婕
童磊
金靖熠
冯川
王传胜
管来福
刘敏
王娟
刘聪
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Hubei University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/277—Analysis of motion involving stochastic approaches, e.g. using Kalman filters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • 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

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  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
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Abstract

The present invention discloses a kind of moving target detecting method combined based on fuzzy means clustering with Kalman filter tracking, the main thought of this method is: first calculating the color difference histogram (CDH) of pixel local neighborhood, then fuzzy color difference histogram is obtained using Fuzzy c-means Clustering (FCM), and then background modeling is carried out, and similitude matching detection prospect is used, target tracking is further carried out using the Kalman filter based on Block- matching.The background subtraction mechanism based on fuzzy color difference histogram FCDH proposed replaces Pixel-level using patch grade method, and this method is focused on color difference, rather than in color size.By reducing the quantity of false error, FCDH can show excellent performance.

Description

The moving object detection combined based on fuzzy means clustering with Kalman filter tracking Method
Technical field
The invention belongs to multiple technical fields such as information science, computer vision, machine learning, pattern-recognition, especially It is related to a kind of moving target detecting method combined based on fuzzy means clustering with Kalman filter tracking.
Background technique
Currently, monitoring video information is automatically processed with prediction in information science, computer vision, machine learning, mode It is greatly paid close attention in the multiple fields such as identification.And the foreground target information in monitor video how effectively, is quickly extracted, It is wherein extremely important and basic problem.This technology has been widely used in video object tracking, and urban transportation detection is long When scene monitor, video actions capture, video compress etc. application in.
Traditional moving target detecting method includes frame difference method, background subtraction method and optical flow method.Frame difference method is continuous The adjacent interframe of two or three in image sequence is extracted in image using time difference pixel-based and thresholding Moving target.The advantages of frame difference method is that algorithm is simple, and speed is fast, is easy to hardware realization, and it is big can to well adapt to environmental change The case where, but for moving slow target in image sequence, it is difficult to it effectively detects complete prospect, is easy inside movement entity Generate larger " cavity ".Optical flow method is by establishing target motion vectors field, with change of the pixel in image sequence in time-domain Change and consecutive frame between correlation find previous frame with corresponding relationship existing between present frame, to calculate adjacent A kind of method of the motion information of object between frame.Optical flow method can also examine moving target in movement background environment It surveys, but its is maximum the disadvantage is that calculation amount is excessive, it is difficult to accomplish real-time detection.
When carrying out moving object detection, not only target itself, but also be affected by other factors, such as change Illumination, dynamic background, crowded scene and block.In the case where video camera is unstable, moving object segmentation problem becomes It is more challenging because background be no longer it is static, background subtraction method can no longer be used.
Summary of the invention
In order to overcome the shortcomings of the prior art described above, a kind of poly- based on fuzzy mean it is an object of the invention to propose The moving target detecting method that class is combined with Kalman filter tracking, the background based on fuzzy color difference histogram FCDH of proposition Subduction mechanism replaces Pixel-level using patch grade method, and this method is focused on color difference, rather than in color size. By reducing the quantity of false error, FCDH can show excellent performance.
In order to achieve the above object, the technical scheme adopted by the invention is that: filtered based on fuzzy means clustering and Kalman Wave tracks the moving target detecting method combined, which is characterized in that described method includes following steps:
Step 1, the color difference histogram CDH of pixel local neighborhood is calculated；
Step 2, fuzzy color difference histogram is obtained using Fuzzy c-means Clustering FCM；
Step 3, background modeling is carried out, and uses similitude matching detection prospect；
Step 4, target tracking is carried out using the Kalman filter based on Block- matching.
