CN102881002A - Video background recovery method based on movement information and matrix completion - Google Patents
Video background recovery method based on movement information and matrix completion Download PDFInfo
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Abstract
The invention belongs to the field of computer vision, and aims to provide a simple and practical background extracting method. The technical scheme adopted by the invention is as follows: a video background recovery method based on movement information and matrix completion comprises the steps of adopting movements of a moving object between frames and frames through optical flow detection, detecting the movement information to generate weight matrixes, and arranging the weight matrixes according to column vectors to generate a total weight matrix; then, arranging all sampled frames according to the column vectors to form original data array matrixes; multiplying observation matrixes and the total weight matrix according to elements to obtain the observation matrixes; after that, obtaining the completed observation matrixes through the matrix completion; finally, recovering each array of the matrixes according to the size of original sampled frames to obtain a background image. The method is mainly applied to background extraction.
Description
Technical field
The invention belongs to computer vision field, relate to and adopt light stream to detect movable information, and matrix fill-in, realize background extracting.Specifically, the video background restoration methods that relates to based on motion information and matrix fill-in.
Background technology
The usable range of camera increased closely during the decade many.But these growths have caused the expansion of data, mean that dependence goes to store manually or deal with data becomes no longer feasible.For from video, automatically detect, storage, tracing movement target, researchers have proposed the method for some feasibilities.Simple moving object detection algorithm is the one and another present frame with the video sequence the inside, and a stable background frames is made comparisons.This is the background recovery algorithms of main flow, and they can standard go to set up a background model, then compare with it with present frame, detect when very large difference is arranged in the zone.The essence of background recovery algorithms is that moving target (prospect of mentioning namely) is identified from scene (calling background) stable or that slowly move.
Under indoor environment, a stable background model, might be suitable in analyzing of short duration video sequence.But this model poor effect in most of actual conditions needs a kind of more complicated model.And motion detection usually is to analyze the first step of scene.For instance, motion detection zone out may be for Gait Recognition, and people's face detects, stream of people's metering, traffic monitoring etc. and carry out filtering and characterization.The application of background scene and diversity thereof have explained that countless papers are for background extracting institute main topic of discussion.The problem that the background extracting Technology Need solves comprises the comparison for observed image and estimated image, does not comprise any targets of interest.This technology is relevant with background model (perhaps background image).This comparison procedure is called as the prospect detecting, and it is divided into two parts to the pixel that covers entire image: 1) comprise the prospect of targets of interest, 2) background, namely its supplementary set.
A lot of background extracting technology all comprise a lot of models and storage, some algorithm focuses on the specific demand that proposes an idealized background extracting technology, and also some must be adapted to slowly or violent illumination variation (variations of the situations such as time, cloud layer), dynamic change (camera vibrations), high-frequency background object (for example branch leaf) and background Geometrical change (vehicle of for example parking).Some application need in the Algorithms for Background Extraction insert camera, and computational load just becomes subject matter like this.And, in order to monitor outdoor scene, also need very strong noise resisting ability and illumination variation adaptive faculty.A special format for alternative background density model, the algorithm that has keeps an at first buffer memory of the given background value quantity that observes for each pixel, if a new value is coupling for the value that great majority are stored in the pixel model, then range background.Someone wishes that this method can prevent from connecting with offset issue, and the method is called as any assumed density model.But because the value of pixel background substitutes according to the method for first in first out, in accurately careful discussion, this still can have problems, unless it has stored a large amount of pixel samples, such as, about the fast and slow problem of motion in the background.This author mentions the buffer memory of 20 samples, is the minimum essential requirement that allows this method work, certainly they also noticed more than 60 samples to this method be do not have much improved.Therefore, the culture period of these methods must be comprised of 20 frames at least.Finally, in order to tackle illumination variation and target appearance or disappearance in the background, need to add two kinds of extra mechanism (a kind of is pixel level, and also having a kind of is the spot level) to algorithm, process whole target.
