CN107871315A - A kind of video image motion detection method and device - Google Patents
A kind of video image motion detection method and device Download PDFInfo
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Abstract
The present invention proposes a kind of video image target detection method, including：Background modeling is carried out to the image of video input by two layers of mixed Gauss model, obtains the video image background of the video-input image, wherein the input of second of mixed Gauss model is the result after the modeling of first time mixed Gauss model；The video image background and the video-input image are made the difference frame by frame, obtain the video image prospect of video image；UNICOM region and the zonule based on pixel value size are eliminated to the video image prospect successively binary conversion treatment based on Otsu threshold, based on morphological erosion and dilation operation to eliminate, and form the foreground target of the inputted video image.In addition the present invention also proposes a kind of video image target detection means and system.
Description
Technical field
The present invention relates to a kind of video image motion object detection method and device, belong to artificial intelligence field.
Background technology
As China's Urbanization Construction paces are constantly accelerated, cities and towns safety management turns into the problem of very important, intelligence
Video monitoring system is applied to urban safety management more and more widely, and scale also constantly expands.Core as video monitoring
One of heart technology, the detection technique of moving object in video sequences also especially attract attention, and how further to improve in practice
The stability and accuracy of moving object detection turn into the emphasis of domestic and international large quantities of experts and scholar's research.Conventional motion target is examined
Survey method includes inter-frame difference, optical flow method, mixed Gauss model etc., wherein, as a kind of classical adaptive background modeling
Method, mixed Gauss model have preferable adaptability to complex background, and practical manifestation is also relatively preferably, therefore obtain researcher always
Concern.
The Chinese patent ZL201410090199.1 of prior art 1 discloses a kind of fortune based on mixed Gaussian and rim detection
Moving target detection method, current image frame is read from the video of camera shooting；Utilize mixed Gauss model, the initialization back of the body
Scape, and background is constantly updated, while isolate moving target and binaryzation；Fortune work(mesh is extracted using canny edge detection methods
Mark；By the progress of obtained moving target or computing and filling cavity；Shadow removing；Necessary post processing is carried out, obtains and ties to the end
Fruit；Circular treatment is until all picture frame processing terminate.The moving target and canny that the present invention is extracted using mixed Gauss model
The moving target or computing of operator extraction, solve what conventional method was extracted under moving target and background color similar situation
The problem of moving target serious loss.Although patent ZL for moving target with being handled under background color similar situation,
But the performance of individual layer mixed Gauss model is limited to, for the scarce capacity of video image background modeling, part prospect may be missed
It is judged to background.
The Chinese patent ZL200710304222.2 of prior art 2 discloses a kind of motion based on expansion mixed gauss model
Object detection method, module is built by first-order model, based on expansion mixed gauss model, construct shaded background and prospect
Probability density function；Module, based on the model of above-mentioned three class, tectonic movement target and non-athletic target are built by second-level model
Probability density function；By sort module, using MAP-MRF (Maximum a Posteriori-Markov Random
Field) method is classified；The feedback information of application tracking, further accurate foreground model.The present invention is by by Gaussian Mixture
Model Fusion spatial information can overcome the prospect error detection caused by background motion；By merging the back of the body in a probabilistic framework
Scape modeling, foreground detection and shadow removal can overcome the adverse effect caused by shade, so as to improve the detection of moving target
Effect.Although prior art 2 is preferable to the background modeling effect of moving target, the algorithm based on markov random file, meter
It is higher to calculate complexity, is relatively inaccessible to for the requirement of real-time of actual video monitoring image.
The present invention is directed to the drawbacks described above of prior art, and the present invention proposes a kind of video image motion object detection method
And device.
The content of the invention
The purpose of the present invention is the defects of overcoming prior art to exist, there is provided a kind of video image motion object detection method
And device.In addition, another object of the present invention there is provided a kind of video image target detection method of stabilization
Another object of the present invention realizes two layers of Gauss modeling algorithm.
The present invention is still another object is that post-processing algorithm to handling image.
To realize the above-mentioned purpose of the present invention, the present invention proposes a kind of video image motion object detection method, including：
S1. background modeling is carried out to the image of video input by two layers of mixed Gauss model, obtains regarding for the video-input image
Frequency image background, wherein the input of second of mixed Gauss model is the result after the modeling of first time mixed Gauss model；S2. will
The video image background makes the difference frame by frame with the video-input image, obtains the video image prospect of video image；S3.