Further, step 1 calculates the color difference of the local neighborhood of pixel using the color difference for corresponding to two color components Histogram CDH, specifically includes；
1.1 calculating values of chromatism: all frames in video-frequency band are read in and are saved, are denoted as I respectively1, I2..., Im∈Nm×n, Wherein, M is the totalframes in video-frequency band, and m, n are the size of every frame picture；There to be the cromogram of strength grade I (p, q, ch) As being quantified as W grade, I ∈ 0,1,2 ... W-1 }；(p, q) is position coordinates, and ch is Color Channel, and RGB image is converted For CIE L*a*b* color model；
The color difference d in small part N × N neighborhood centered on position (p, q) is sought, is calculated using following formula:
Wherein, I (r, s, ch) indicates the intensity of pixel in neighborhood；
1.2 color difference histograms: it will be blurred from (1) calculated color difference d using Gauss member function are as follows:
σ indicates standard deviation；
Color difference histogram is finally given by the following formula:
Wherein, A × A is the regional area centered on the position (p, q), and the frequency of pixel is calculated with k.
Further, step 2 obtains fuzzy color difference histogram using Fuzzy c-means Clustering FCM, specifically includes:
FCM is by K-bin local histogram H={ h1, h2, h3..., hkBe classified as with liC cluster centered on position, often A cluster distributes bin based on fuzzy membership, this is completed by minimizing the iteration of cost function；
Fuzzy color difference histogram uses subordinated-degree matrix u (c × K dimension) and CDH vector H (K × 1) building c Victoria C DH (FCDH) Fuzzy vector v, as follows:
hc×1=uc×KHK×1 (5)。
Further, step 3 carries out background modeling, and uses similitude matching detection prospect, specifically includes；
3.1 background modelings: similarity measurement p is between modeling background frame F and present frame h using histogram measurement The intersection of FCDH；
Wherein, c is the quantity for providing histogram case, and background B is made comparisons by same threshold value and obtained:
3.2 context updates: background FCDH uses [0,1] to update each pixel as Background learning rate, and t is time index, It is as follows:
Further, step 4 carries out target tracking using the Kalman filter based on Block- matching, specifically includes:
4.1 can be changed block size (BVBS) BM from bottom to top: just starting to select small size block to detect more moving mass, Then block size doubles in each dimension, a series of pieces of { Bi, i=1,2,3,4 } } in smaller size, they are shared Identical larger size father block Sb, it is defined as follows:
Sb=B1∪B2∪B3∪B4, where BI, 1≤1≤4
1,2,3 and 4 father's block S is corresponded respectively tobUpper left, upper right, lower-left, bottom right sub-block；
B in step previousiDetecting the BM process with small moving mass can be repeated as desired for repeatedly, or until block Until size reaches scheduled full-size, in this way, more false moving mass can be abandoned with biggish block size, keep simultaneously The real motion details of smaller piece size；
4.2 times updated: known state xk-1, start iteration to NextState xkIt is predicted；
Wherein,It is prior estimate error covariance, because the doing exercises for object block in X and Y coordinates is complete phase Mutually independent, Kalman filtering can apply X, Y coordinates respectively, and in each coordinate, the fast movement of a target can be to Lower formula indicates:
xk,Δ k respectively indicates the displacement at k moment, speed, acceleration and the time difference, because not knowing control item, B in equationkukIt is 0, the specific equation of the motion tracking of real-time update is as follows:
The determination of process noise covariance is generally relatively difficult, because the estimation procedure directly observed is not always feasible , it, can be using acceleration as piecewise constant Wiener process in this work, i.e. acceleration continues in each period It is remained unchanged in time, but different for each period, and is each zero-mean gaussian sequence,
Q is that the energy spectral density density of continuous time white noise enables P to reduce calculation amount0=cQk, c is one normal Number；
4.3: measurement updaue: state xkZkIt can be indicated with a linear equation:
zk=Hkxk+vk (13)
zkIt is the measured value of m × 1 at k moment, HkIt is and zkThe observing matrix of relevant m × n, vkIt is the additional survey of m × 1 Noise vector is measured, the equation of specific measurement updaue is as follows:
Formula n × m matrix K is so that the smallest gain of posteriori error covariance.
