CN106485734B - A kind of video moving object detection method based on non local self-similarity - Google Patents
A kind of video moving object detection method based on non local self-similarity Download PDFInfo
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Abstract
The present invention relates to a kind of video moving object detection method based on non local self-similarity, this method improves batch type video moving object detection algorithm DECOLOR, and steps are as follows: for every frame image in video sequence, being divided into image block；Calculate the non local self-similarity value of each image block；Obtain non local self-similarity matrix S；Prospect matrix F is constrained with the non local self-similarity matrix Q of vectorization, obtains non local self-similarity bound term；Non local self-similarity bound term is added to the objective function of DECOLOR, obtains new objective function；For new objective function, unconstrained minimization problem is solved；Each frame image vectorization of video sequence to be processed is formed into input matrix O；It is iterated calculating, acquires new prospect matrix F.The present invention has calculating speed fast, the good advantage of effect.
Description
Technical field
The present invention relates to computer vision, efficient, quick video moving object detection method in the fields such as pattern-recognition,
More particularly to the video moving object detection method using non local self-similarity.
Background technique
Video automatic analysis technology has important role in video monitoring, augmented reality, auto navigation etc..Video is certainly
Dynamic analytical technology [1] includes three steps: moving object detection, motion target tracking and moving target behavioural analysis.As view
The basis of frequency automatic analysis technology, moving object detection refers to extracts moving target in real time from video, is subsequent place
It manages (such as: target classification) and priori conditions is provided.Therefore, it is most important that how real-time and accurate detection, which goes out moving target,
's.However in existing video moving object detection technique, since subject shake (such as water surface, leaf etc.) is right in video background
Video moving object detection affects.This patent is intended to study how to improve the complete of video moving object testing result
Whole property and accuracy rate.
Currently, video moving object detection algorithm specifically includes that frame differential method, optical flow method and is based on background subtraction
The method that matrix indicates.
Frame differential method is also known as time differencing method, mainly utilizes in video sequence between the several adjacent image frames in front and back
Difference extracts moving object region in image.This method is simple, has stronger adaptivity and meets requirement of real-time, still
There is the case where changing (such as: the leaf of shaking) for background, algorithm robustness is poor.
Optical flow method is based drive method.The optical flow characteristic changed over time using moving object carries out Objective extraction
And tracking.The motion profile of each pixel is usually first calculated, motion segmentation is carried out, to carry out point of moving target and background
From.T.Brox [2] et al. proposes a kind of moving object detection algorithm based on pixel motion profile.This method analyzes each picture
The motion profile of vegetarian refreshments is simultaneously split it.Optical flow method can detect independent under conditions of not knowing scene information in advance
Moving object, but optical flow method computation complexity is higher, takes a long time, it is difficult to meet requirement of real-time, and detection accuracy is not high.
Background subtraction, that is, background subtraction method.Since there are difference, backgrounds in gray scale and color for moving region and background
Subduction algorithm by the way that present image is done subtraction with the background model that pre-establishes, as a result in each pixel point value and in advance
The threshold value of setting compares, and if more than threshold value, then the point is foreground point, otherwise is background dot.But this method usually requires that
Preceding several width images of video sequence do not include motion target area, could train background model well.T.Haines [3] etc.
It is proposed a kind of gauss hybrid models algorithm based on Di Li Cray process, this method is with Di Li Cray gauss hybrid models to background
Then modeling realizes moving object detection with background subtraction.In practice, it is difficult to obtain the picture frame for being entirely free of moving target
Background model is established, if moving suddenly in background there are the stationary object in moving object or background will cause empty inspection to be asked
Topic, thus detection accuracy is affected.
Moving object detection algorithm based on matrix decomposition can be divided into batch type moving object detection algorithm and increment type fortune
Moving-target detection algorithm.For the video sequence of Still Camera shooting, this method is typically based on two hypothesis: transporting in video
Dynamic target area is smaller for background area, to assume that prospect matrix is sparse；And the static shooting of video camera
Background is constant in obtained video sequence, so the background correlation of each frame image is very big, to assume that background matrix is
Low-rank.Based on the two it is assumed that the video sequence matrix decomposition of input at sparse and low-rank matrix.Batch type detection algorithm
Batch type processing is carried out to several frames of input video, and increment type detection algorithm is able to achieve real-time processing.