Connection is eliminated to the video image prospect successively binary conversion treatment based on Otsu threshold, based on morphological erosion and dilation operation
Logical region and the zonule elimination based on pixel value size, form the foreground target of the inputted video image.
In addition, the invention also provides a kind of video image target detection means, including：Background modeling unit, passes through two
Layer mixed Gauss model carries out background modeling to the image of video input, obtains the video image back of the body of the video-input image
Scape, wherein the input of second of mixed Gauss model is the result after the modeling of first time mixed Gauss model；Background eliminates unit,
The video image background that the background modeling unit exports is made the difference frame by frame with the video-input image, obtains video figure
The video image prospect of picture；Post-processing unit, the video image prospect that unit output is eliminated to the background are based on big Tianjin successively
The binary conversion treatment of threshold value, UNICOM region and the zonule based on pixel value size are eliminated based on morphological erosion and dilation operation
Eliminate, form the foreground target of the inputted video image.
In addition, the invention also provides a kind of video image target detection means, including：One or more processors；Deposit
The non-transitorycomputer readable storage medium of the one or more instructions of storage, the one or more of instructions of computing device
When, it is configured to：Background modeling is carried out to the image of video input by two layers of mixed Gauss model, obtains the video input
The video image background of image, wherein the input of second of mixed Gauss model is the knot after the modeling of first time mixed Gauss model
Fruit；The video image background and the video-input image are made the difference frame by frame, before obtaining the video image of video image
Scape；Disappear to the video image prospect successively binary conversion treatment based on Otsu threshold, based on morphological erosion and dilation operation
Eliminated except UNICOM region with the zonule based on pixel value size, form the foreground target of the inputted video image.
The beneficial effects of the invention are as follows：First, the mode of two layers of mixed Gauss model modeling provided by the invention can counted
Reach the effect of relative equilibrium, effect stability in terms of the degree of accuracy for calculating speed and background modeling.2nd, after algorithm is by a variety of images
Processing operation, including binaryzation, morphology operations, avoid the interference of Small object.3rd, algorithm can be realized effectively continuous for video
The background modeling of image, so as to effective detection moving target.
Brief description of the drawings
Fig. 1 shows the video image motion target detection schematic diagram based on two layers of gauss hybrid models；
Fig. 2 shows the video image motion object detection method flow chart based on two layers of gauss hybrid models；
Fig. 3 shows the composition frame chart of video image target detection means；
Fig. 4 shows the composition frame chart of post-processing unit；
Fig. 5 shows the composition frame chart of another video image target detection means；
Fig. 6 shows the design sketch after the processing of video images detection method.
Embodiment
In image and adaptation processing, so-called video, actually by a series of images with time series feature,
It is sequence image.For common monitor video, because camera is relatively stable, therefore one will be had in sequence image
Individual metastable scene does not change, that is, background.The object changed in the background, i.e. moving target, because with value
Information is, it is necessary to be detected, i.e., so-called foreground information.Background and prospect are actually also image, but background is typically constant, preceding
Scape is real-time change as moving target.
Fig. 1 is the video image motion target detection schematic diagram proposed by the present invention based on two layers of gauss hybrid models, defeated
The video image entered obtains video background after two layers of gauss hybrid models processing 101, then carries out background and eliminates 102, will
Result after background eliminates carries out post processing of image 103, obtains the foreground image of moving target.
Fig. 2 is the video image motion object detection method flow proposed by the present invention based on two layers of gauss hybrid models
Figure, it is broadly divided into two layers of mixed Gauss model background modeling S1, background makes the difference and eliminates tri- S2, post processing of image S3 steps.
1) background modeling S1：From input monitoring video input picture frame by frame, background is entered by two layers of mixed Gauss model
Row modeling, wherein the input of second of mixed Gauss model is the result of modeling for the first time.Modeled by two-layer model, most end form
Into the background of video image.
The step of wherein single mixed Gauss model models is as follows：
Initial background model μ, initial background average are μ0, primary standard difference σ0, initial differential threshold value T (being arranged to 20),
IX, yFor the pixel value at pixel (x, y) place：
μ (x, y)=IX, y
σ (x, y)=T
Wherein, T is the pixel value of image, and it only has gray level, without dimension, artificially can be set according to environment.