Compared with prior art, the beneficial effects of the present invention are: the invention proposes a kind of fuzzy means clustering and karrs The moving target detecting method that graceful filter tracking combines, compared with the conventional method, remarkable advantage is:
A) the color difference histogram calculation of local neighborhood can handle due to dynamic background (such as tap water, brandish trees) and The problem.
B) fuzzy color difference histogram is obtained using Fuzzy c-means Clustering FCM, overcome due to illumination variation (suddenly or Gradual change) and the influence of generation.
C) method of the Block- matching of variable block size from bottom to top more accurately determines possible object block, then adaptive It answers Kalman filter for distinguishing the presence of significant movement and other distractive movements, improves correct detection target Reliability detection and meanwhile reduce the presence of noise.
Detailed description of the invention
Fig. 1 is the flow chart of moving target detecting method of the invention.
Specific embodiment
For the ease of those of ordinary skill in the art understand and implement the present invention, below with reference to embodiment to the present invention make into The detailed description of one step, it should be understood that implementation example described herein is merely to illustrate and explain the present invention, and is not used to limit The fixed present invention.
The present invention provides a kind of moving target detecting method combined based on color characteristic with Kalman filter tracking.It is first First, can be regarded for video processing problem as the problem of processing to each frame picture, and grayscale image can regard one as Matrix xi∈Rw×h, wherein w is the width of image, and h is the height of image, and each element is some integer between 0~255 in matrix Value, then can be converted into the processing of video to set of matrices X=x1, x2, x3..., xtProcessing, wherein t indicate view The frame number of frequency.
The main thought of this method is: first calculating the color difference histogram (CDH) of pixel local neighborhood, then uses fuzzy c Mean cluster (FCM) obtains fuzzy color difference histogram, and then carries out background modeling, and using before similitude matching detection Scape further carries out target tracking using the Kalman filter based on Block- matching.
Algorithm flow chart of the invention is as shown in Figure 1, the specific steps are as follows:
Step 1: the color difference histogram of the local neighborhood of pixel is calculated using the color difference for corresponding to two color components CDH。
1.1 calculating values of chromatism: all frames in video-frequency band are read in and are saved, are denoted as I respectively1, I2..., Im∈Nm×n, Wherein M is the totalframes in video-frequency band, and m, n are the size of every frame picture；There to be the color image of strength grade I (p, q, ch) It is quantified as W grade, such as I ∈ 0,1,2 ... W-1 }；(p, q) is position coordinates, and ch is Color Channel.RGB image is turned It is changed to CIE L*a*b* color model.
The color difference d in small part N × N neighborhood centered on position (p, q) is sought, is calculated using following formula:
Wherein I (r, s, ch) indicates the intensity of pixel in neighborhood.
1.2 color difference histograms: it will be blurred from (1) calculated color difference d using Gauss member function are as follows:
σ indicates standard deviation.
Color difference histogram is finally given by the following formula:
Wherein A × A is the regional area centered on the position (p, q), and the frequency of pixel is calculated with k.
Step 2: Fuzzy c-means Clustering
FCM is by K-bin local histogram H={ h1, h2, h3..., hkBe classified as with liC cluster centered on position, often A cluster distributes bin based on fuzzy membership, this is completed by minimizing the iteration of cost function.
Fuzzy color difference histogram uses subordinated-degree matrix u (c × K dimension) and CDH vector H (K × 1) building c Victoria C DH (FCDH) Fuzzy vector v, as follows:
hc×1=uc×KHK×1 (5)
Step 3: background subtraction method
3.1 background modelings: similarity measurement p is between modeling background frame F and present frame h using histogram measurement The intersection of FCDH.