RPCA [4] algorithm is a classical batch type moving object detection algorithm.The algorithm is by video sequence matrix table
It is shown as background low-rank matrix and prospect sparse matrix, it is constrained with nuclear norm and 1 norm respectively.Constitute a constraint
Optimization problem.But the algorithm does not account for the influence of noise.DECOLOR [5] algorithm on the basis of RPCA, considers simultaneously
The influence of neighbor pixel and noise.RFDSA algorithm [6] is that a kind of detect from the video moving object of probability angle is calculated
Method, the algorithm set the base vector of background matrix, while considering flatness of the moving target in time domain and airspace.2015
Year, Bo Xin [7] et al. proposes a kind of new batch type moving object segmentation algorithm based on matrix decomposition, and the algorithm is not with having
The linearity of moving object indicates background matrix, is constrained with drag-line method prospect matrix.
GRASTA [8] algorithm is a kind of increment type moving object segmentation algorithm of classics.The algorithm by background subspace
Basal orientation moment matrix considers the sparsity of prospect at background vector.But the algorithm does not account for the shadow of noise in video
It rings, and for there is the case where changing background, robustness is poor.ISC algorithm [9] considers the influence of neighbor pixel, in target
Smooth item is added in function.MODSM algorithm [10] is detected with conspicuousness to background matrix on the basis of being based on matrix decomposition
It is constrained, constitutes conspicuousness and detect bound term.
Method based on matrix decomposition can preferably realize real-time and accurate detection moving object, be capable of handling illumination
Influence with noise to testing result becomes the main stream approach of current video moving object segmentation.The existing video of comprehensive analysis
Railway Project existing for moving object segmentation algorithm: it is by background (such as the leaf shaken, the ripple etc. of the water surface) erroneous detection of movement
Prospect；The moving target detected is imperfect, there is hollow out phenomenon.
Bibliography:
[1]A.Yilmaz,O.Javed,and M.Shah,“Object tracking:A survey,”ACM
Transaction on Computing Surveys,vol.38,no.4,pp.1-45,2006.
[2]T.Brox,and J.Malik,“Object segmentation by long term analysis
ofpoint trajectories,”European Conference on Computer Vision,2010.
[3]T.Haines,and Tao Xiang,“Background Subtraction with Dirichlet
Process Mixture Models,”IEEE Transaction on Pattern Analysis and Machine
Intelligence,vol.36,no.4,2014.
[4]E.Candes,X.Li,Y.Ma,and J.Wright,“Robust Principal Component
Analysis? " Journal of the ACM, 2011.
[5]X.Zhou,C.Yang,and W.Yu,“Moving object detection by detecting
contiguous outliers in the low-rank representation,”IEEE Transactions on
Pattern Analysis and Machine Intelligence,vol.35,no.3,pp.597-610,2013.
[6]X.Guo,X.Wang,L.Yang,X.Cao,and Y.Ma,“Robust foreground Detection
Using Smoothness and Arbitrariness Constraints,”European Conference on
Computer Vision,2014.
[7]B.Xin,Y.Tian,Y.Wang,and W.Gao,“Background Subtraction via
Generalized Fused Lasso Foreground Modeling,”Proc.IEEE International
Conference on Computer Vision and Pattern Recognition,2015.
[8]J.He,L.Balzano,and A.Szlam,“Incremental Gradient on the
Grassmannian for Online Foreground and Background Separation in Subsampled
Video,”IEEE International Conference on Computer Vision and Pattern
Recognition,2012.
[9]J.Pan,X.Li,X.Li,and Y.Pang,“Incrementally detecting moving objects
in video with sparsity and connectivity,”Cognitive Computation,vol.8,no.4,
pp.420-428,2016.
[10]Y.Pang,L.Ye,X.Li,and J.Pan,“Moving Object Detection in Video
Using Saliency Map and Subspace Learning,”CoRR,abs/1509.09089,2015.