I. pixel I is checkedX, yBelong to prospect or background, wherein being λ threshold parameters, judge mean μ (x, y) whether one
Determine in scope：
If | IX, y- μ (x, y) | ＜ λ * σ (x, y), IX, yFor background
Otherwise, IX, yFor prospect
Ii. study renewal is carried out to background, more new formula is as follows, and wherein α is learning rate, typically may be configured as 1e-4：
μ (x, y)=(1- α) * μ (x, y)+α * IX, y
Iii. repeat step ii, iii stop until algorithm, that is, work asWhen stop, ε is also here
One constant value is a small amount of, can use 1e-5.
2) background eliminates S2：The video background established using the first step, is made the difference frame by frame with video image, eliminates background, warp
Cross the prospect of the result made the difference, as video image, and the moving target of video sequence image；
Making the difference formula is：
DX, y=IX, y- μ (x, y)
Wherein, IX, yArtwork is represented, μ (x, y) is calculating gained background.
3) post processing of image S3：To the image after making the difference, post processing of image operation is carried out, before ultimately forming image
Scape target.These post-processing operations are carried out successively, are specifically included：It is binary conversion treatment based on Otsu threshold, rotten based on morphology
Erosion eliminates UNICOM region, the zonule based on pixel value size with dilation operation and eliminated.
Wherein, the binary conversion treatment based on Otsu threshold is：
Otsu threshold assumes that image histogram is bimodal distribution, and its basic assumption is that setting can be by image G prospects and the back of the body
The separated threshold value of scape, it should make it that the inter-class variance of foreground and background pixel is maximum.Mathematically, Otsu threshold t should meet such as
Lower optimal expression formula：
Wherein, ω0=N0/ N, ω1=N1/ N,Here,
N0, N1Represent prospect, background and total number of pixels respectively with N.piRepresent gray level i frequency.μ0, μ1WithBefore representing respectively
The gray average of scape, background and full figure pixel.For RGB image, t value scope is 0-255.Therefore, after obtaining t, lead to
Thresholding is crossed, segmentation figure can be obtained as RsegIt is as follows：
In segmentation figure as RsegIn, the pixel for representing prospect is all marked as 1, and background pixel is labeled as 0.
Wherein, it is based on morphological erosion and the step of dilation operation elimination UNICOM region：
Provided with two images B, A, if A is processed object, i.e., the data after the binary conversion treatment based on Otsu threshold,
And B is for handling A, then B is referred to as structural element, and is visually referred to as brush.Structural element is generally all that some compare
Small image.Etching operation is first carried out to the data after the binary conversion treatment based on Otsu threshold, then carries out expansive working,
It is exactly morphologic opening operation.
Wherein corroding (Erosion) operation is：
X is all set for making x still in X after S translations x with the S results corroded.In other words, obtained with S to corrode X
To set be set that S is entirely included in S origin position when in X.
Wherein expanding (Dilation) operation is：
Expansion can regard the dual operations of corrosion as, and its definition is：Ba is obtained after structural element B is translated a, if Ba is hit
Middle X, write down this point.The set of all a points compositions for meeting above-mentioned condition is referred to as the result that X is expanded by B.
Carried out to eliminating the data behind UNICOM region based on morphological erosion and dilation operation based on the small of pixel value size
Region eliminates, wherein, the zonule elimination side based on pixel value size is：
If it is { A to obtain UNICOM region in image G1, A2..., AN, corresponding UNICOM's area pixel value number is respectively
{n1, n2..., nN, if then ni＜ ε, wherein ε are manually set, and can be taken as 30, then the region is cast out, and are determined as non-targeted；If ni＞
ε, then it is target, as prospect.
Fig. 3 is a kind of video image target detection means 300 proposed by the present invention, including：Background modeling unit 301, lead to
Cross two layers of mixed Gauss model and background modeling is carried out to the image of video input, obtain the video image of the video-input image
Background, wherein the input of second of mixed Gauss model is the result after the modeling of first time mixed Gauss model；Background eliminates single
Member 302, the video image background that the background modeling unit exports is made the difference frame by frame with the video-input image, obtained
The video image prospect of video image；Post-processing unit 303, the video image prospect of unit output is eliminated to the background successively
Binary conversion treatment based on Otsu threshold, eliminate based on morphological erosion and dilation operation UNICOM region and based on pixel value size
Zonule eliminate, form the foreground target of the inputted video image.
Each layer of modeling method in wherein described background modeling unit in two layers of mixed Gauss model regards to Fig. 1 as described above
In frequency image object detection method described in modeling method.