Wherein c is the quantity for providing histogram case.Background B is made comparisons by same threshold value and is obtained:
3.2 context updates: background FCDH uses [0,1] to update each pixel as Background learning rate, and t is time index, It is as follows:
Step 4: the target tracking of the Kalman filter based on Block- matching
4.1 can be changed block size (BVBS) BM from bottom to top: just start that small size block is selected more to do more physical exercises to detect Block,.Then block size doubles in each dimension.A series of pieces of { Bi, i=1,2,3,4 } } in smaller size, they Share identical larger size father block Sb, it is defined as follows:
Sb=B1∪B2∪B3∪B4, where BI, l≤l≤4
1,2,3 and 4 father's block S is corresponded respectively tobUpper left, upper right, lower-left, bottom right sub-block.
B in step previousiDetecting the BM process with small moving mass can be repeated as desired for repeatedly, or until block Until size reaches scheduled full-size.In this way, more false moving mass can be abandoned with biggish block size, keep simultaneously The real motion details of smaller piece size.
4.2 times updated: known state xk-1, start iteration to NextState xkIt is predicted.
WhereinIt is prior estimate error covariance.Because the doing exercises for object block in X and Y coordinates is completely mutually Independent, Kalman filtering can apply X, Y coordinates respectively.In each coordinate, the fast movement of a target can be with following Formula indicates:
xk,ΔkRespectively indicate the displacement at k moment, speed, acceleration and the time difference.Because we do not know control , the B in equationkukIt is 0.The specific equation of the motion tracking of real-time update is as follows:
The determination of process noise covariance is generally relatively difficult, because the estimation procedure directly observed is not always feasible 's.In this work, using acceleration as piecewise constant Wiener process, i.e. acceleration continues in each period for we It is remained unchanged in time, but different for each period, and is each zero-mean gaussian sequence.
Q is that the energy spectral density density of continuous time white noise enables P to reduce calculation amount0=cQk, c is one normal Number.
4.3: measurement updaue: state xkZkIt can be indicated with a linear equation:
zk=Hkxk+vk (13)
zkIt is the measured value of m × 1 at k moment, HkIt is and zkThe observing matrix of relevant m × n, vkIt is the additional survey of m × 1 Measure noise vector.The equation of specific measurement updaue is as follows:
Formula n × m matrix K is so that the smallest gain of posteriori error covariance.
In this work, measured value zkIt is most likely to be the target object block coordinate in current compensation frame.Therefore x, Y, observing matrix H are in [100] in each direction definition.The position of object block is by three steps (TSS) BM algorithm measurement.Measurement is made an uproar Sound vkIndicate the noise characteristic of sensor.Because only from the coordinate of the angle measurement target object of Block- matching, covariance matrix R exists vkIn byScalar valueIt is that experience determines.
It should be understood that the part that this specification does not elaborate belongs to the prior art.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this The limitation of invention patent protection range, those skilled in the art under the inspiration of the present invention, are not departing from power of the present invention Benefit requires to make replacement or deformation under protected ambit, fall within the scope of protection of the present invention, this hair It is bright range is claimed to be determined by the appended claims.

Claims (5)

1. the moving target detecting method combined based on fuzzy means clustering with Kalman filter tracking, which is characterized in that institute The method of stating includes the following steps:
Step 1, the color difference histogram CDH of pixel local neighborhood is calculated；
Step 2, fuzzy color difference histogram is obtained using Fuzzy c-means Clustering FCM；
Step 3, background modeling is carried out, and uses similitude matching detection prospect；
Step 4, target tracking is carried out using the Kalman filter based on Block- matching.
2. the moving object detection according to claim 1 combined based on fuzzy means clustering with Kalman filter tracking Method, which is characterized in that step 1 is straight come the color difference for calculating the local neighborhood of pixel using the color difference for corresponding to two color components Side figure CDH, specifically includes；
1.1 calculating values of chromatism: all frames in video-frequency band are read in and are saved, are denoted as I respectively1, I2..., Im∈Nm×n, In, M is the totalframes in video-frequency band, and m, n are the size of every frame picture；There to be the color image of strength grade I (p, q, ch) It is quantified as W grade, I ∈ 0,1,2 ... W-1 }；(p, q) is position coordinates, and ch is Color Channel, and RGB image is converted to CIE L*a*b* color model；
The color difference d in small part N × N neighborhood centered on position (p, q) is sought, is calculated using following formula:
Wherein, I (r, s, ch) indicates the intensity of pixel in neighborhood；
1.2 color difference histograms: it will be blurred from (1) calculated color difference d using Gauss member function are as follows:
σ indicates standard deviation；
Color difference histogram is finally given by the following formula:
Wherein, A × A is the regional area centered on the position (p, q), and the frequency of pixel is calculated with k.