Summary of the invention
The purpose of the present invention is erroneous detections and part as caused by the background of variation in detecting for reduction video moving object
Missing inspection problem improves existing batch type video moving object detection algorithm DECOLOR, provides a kind of non local self-similarity
Video moving object detection method.The present invention is on the basis of matrix indicates, by adding non local self-similarity bound term, from
And improve erroneous detection and missing inspection problem.In addition, for increasing conspicuousness detection bound term algorithm, non local self-similarity meter
It calculates simply, it is high-efficient.Technical scheme is as follows:
A kind of video moving object detection method based on non local self-similarity, this method is to batch type video motion object
Physical examination method of determining and calculating DECOLOR is improved, and steps are as follows:
Step 1: for every frame image in video sequence, dividing the image into the image that size is p according to certain step-length η
Block；Each image block and remaining image block calculate similarity；N the smallest similarities are overlapped before taking, as this
The non local self-similarity value of image block；The smallest non local self-similarity value conduct in all image blocks where capture vegetarian refreshments
The value of non local self-similarity matrix corresponding position obtains non local self-similarity matrix S；
Step 2: the non local self-similarity matrix S vectorization of all images is constituted into matrix Q；
Step 3: prospect matrix F being constrained with the non local self-similarity matrix Q of vectorization, is obtained non local from phase
Like property bound term；
Step 4: non local self-similarity bound term is added to the mesh of batch type video moving object detection algorithm DECOLOR
Scalar functions obtain new objective function；
Step 5: for new objective function, solving unconstrained minimization problem；
Step 6: each frame image vectorization of video sequence to be processed is formed into input matrix O；
Step 7: carrying out parameter initialization, and set the number of iterations, calculating is iterated, for prospect matrix F and background
Matrix B is successively solved, until result restrains；
Step 8: output prospect matrix F, foreground area as detected.
Using the method for the invention, on the basis of the video moving object detection algorithm based on matrix decomposition, increase
Non local self-similarity bound term.Improve the accuracy rate and efficiency of moving object segmentation.Firstly, relative to traditional batch type
Video moving object detection algorithm is effectively improved due to the erroneous detection that background variation causes and the expansion of foreground area profile generates
Problem, while the problem of improve missing inspection.And for the video moving object detection algorithm detected based on conspicuousness, due to
Non local self-similarity matrix calculates simply, is not related to super-pixel calculating, time-consuming short, high-efficient.
Detailed description of the invention
Fig. 1 takes 8 × 8 block of pixels with interval steps for 4.Heavy black line is 8 × 8 block of pixels.
Fig. 2 is the mentioned method block diagram of the present invention.
Specific embodiment
The technical problem to be solved by the present invention is on the basis of matrix indicates, by increasing non local self-similarity about
Shu Xiang realizes video moving object detection.
Batch type video moving object detection algorithm based on matrix decomposition generally includes, the sparse item of prospect, smooth item, back
Scape, which is rebuild, to be indicated and background low-rank bound term.Usually there is following constrained optimization objective function:
Or it is converted into unconstrained optimization problem:
In formula (2), F is prospect matrix and Fij∈ { 0,1 }, being worth indicates that the point is foreground point for 1, otherwise is background dot；O
It is the matrix of the video sequence vectorization composition of input；B is background matrix and Bij∈[0,1]。It is Background Reconstruction
Indicate item,With FijValue is on the contrary, this indicates to work as FijFor 0 (For 1) when, the point be background dot when, OijWith BijMore connect
Closely；||B||*For background low-rank bound term, with the low-rank of nuclear norm representing matrix；||F||1It is the sparse constraint of prospect；It examines
Consider the influence of neighbor pixel, | | Avec (F) | |1For smooth item.
There are the following problems for the above-mentioned batch type video moving object detection algorithm indicated based on matrix.Batch type movement
Object detection is uniformly handled several frame images of video, it is difficult to meet the real-time of video moving object testing requirements,
And algorithm complexity is higher, it is difficult to meet the needs of practical application, in addition, obtaining from experimental result, existing batch type
The motion target area profile that moving object segmentation algorithm detects is unintelligible, there are problems that erroneous detection and part missing inspection, meanwhile, when
When moving object and larger context similarity, there are problems that the missing inspection of moving target partial region.