As shown in figure 4, wherein described post-processing unit 303 includes the binary conversion treatment module 304 based on Otsu threshold, base
UNICOM's regions module 305 and the zonule cancellation module 306 based on pixel value size are eliminated in morphological erosion and dilation operation,
Described image prospect obtains the foreground target of inputted video image after three resume modules successively.
Fig. 5 is that the present invention proposes another video image target detection means 400, and it includes：One or more processors
401；
The non-transitorycomputer readable storage medium 403 of the one or more instructions 402 of storage, wherein the computer can
Pending data can also be stored by reading storage medium 403, and the data may be alternatively stored in other storage mediums, the processor
When performing one or more of instructions, it is configured to：The image of video input is carried on the back by two layers of mixed Gauss model
Scape models, and the video image background of the video-input image is obtained, wherein the input of second of mixed Gauss model is first
Result after secondary mixed Gauss model modeling；The video image background and the video-input image are made the difference frame by frame,
Obtain the video image prospect of video image；To the video image prospect successively binary conversion treatment based on Otsu threshold, base
Eliminated in morphological erosion with dilation operation elimination UNICOM region with the zonule based on pixel value size, form the input and regard
The foreground target of frequency image.
Fig. 6 is the inventive method to be provided by taking indoor result as an example and result that device obtains, city video and indoor video
Processing form is different except position, and remaining is consistent.
In a word, the modeling format provided by the invention based on two layers of mixed Gauss model, it is possible to prevente effectively from video image
The influence of noise spot in moving object detection, the effect of relative equilibrium is reached in terms of the degree of accuracy of calculating speed and background modeling
Fruit.The operation of a variety of post processing of image, such as binaryzation, morphology operations, the interference of Small object can be avoided.In general,
For the detection method of moving object in video sequences, it is possible to achieve automatically analyzing and studying and judging to supervision of the cities video, for
The urban issueses such as processing parking offense, random road occupying can play effective Auxiliary support effect.
Claims (17)
1. a kind of video image target detection method, including：
S1. background modeling is carried out to the image of video input by two layers of mixed Gauss model, obtains the video-input image
Video image background, wherein the input of second of mixed Gauss model be first time mixed Gauss model modeling after result；
S2. the video image background and the video-input image are made the difference frame by frame, obtains the video figure of video image
As prospect；
S3. transported to the video image prospect successively binary conversion treatment based on Otsu threshold, based on morphological erosion and expansion
Calculate and eliminate UNICOM region and the zonule elimination based on pixel value size, form the foreground target of the inputted video image.
2. image object detection method as claimed in claim 1, each layer in two layers of mixed Gauss model is modeled as：
S11. image background model is initialized,Wherein, initial background average is μ0, primary standard is poor
σ0, initial differential threshold value T (being arranged to 20), IX, yFor the pixel value at pixel (x, y) place；
S12. by whether within the specific limits to judge mean μ (x, y), to check pixel IX, yBelong to prospect or background, if
|IX, y- μ (x, y) | ＜ λ * σ (x, y), IX, yFor background, otherwise, IX, yFor prospect, wherein being λ threshold parameters；
S13. μ (x, y)=(1- α) * μ (x, y)+α * I are passed throughX, yStudy renewal, and repeat step S12 are carried out to background, until μ
(x, y) meets condition, whereinWherein α is learning rate.
3. method as claimed in claim 2, wherein learning rate α takes 1e-4.
4. the method as described in claim 1, wherein the binary conversion treatment based on Otsu threshold is：It is double to set image histogram
Peak is distributed, and sets the separated Otsu threshold of display foreground and background so that the inter-class variance of foreground and background pixel is maximum.
5. method as claimed in claim 4, wherein the Otsu threshold t meets following formula,
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Wherein, ω0=N0/ N, ω1=N1/ N,N0, N1Distinguish with N
Represent prospect, background and total number of pixels, piRepresent gray level i frequency, μ0, μ1WithProspect, background and full figure are represented respectively
The gray average of pixel.
6. the method as described in claim 1, wherein eliminating UNICOM region with dilation operation based on morphological erosion and being：Knot is set
Constitutive element B, etching operation is first carried out to the result after the binary conversion treatment based on Otsu threshold, then carries out expansive working.
7. the method as described in claim 1, wherein the elimination of the zonule based on pixel value size is：It is by UNICOM's region representation
{A1, A2..., AN, corresponding UNICOM's area pixel value number is respectively { n1, n2..., nN, if then ni＜ ε, wherein ε are artificial
Setting, then the region is cast out, and is determined as non-targeted；If ni＞ ε, then it is target, as prospect.