Summary of the invention
The present invention is intended to solution and overcomes the deficiencies in the prior art, and a kind of simple and practical background extracting method is provided.The technical scheme that the present invention takes is that the video background restoration methods of based on motion information and matrix fill-in adopts light stream to detect the movement of the moving target between frame and the frame, and detect movable information, generate weight matrix, and arrange by column vector, generate the total weight value matrix; Again all sample frame are arranged by column vector, form the raw data array matrix; Multiplying each other by element with observing matrix and total weight value matrix obtains observing matrix, afterwards, by matrix fill-in, draws the observing matrix of having filled; Finally, with each row of this matrix according to the size restoration of crude sampling frame out, obtain background image.Specifically, comprise the following steps:
1) construct original experimental data:
11) uniform sampling k frame image matrix from video is as original experimental data;
2) structure total weight value matrix:
21) detect the displacement that the movement elements between per two frames occurs with optical flow approach, obtain k-1 light stream matrix, this element is being worked as the displacement vector that occurs between the front cross frame in the original experimental data of each element representation in this matrix;
22) keep displacement vector in each light stream matrix less than 1 element, though namely in the raw data element motion be no more than a pixel, also to take into account, generate k-1 gray level image by conversion, the pixel value of the element that wherein moves is 0; The pixel value of the element that does not move is 255, claims that this gray level image is weight matrix;
23) this k-1 weight matrix is arranged by column vector, generated the total weight value matrix;
3) structure raw data array:
31) because the last frame image of sampling does not participate in motion, only the k-1 frame asks weight matrix used by light stream as a comparison, so the k frame of original experimental data is given up; This k-1 frame raw data is arranged by column vector, generate raw data array;
4) carry out background extracting:
41) make up observing matrix: total weight value matrix and raw data array corresponding element are multiplied each other obtains observing matrix;
42) by the matrix fill-in algorithm observing matrix is rebuild: make up Augmented Lagrangian Functions and find the solution protruding optimization problem.
41) making up observing matrix is specially:
Weight matrix has marked the position of part foreground pixel point, namely obtains the required observing matrix of matrix fill-in algorithm after it and raw data array corresponding element are multiplied each other; The element of observing matrix can be divided three classes: background pixel, the prospect that has calibrated, the unrecognized prospect that goes out; The prospect that has calibrated is set to 0, in iterative process, treats as the vacancy element, this matrix is summarized as following mathematical model:
P
Ω(D)＝P
Ω(A)+E
D ∈ R wherein
(m*n) * (k-1)Be the observing matrix of input, A ∈ R
(m*n) * (k-1)Be the background matrix that reconstructs, E ∈ R
(m*n) * (k-1)Be the matrix that the unrecognized foreground elements that goes out forms, (m*n) the every frame of expression observation data has m*n pixel, and k-1 represents to have in the observation data k-1 frame video sequence; Ω is the prospect coordinate that calibrates, P
Ω() represents matrix projection to index Ω.
42) be specially, it can abstractly be following protruding optimization problem that observing matrix is filled:
min||A||*+λ||E||
1 subject to P
Ω(D)＝P
Ω(A)+E
Wherein λ＞0 is used for balance nuclear norm and 1 norm to the impact of optimization problem, and subject to represents to make obedience, find the solution following formula and need to make up Augmented Lagrangian Functions, by iteration convergence progressively to optimum solution:
Y ∈ R in the formula
(m*n) * (k-1)Be Lagrange multiplier, μ＞0 is penalty factor.Finally finish matrix fill-in through after the limited number of time iteration to following formula, can produce the observing matrix of having filled, split, can obtain (k-1) individual size is the background image of m * n.
The characteristics of method of the present invention and effect:
The inventive method is also detected fully or is extracted in the situation of moving target not needing, and uses matrix fill-in can recover preferably background image.Have following characteristics:
1, program is simple, is easy to realize.
2, adopt the method for light stream, raw data is processed, detect movable information, draw weight matrix, obtain the total weight value matrix after arranging by column vector; Again raw data is given up last frame and obtained observing matrix by the column vector arrangement.These two matrixes are multiplied each other by corresponding element, obtain a matrix that the element disappearance is arranged, can turn parts into the whole problem like this, go whole dealing with problems with a larger matrix.And need not stick to moving target and the background parts of processing in each two field picture.
3, utilize the result of optical flow analysis, detect movable information, then carry out again matrix fill-in, can obviously improve the effect of background extracting.
Description of drawings
Above-mentioned and/or the additional aspect of the present invention and advantage are from obviously and easily understanding becoming the description of embodiment below in conjunction with accompanying drawing, wherein:
Fig. 1 is actual implementing procedure figure;
Fig. 2 is the raw data image that the k frame sampling obtains;
Fig. 3 is k-1 frame weight matrix image;
Fig. 4 is k-1 frame background image.
Embodiment
Below in conjunction with embodiment and accompanying drawing the present invention is made a detailed description.