3. the moving object detection according to claim 2 combined based on fuzzy means clustering with Kalman filter tracking Method, which is characterized in that step 2 obtains fuzzy color difference histogram using Fuzzy c-means Clustering FCM, specifically includes:
FCM is by K-bin local histogram H={ h1, h2, h3..., hkBe classified as with liC cluster centered on position, Mei Geju Class is based on fuzzy membership and distributes bin, this is completed by minimizing the iteration of cost function；
Fuzzy color difference histogram is fuzzy using subordinated-degree matrix u (c × K dimension) and CDH vector H (K × 1) building c Victoria C DH (FCDH) Vector v is as follows:
hc×1=uc×KHK×1 (5)。
4. the moving object detection according to claim 3 combined based on fuzzy means clustering with Kalman filter tracking Method, which is characterized in that step 3 carries out background modeling, and uses similitude matching detection prospect, specifically includes；
3.1 background modelings: similarity measurement p is FCDH between modeling background frame F and present frame h using histogram measurement Intersection；
Wherein, c is the quantity for providing histogram case, and background B is made comparisons by same threshold value and obtained:
3.2 context updates: background FCDH uses [0,1] to update each pixel as Background learning rate, and t is time index, as follows It is shown:
5. the moving object detection according to claim 4 combined based on fuzzy means clustering with Kalman filter tracking Method, which is characterized in that step 4 carries out target tracking using the Kalman filter based on Block- matching, specifically includes:
4.1 can be changed block size (BVBS) BM from bottom to top: just start to select small size block to detect more moving mass, so Block size doubles in each dimension afterwards, a series of pieces of { Bi, i=1,2,3,4 } } in smaller size, their shared phases Same larger size father's block Sb, it is defined as follows:
Sb=B1∪B2∪B3∪B4, where BI, 1≤1≤4
1,2,3 and 4 father's block S is corresponded respectively tobUpper left, upper right, lower-left, bottom right sub-block；
B in step previousiDetecting the BM process with small moving mass can be repeated as desired for repeatedly, or until block size Until reaching scheduled full-size, in this way, more false moving mass can be abandoned with biggish block size, while keeping smaller The real motion details of block size；
4.2 times updated: known state xk-1, start iteration to NextState xkIt is predicted；
Wherein,It is prior estimate error covariance, because the doing exercises for object block in X and Y coordinates is completely mutually solely Vertical, Kalman filtering can apply X, Y coordinates respectively, and in each coordinate, the fast movement of a target can use following public affairs Formula indicates:
xk,Δ k respectively indicates the displacement at k moment, speed, acceleration and the time difference, because not knowing control item, equation In BkukIt is 0, the specific equation of the motion tracking of real-time update is as follows:
The determination of process noise covariance is generally relatively difficult because the estimation procedure directly observed be not always it is feasible, It, can be using acceleration as piecewise constant Wiener process, i.e. duration of the acceleration in each period in this work It inside remains unchanged, but different for each period, and is each zero-mean gaussian sequence,
Q is that the energy spectral density density of continuous time white noise enables P to reduce calculation amount0=cQk, c is a constant；
4.3: measurement updaue: state xkZkIt can be indicated with a linear equation:
zk=Hkxk+vk (13)
zkIt is the measured value of m × 1 at k moment, HkIt is and zkThe observing matrix of relevant m × n, vkIt is the additional measurement noise of m × 1 The equation of vector, specific measurement updaue is as follows:
Formula n × m matrix K is so that the smallest gain of posteriori error covariance.
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