CN106372576A - Deep learning-based intelligent indoor intrusion detection method and system - Google Patents

Deep learning-based intelligent indoor intrusion detection method and system Download PDF

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CN106372576A
CN106372576A CN201610705858.7A CN201610705858A CN106372576A CN 106372576 A CN106372576 A CN 106372576A CN 201610705858 A CN201610705858 A CN 201610705858A CN 106372576 A CN106372576 A CN 106372576A
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胡婧
覃婷婷
成孝刚
邵文泽
成云
李德志
李海波
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Nanjing Post and Telecommunication University
Nanjing University of Posts and Telecommunications
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    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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    • G06V40/168—Feature extraction; Face representation
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses a deep learning-based intelligent indoor intrusion detection method and system. The method comprises the following steps of establishing a BP neural network model; obtaining difference images between adjacent frames in a monitoring video picture by utilizing a frame difference algorithm; carrying out binarization processing on the obtained difference images and extracting change foreground area images, under a static background, in the processed images; detecting and identifying whether human shapes exist in the extracted change foreground images or not; when the human shapes exist, detecting and extracting face area images from the change foreground areas; identifying whether the extracted face area images are user images or not; and when the face area images are not the user images, determining the face area images as non-user intrusion and sending alarm signals to the users. The method and system are strong in ability of resisting the interference of other moving objects and low in mis-judgement rate, and can be used for carrying out massive video data analysis and correctly carrying out intrusion detection and identification.