The technical scheme that the present invention takes is 1) adopt light stream to detect the movement of the moving target between frame and the frame, detect movable information, generate weight matrix, and arrange by column vector, generate the total weight value matrix; Again all sample frame are arranged by column vector, form raw data array; 2) multiply each other by element with raw data array matrix and total weight value matrix and obtain observing matrix, afterwards, by matrix fill-in, draw the observing matrix of having filled; 3) final, each row of this low-rank matrix are filled out according to the size of crude sampling frame, obtain background image.Specifically, comprise the following steps:
1) construct original experimental data:
11) image array of uniform sampling k frame m * n size from video is as original experimental data;
2) structure total weight value matrix:
21) detect the displacement that the movement elements between per two frames occurs with optical flow approach, detect movable information, it is the same with sample frame to obtain (k-1) individual size, dimension is 2 light stream matrix, and this element is being worked as the displacement vector that occurs between the front cross frame in the original experimental data of each element representation in this matrix;
22) each light stream matrixing is become to be of a size of m * n capable, the compute matrix of 2 row, the 1st displacement scalar of classifying each element transverse movement between two frames as wherein, the 2nd classifies the displacement scalar of each element lengthwise movement between two frames as;
23) keep displacement vector in each light stream matrix less than 1 element (even namely in the raw data element motion be no more than a pixel, also to take into account), generate (k-1) individual gray level image that is of a size of m * n size by conversion, the pixel value of the element that wherein moves is 0, i.e. black; The pixel value of the element that does not move is 255, i.e. white.We claim that this gray level image is weight matrix, and (k-1) is individual altogether;
24) weight matrix of this (k-1) individual m * n size is arranged by column vector, generated size and be (the total weight value matrix of m * n) * (k-1);
3) structure raw data array:
31) because the last frame image of sampling does not participate in motion, only (k-1) frame asks weight matrix used by light stream as a comparison.So the k frame of original experimental data is given up (k-1) frame before keeping.The raw data of this (k-1) frame m * n size is arranged by column vector, generate (the raw data array of size of m * n) * (k-1).
4) carry out background extracting
41) make up observing matrix
Weight matrix has marked the position of part foreground pixel point, namely obtains the required observing matrix of matrix fill-in algorithm after it and raw data array corresponding element are multiplied each other.It is to concentrate at partial data as a reference with the motion vector that optical flow analysis produces the part foreground elements is demarcated.The element of observing matrix can be divided three classes: background pixel, the prospect that has calibrated, the unrecognized prospect that goes out.The prospect that we will calibrate sets to 0, and treats as the vacancy element in iterative process.To sum up, this matrix can be summarized as following mathematical model:
P
Ω(D)＝P
Ω(A)+E
D ∈ R wherein
(m*n) * (k-1)Be the observing matrix of input, A ∈ R
(m*n) * (k-1)Be the background matrix that reconstructs, E ∈ R
(m*n) * (k-1)Be the matrix that the unrecognized foreground elements that goes out forms, (m*n) the every frame of expression observation data has m*n pixel, (k-1) has k-1 frame video sequence in the expression observation data.Ω is the prospect coordinate that calibrates, P
Ω() represents matrix projection to index Ω.
43) mathematical model
It can abstractly be following protruding optimization problem that observing matrix is filled:
min||A||*+λ||E||
1 subject to P
Ω(D)＝P
Ω(A)+E
Wherein λ＞0 is used for the impact on optimization problem of balance nuclear norm and 1 norm.Find the solution following formula and need to make up Augmented Lagrangian Functions, by iteration convergence progressively to optimum solution:
Y ∈ R in the formula
(m*n) * (k-1)Be Lagrange multiplier, μ＞0 is penalty factor.Finally finish matrix fill-in through after the limited number of time iteration to following formula, can produce the observing matrix of having filled, split, can obtain (k-1) individual size is the background image of m * n.
The present invention proposes the background extracting method (shown in the flow process of Fig. 1) of a kind of optical flow-based and matrix fill-in, reach by reference to the accompanying drawings embodiment and be described in detail as follows:
11) image array of uniform sampling 25 frames 288 * 360 sizes from video is as original experimental data (as shown in Figure 2);
2) structure total weight value matrix:
21) detect the displacement that the movement elements between per two frames occurs with optical flow approach, it is the same with sample frame to obtain 24 sizes, dimension is 2 light stream matrix, and this element is being worked as the displacement vector that occurs between the front cross frame in the original experimental data of each element representation in this matrix;
22) each light stream matrixing is become to be of a size of 288 * 360 row, the compute matrix of 2 row, the 1st displacement scalar of classifying each element transverse movement between two frames as wherein, the 2nd classifies the displacement scalar of each element lengthwise movement between two frames as;
23) keep displacement vector in each light stream matrix less than 1 element (even namely in the raw data element motion be no more than a pixel, also to take into account), generate 24 gray level images that are of a size of 288 * 360 sizes by conversion, the pixel value of the element that wherein moves is 0, i.e. black; The pixel value of the element that does not move is 255, i.e. white.We claim that this gray level image is weight matrix, totally 24 (as shown in Figure 3);
24) weight matrix of these 24 288 * 360 sizes is arranged by column vector, generating size is the total weight value matrix of (288 * 360) * 24;
3) structure raw data array:
31) because the last frame image of sampling does not participate in motion, only the 24th frame asks weight matrix used by light stream as a comparison.So the 25th frame of original experimental data is given up, is kept front 24 frames.The raw data of these 24 frame, 288 * 360 sizes is arranged by column vector, generate the raw data array of (288 * 360) * 24 size.