In order to solve the problems, such as erroneous detection and missing inspection existing for batch video moving object detection algorithm, this patent is in above-mentioned matrix
Non local self-similarity bound term is added on decomposition base.Non local self-similarity refers to all pictures in some pixel and image
Vegetarian refreshments similarity-rough set, steps are as follows for the calculating of non local self-similarity matrix:
1. dividing image to the image block for being p for size according to certain step-length η, such as: being that 4 pairs of images take 8 × 8 according to step-length
Image block.As shown in Figure 1.
2. pair currently processed image block and remaining all image block calculate similarity, as shown in formula (4).For each
A image block all carries out Similarity measures with residual image block.
3. n the smallest similarities (i.e. similarity is highest) carry out before taking for the image block that each has been handled
Superposition, the non local self-similarity value as the image block.
4. for each pixel, take in all image blocks where it the smallest non local self-similarity value as non-
The value of local self-similarity matrix corresponding position.Non local self-similarity matrix is indicated with S.As shown in Figure 1, S55Value be its institute
Four image blocks the non local self-similarity value of minimum.
In formula (4), Xi, XjTwo image blocks are respectively represented, calculate the pixel difference of two image block corresponding positions first
Quadratic sum, to it divided by pixel block size (p2), acquire image block XiWith XjSimilarity.
In the video sequence, for background for foreground area, area is larger.According to above-mentioned steps, it can be seen that back
Scape image block and remaining image block similarity are larger, and the non local self-similarity value acquired is smaller.That is SijValue it is smaller, the point is past
Past is background dot.
On the basis of the batch type video moving object detection based on matrix decomposition, increase non local self-similarity constraint
?.Such as increase non local self-similarity available following objective function on the basis of formula (2).
In formula (5), Q is that the non local self-similarity matrix S vectorization acquired by each frame image of video sequence is constituted
Matrix, QijQ is worked as in ∈ [0,1] expressionijValue it is bigger, indicate other pixel similarity very littles in the point and image, then the point
A possibility that for foreground point, is bigger.LastFor non-self-similarity bound term.This shows to work as QijValue it is bigger
When, it is constrained by minimizing, so that the prospect matrix respective value F finally acquiredijBigger, when 1, which is foreground point.To
Generate the constraint to prospect.The problem of moving object contours expansion detected in existing batch methods can be reduced, effectively
Improve erroneous detection problem.
In above-mentioned formula (5), the prospect matrix in batch type algorithm is carried out about respectively with non local self-similarity matrix
Beam increases non local self-similarity bound term.Due to the calculating of non local self-similarity matrix be based on single image, not by
Change the influence of the variations such as background and illumination.By the definition of non local self-similarity matrix it is found that working as non local self-similarity square
A possibility that value of battle array corresponding points is smaller, and the similarity of the point and other pixels is bigger, then the point is background is bigger.As a result,
Increase non local self-similarity bound term can improve variation background (as shake leaf, ripples etc.) caused by erroneous detection ask
Topic and the inaccurate problem of foreground area detection.And relative to the video moving object detection method detected based on conspicuousness
For, the calculating of non local self similarity is relatively simple, is not related to the calculating of super-pixel, time-consuming short, efficiency of algorithm is higher.
It should be noted that last in formula (5)It is only a kind of form of non-self-similarity bound term.
But this form has the advantages that expression way is simple, computational efficiency is high and accuracy in detection.Another non-self-similarity constraint
Item can beBut due to QijThe value of ∈ [0,1] is relative to FijValue very little, when the two addition be QijEffect
It can be submerged, cause the effect of non-self-similarity constraint unobvious.It certainly, can be by Q in the case where not considering calculation amountij
And FijConstitute the non-self-similarity bound term of other forms.
The present invention proposes on the basis of the video moving object detection algorithm based on matrix decomposition for Zhou et al.