8. method as claimed in claim 7, wherein the ε is 30.
9. as profit requires 2 methods stated, wherein T values 20.
10. a kind of video image target detection means, including：
Background modeling unit, background modeling is carried out to the image of video input by two layers of mixed Gauss model, obtains described regard
The video image background of frequency input picture, wherein the input of second of mixed Gauss model is the modeling of first time mixed Gauss model
Result afterwards；
Background eliminates unit, and the video image background that the background modeling unit exports is done frame by frame with the video-input image
Difference processing, obtains the video image prospect of video image；
Post-processing unit, the video image prospect exported to background elimination unit is successively at the binaryzation based on Otsu threshold
Reason, the zonule based on morphological erosion and dilation operation elimination UNICOM region and based on pixel value size eliminate, described in formation
The foreground target of inputted video image.
11. device as claimed in claim 10, wherein each in two layers of mixed Gauss model in the background modeling unit
Layer is modeled as：
Image background model is initialized,Wherein, initial background average is μ0, primary standard difference σ0, just
Beginning differential threshold T (being arranged to 20), IX, yFor the pixel value at pixel (x, y) place；
By whether within the specific limits to judge mean μ (x, y), to check pixel IX, yBelong to prospect or background, if |
IX, y- μ (x, y) | ＜ λ * σ (x, y), IX, yFor background, otherwise, IX, yFor prospect, wherein being λ threshold parameters；
Pass through μ (x, y)=(1- α) * μ (x, y)+α * IX, yStudy renewal, and repeat step S12 are carried out to background, until μ (x, y)
Meet condition, whereinWherein α is learning rate.
12. device as claimed in claim 10, wherein the post-processing unit includes the binary conversion treatment mould based on Otsu threshold
Block, UNICOM's regions module and the zonule cancellation module based on pixel value size are eliminated based on morphological erosion and dilation operation,
Described image prospect obtains the foreground target of inputted video image after three resume modules successively.
13. device as claimed in claim 12, wherein the setting image histogram of the binary conversion treatment module based on Otsu threshold
For bimodal distribution, and set the separated Otsu threshold of display foreground and background so that the inter-class variance of foreground and background pixel
It is maximum.
14. device as claimed in claim 13, wherein the Otsu threshold t meets following formula,
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Wherein, ω0=N0/ N, ω1=N1/ N,N0, N1Distinguish with N
Represent prospect, background and total number of pixels, piRepresent gray level i frequency, μ0, μ1WithProspect, background and full figure are represented respectively
The gray average of pixel.
15. device as claimed in claim 12, wherein it is described based on morphological erosion and dilation operation eliminate UNICOM region and
Zonule cancellation module based on pixel value size is used for：Setting structure element B, after the binary conversion treatment based on Otsu threshold
Result first carry out etching operation, then carry out expansive working.
16. device as claimed in claim 12, wherein the elimination of the zonule based on pixel value size is：By UNICOM's region representation
For { A1, A2..., AN, corresponding UNICOM's area pixel value number is respectively { n1, n2..., nN, if then ni＜ ε, wherein ε are people
Work is set, then the region is cast out, and is determined as non-targeted；If ni＞ ε, then it is target, as prospect.
17. a kind of video image target detection means, including：
One or more processors；
The non-transitorycomputer readable storage medium of the one or more instructions of storage, the computing device are one or more
During individual instruction, it is configured to：
Background modeling is carried out to the image of video input by two layers of mixed Gauss model, obtains regarding for the video-input image
Frequency image background, wherein the input of second of mixed Gauss model is the result after the modeling of first time mixed Gauss model；
The video image background and the video-input image are made the difference frame by frame, before obtaining the video image of video image
Scape；
Disappear to the video image prospect successively binary conversion treatment based on Otsu threshold, based on morphological erosion and dilation operation
Eliminated except UNICOM region with the zonule based on pixel value size, form the foreground target of the inputted video image.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN109102526A (en) * | 2018-07-27 | 2018-12-28 | 东莞职业技术学院 | The foreground detection method and device of the monitoring system of unmanned plane |
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| CN111582166A (en) * | 2020-05-07 | 2020-08-25 | 深圳市瑞驰信息技术有限公司 | Remnant detection method based on Gaussian modeling and YoLo V3 target detection |
| CN114677343A (en) * | 2022-03-15 | 2022-06-28 | 华南理工大学 | Highway object throwing detection method based on double backgrounds |
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