4) carry out background extracting
41) make up observing matrix
Total weight value matrix and raw data array corresponding element are multiplied each other, generate the observing matrix that contains the element of having vacant position of (288 * 360) * 24 size.
42) substitution matrix fill-in algorithm produces background matrix
Observing matrix is carried out iteration as input substitution function, and the number of times of different video sequence iteration is not quite similar.After algorithm convergence, can automatically stop to calculate, generate the observing matrix of having filled of (288 * 360) * 24 size.Each row of the observing matrix of then this having been filled split out, and rearranging becomes 24 288 * 360 matrix, namely obtain 24 background images after the reduction.
Claims (4)
1. the video background restoration methods of a based on motion information and matrix fill-in is characterized in that, comprises the steps: to adopt light stream to detect the movement of the moving target between frame and the frame, and detect movable information, generate weight matrix, and arrange by column vector, generate the total weight value matrix; Again all sample frame are arranged by column vector, form the raw data array matrix; Multiplying each other by element with observing matrix and total weight value matrix obtains observing matrix, afterwards, by matrix fill-in, draws the observing matrix of having filled; Finally, with each row of this matrix according to the size restoration of crude sampling frame out, obtain background image.
2. the video background restoration methods of based on motion information as claimed in claim 1 and matrix fill-in is characterized in that, described step further is refined as:
1) construct original experimental data:
11) uniform sampling k frame image matrix from video is as original experimental data;
2) structure total weight value matrix:
21) detect the displacement that the movement elements between per two frames occurs with optical flow approach, obtain k-1 light stream matrix, this element is being worked as the displacement vector that occurs between the front cross frame in the every one by one original experimental data of element representation in this matrix;
22) keep displacement vector in each light stream matrix less than 1 element, though namely in the raw data element motion be no more than a pixel, also to take into account, generate k-1 gray level image by conversion, the pixel value of the element that wherein moves is 0; The pixel value of the element that does not move is 255, claims that this gray level image is weight matrix;
23) this k-1 weight matrix is arranged by column vector, generated the total weight value matrix;
3) structure raw data array:
31) because the last frame image of sampling does not participate in motion, only the k-1 frame asks weight matrix used by light stream as a comparison, so the k frame of original experimental data is given up; This k-1 frame raw data is arranged by column vector, generate raw data array;
4) carry out background extracting:
41) make up observing matrix: total weight value matrix and raw data array corresponding element are multiplied each other obtains observing matrix;
42) by the matrix fill-in algorithm observing matrix is rebuild: make up Augmented Lagrangian Functions and find the solution protruding optimization problem.
3. the video background restoration methods of based on motion information as claimed in claim 2 and matrix fill-in, it is characterized in that, 41) making up observing matrix is specially, weight matrix has marked the position of part foreground pixel point, namely obtains the required observing matrix of matrix fill-in algorithm after it and raw data array corresponding element are multiplied each other; The element of observing matrix can be divided three classes: background pixel, the prospect that has calibrated, the unrecognized prospect that goes out; The prospect that has calibrated is set to 0, in iterative process, treats as the vacancy element, this matrix is summarized as following mathematical model:
P
Ω(D)＝P
Ω(A)+E
D ∈ R wherein
(m*n) * (k-1)Be the observing matrix of input, A ∈ R
(m*n) * (k-1)Be the background matrix that reconstructs, E ∈ R
(m*n) * (k-1)Be the matrix that the unrecognized foreground elements that goes out forms, (m*n) the every frame of expression observation data has m*n pixel, and k-1 represents to have in the observation data k-1 frame video sequence; Ω is the prospect coordinate that calibrates, P
Ω() represents matrix projection to index Ω.
4. the video background restoration methods of based on motion information as claimed in claim 2 and matrix fill-in is characterized in that 42) be specially, it can abstractly be following protruding optimization problem that observing matrix is filled:
min||A||*+λ||E||
1subject to P
Ω(D)＝P
Ω(A)+E
Wherein λ＞0 is used for balance nuclear norm and 1 norm to the impact of optimization problem, and subject to represents to make obedience, find the solution following formula and need to make up Augmented Lagrangian Functions, by iteration convergence progressively to optimum solution:
Y ∈ R in the formula
(m*n) (k-1)Be Lagrange multiplier, μ＞0 is penalty factor, finally finishes matrix fill-in through after the limited number of time iteration to following formula, can produce the observing matrix of having filled, and splits again, and can obtain (k-1) individual size is the background image of m * n.
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| CN104573328A (en) * | 2014-12-17 | 2015-04-29 | 天津大学 | Rectangle fitting method based on slack variable bound |
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| CN106204477B (en) * | 2016-07-06 | 2019-05-31 | 天津大学 | Video frequency sequence background restoration methods based on online low-rank background modeling |
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