DECOLOR [5] algorithm increases non local self-similarity to the bound term of prospect matrix, propose it is a kind of based on it is non local from
The video moving object detection algorithm of similitude, it is intended to improve present in current video moving object segmentation algorithm by changing
Erroneous detection caused by background and foreground area portion missing inspection problem.Specific step is as follows:
Step 1: for every frame image in video sequence, dividing the image into the image that size is p according to certain step-length η
Block；Each image block and remaining image block are according to formula (4) calculating similarity；N the smallest similarities carry out before taking
Superposition, the non local self-similarity value as the image block；In all image blocks where capture vegetarian refreshments it is the smallest it is non local from
Value of the similarity as non local self-similarity matrix corresponding position, obtains non local self-similarity matrix S.
Step 2: the non local self-similarity matrix S vectorization of all images is constituted into matrix Q.
Step 3: prospect matrix being constrained with non local self-similarity matrix Q, obtains non local self-similarity constraint
?Wherein F is prospect matrix, and λ is constraint factor.
Step 4: existing batch type video moving object is added in the non local self-similarity bound term in step 3 and is detected
In algorithm (DECOLOR), the objective function as shown in formula (5) is obtained.
Step 5: unconstrained minimization problem shown in solution formula (5).
Step 6: each frame image vectorization of video sequence to be processed is formed into input matrix O.
Step 7: by parameter alpha, β, λ, γ initialization.
Step: 8: setting the number of iterations starts to iterate to calculate.
Step 9: according to variable solution formula, F and B successively being solved
Step 10: 8~9 are repeated the above steps, until result restrains.
Step 11: iteration terminates, and exports prospect matrix F.The foreground area as detected.
Claims (1)
1. a kind of video moving object detection method based on non local self-similarity, this method is to batch type video moving object
Detection algorithm DECOLOR is improved, and steps are as follows:
Step 1: for every frame image in video sequence, dividing the image into the image block that size is p according to certain step-length η；
Each image block and remaining image block calculate similarity；N the smallest similarities are overlapped before taking, as the image
The non local self-similarity value of block；The smallest non local self-similarity value is as non-office in all image blocks where capture vegetarian refreshments
The value of portion's self-similarity matrix corresponding position obtains non local self-similarity matrix S；
Step 2: the non local self-similarity matrix S vectorization of all images is constituted into matrix Q；
Step 3: prospect matrix F being constrained with the non local self-similarity matrix Q of vectorization, obtains non local self-similarity
Bound term:Fij∈ { 0,1 } is the element of the i-th row of prospect matrix F, jth column, which is that the 1 expression point is
Foreground point, it is background dot which, which is the 0 expression point,；QijIt is the matrix Q constituted after non-local self-similarity matrix S vectorization
The i-th row, jth column element；
Step 4: non local self-similarity bound term is added to the target letter of batch type video moving object detection algorithm DECOLOR
Number, obtains new objective function:
Wherein O is the matrix of the video sequence vectorization composition of input；B is background matrix and Bij∈[0,1]；
It is that Background Reconstruction indicates item,With FijValue is on the contrary, this indicates to work as FijIt is 0, i.e.,When being 1, when which is background dot, Oij
With BijIt is closer；||B||*For background low-rank bound term, with the low-rank of nuclear norm representing matrix；||F||1It is the sparse of prospect
Bound term；Consider the influence of neighbor pixel, | | Avec (F) | |1For smooth item；α, β, γ, λ are background low-rank bound term respectively
Weighting coefficient, the weighting coefficient of prospect sparse constraint, smooth item weighting coefficient, non local self-similarity bound term weighting
Coefficient；
Step 5: for new objective function, solving following unconstrained minimization problem, obtain optimal background matrix B and prospect
Matrix F:
Step 6: each frame image vectorization of video sequence to be processed is formed into input matrix O；
Step 7: carrying out parameter initialization, and set the number of iterations, calculating is iterated, for prospect matrix F and background matrix B
It is successively solved, until result restrains；
Step 8: output prospect matrix F, foreground area as detected.
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