Description

A kind of Intelligent indoor intrusion detection method based on deep learning and system
Technical field
The present invention relates to a kind of Intelligent indoor intrusion detection method based on deep learning and system, belong to video monitoring Technical field.
Background technology
With economic and science and technology development, people are for the attention degree also more and more higher of indoor security, intelligent room Interior intruding detection system becomes trend, then the intelligent, accuracy for intrusion target detection and identification and real-time Research also become very meaningful.
In traditional intelligent control method, moving object whether is had to intrude into it using photographic head and Infrared Detectorss perception Sensing range, if invader is detected, video information is sent to mobile terminal.
But traditional monitoring method existing defects, specifically, its method cannot differentiate whether invader is user, resists other Moving object interference performance is little, and False Rate is high, intelligent low, does not possess accurately detection identification function；It is due to processor Limit it is impossible to carry out substantial amounts of video data analysis, real-time is low.Accordingly, there exist to user and invader, moving object with enter The person of invading can not accurately detect the problem of identification.
Content of the invention
The technical problem to be solved is to overcome the deficiencies in the prior art, provide a kind of based on deep learning Intelligent indoor intrusion detection method and system, solve to user and invader, moving object and invader can not accurately detect knowledge Other problem.
The present invention specifically employs the following technical solutions solution above-mentioned technical problem:
A kind of Intelligent indoor intrusion detection method based on deep learning, comprises the following steps:
Set up bp neural network model, and according to the training data that input comprises user images, bp neural network model is instructed Practice；
The difference image of adjacent interframe is obtained using inter-frame difference algorithm from monitor video picture；
Binary conversion treatment is carried out to the difference image of acquired adjacent interframe, and extracts the static back of the body in image after treatment Prospects For Changes region under scape；
To the Prospects For Changes image detection extracted and identify that it whether there is using the bp neural network model set up Humanoid；When identification has humanoid, detection and extraction from described Prospects For Changes image obtains human face region image；
Using the bp neural network model set up, the human face region image detection extracted and identification are determined whether User images；And when identification is judged as non-user image, is defined as non-user invasion and sends alarm signal to user.
Further, as a preferred technical solution of the present invention, in methods described, human face region image is by following step Rapid acquisition:
Set up based on colored complexion model, and normalization colour of skin similarity；
Using the complexion model set up to Prospects For Changes image detection, obtain normalized colour of skin similarity graph picture；
Set colour of skin similarity threshold, binary conversion treatment is carried out to acquired normalized colour of skin similarity graph picture, obtain Area of skin color；
According to the scope setting, area of skin color is chosen, obtain human face region image.
Further, as a preferred technical solution of the present invention: utilize prp conjugate gradient algorithms pair in methods described Bp neural network model is trained.
Further, as a preferred technical solution of the present invention: in methods described, training data at least includes possessing User's whole body images of different attitudes and user's facial image.
Further, as a preferred technical solution of the present invention: in methods described training data also include non-humanoid Image.Further, as a preferred technical solution of the present invention: in methods described when identification judges not have humanoid, Reacquire monitor video picture.
Further, as a preferred technical solution of the present invention: be judged as user images in identification in methods described When, it is defined as user and monitor video picture is reacquired according to setting time.
The present invention also proposes a kind of Intelligent indoor intruding detection system based on deep learning, comprising:
Video monitoring module, for domestic environment is entered with Mobile state monitoring, obtains monitor video picture；
Image capture module, for obtaining the difference diagram of adjacent interframe from monitor video picture using inter-frame difference algorithm Picture；
Image zooming-out module, for carrying out binary conversion treatment to the difference image of acquired adjacent interframe, and extracts through place Prospects For Changes area image under static background in image after reason；
Discriminatory analysiss module, including modeling unit, recognition unit, area extracting unit and output unit；Wherein, described build Form unit, is used for setting up bp neural network model, and the training data comprising user images according to input is to bp neutral net mould Type training；Described recognition unit, for being detected the Prospects For Changes being extracted image input bp neural network model and being known Do not judge whether humanoid；Described area extracting unit is used for when identifying that judgement has humanoid, from described Prospects For Changes figure In picture, detection and extraction obtain human face region image；Described recognition unit is additionally operable to the human face region image extracting input bp god Detected through network model and identification is determined whether user images；Described output unit, for being judged as non-use in identification It is defined as non-user invasion during the image of family, and generate and output control signal；
Transmitting element, for sending invasion prompting data according to control signal to user.
Further, as a preferred technical solution of the present invention: the invasion prompting packet sending in this unit Include invader's image.
Further, as a preferred technical solution of the present invention: also include alarm module, described alarm module is used for Sound and light alarm is produced according to control signal.
The present invention adopts technique scheme, can produce following technique effect:
Intelligent indoor intrusion detection method based on deep learning provided by the present invention and system, first to monitor video Moving target in picture is detected, so not carrying out subsequent treatment for the constant video image of scene, saves server Expense；And add in systems to humanoid detection, it is to avoid house pet, the interference of the inhuman factor such as sweeping robot；Because right The identification of user is eventually transformed into the identification to user's face, so the change of user's dress ornament hair style has no effect on the correct knowledge to it , there is no training data needed for good universality, neutral net few.And, image is gathered using infrared camera, can not be subject to Illumination limits；Therefore design more humane, other moving object interference performances anti-are strong, False Rate is low, it is possible to use high performance Processing mode carries out substantial amounts of video data analysis, possesses high efficiency and real-time, can transport exactly to user and invader Animal body and invader accurately detect identification.
The method of the present invention and system possess following advantage:
(1) the Intelligent indoor intrusion detection method based on deep learning provided by the present invention and system, using based on prp The bp neutral net of conjugate gradient algorithms, improves network training speed it is ensured that restraining correctness.
(2) present invention incorporates inter-frame difference algorithm and skin cluster model, to target area grading extraction, calculate letter Single it is ensured that real-time.
(3) extract the target area input neural network detecting, be not required to identify complex background and other interference factors, protect The correctness of card classification.
(4) present invention is based on image processing techniquess, can expeditiously complete to identify, can save power consumption.Can effectively solve Certainly to user and invader, moving object and invader can not accurately detect the problem of identification.
Brief description
Fig. 1 is the schematic flow sheet of the Intelligent indoor intrusion detection method based on deep learning of the present invention.
Fig. 2 is the bp Artificial Neural Network Structures schematic diagram of the present invention.
Fig. 3 is the principle schematic of the Intelligent indoor intruding detection system based on deep learning of the present invention.
Specific embodiment
With reference to Figure of description, embodiments of the present invention are described.
As shown in figure 1, the present invention devises a kind of Intelligent indoor intrusion detection method based on deep learning, including following Step:
Step 1, set up bp neural network model, and the training data comprising user images according to input is to bp neutral net Model training.In methods described, training data to be inputted using this model for the first time, bp neural network model is trained, its Step includes initializing weights and threshold value, successively adjusts weights and threshold value respectively using prp conjugate gradient algorithms, iteration is to maximum Iterationses.Its process is specific as follows:
To the bp neural network model input training data using prp conjugate gradient algorithms, described training data at least wraps Include the user's whole body images possessing different attitudes and user's facial image it is preferable that can also include non-humanoid image such as house pet, Intellective dust collector etc..
As shown in Fig. 2 f is a class training data, neutral net is output as y=ψ to the iterative process of bp neural network model (wixi- θ), wherein ψ represents excitation function, can use sigma function；xi, xjRepresent i-th layer and the input of jth layer neuron respectively Vector；wiRepresent the weight vector connecting i-th layer and next layer, wjRepresent the weight vector connecting jth layer and next layer；θ is threshold Value.Using i in Fig. 2, referring to all hidden layers in neutral net, hidden layers numbers determine j two-layer hidden layer according to practical situation, because respectively Layer alternative manner is identical, so hereafter only referring to the input vector x of each layerlWith weight vector wl, have
Prp conjugate gradient algorithms convergence weights and threshold value is adopted in the present invention.Then object function is:
l(wl, θ) and=wlxl-θ
And, set the object function error gradient that kth time iteration obtains as ek, be can get based on prp conjugate gradient algorithms CoefficientThus can determine that the direction of search: pk=-ek+βkpk-1.Step-size in search α is obtained by linear searchk, lead to Cross that conjugate gradient algorithms obtain weight vector and the iterative difference of input vector is as follows:
wl(k+1)=wl(k)+αkpk, k=0,1 ...；
θ (k+1)=θ (k)+αkpk, k=0,1 ....
W in above-mentioned formulalK () represents kth time iteration wlThe weight vector iterative value obtaining, θ (k) represents that kth time iteration θ obtains To threshold value iterative value.
And, the bp neural network learning process of the present invention adopted prp conjugate gradient algorithms is as described below:
First, input: a class training image f={ f1, f2..., fn, wherein f can comprise the people possessing different attitudes Shape whole body images or user's facial image.
Subsequently into iterative process, as follows:
1. initialize；Initialize each layer neuron weight vector wl(0), threshold θ (0).
2. adjust weights；Fixed threshold θ, successively adjusts each layer weight vector w using weight vector is iterativel, until convergence.
3. adjust threshold value；Fixing weight wl, using threshold value iterative adjustment threshold θ, until convergence.
4. iteration, repetitive process 2,3, until it reaches maximum iteration time.
Possess the humanoid whole body images of different attitudes to the bp neural network model input using prp conjugate gradient algorithms, Trained study model can be made to obtain Human detection device function, can recognize that humanoid and non-humanoid region；And, input user Facial image, makes model obtain face identification functions, the invader of recognizable user and non-user with trained study.
Thus obtained bp neural network model is capable of identify that user and invader, possesses the humanoid mankind and does not possess people The moving object of shape.
Step 2, obtain the difference image of adjacent interframe from monitor video picture using inter-frame difference algorithm.It is from prison Control video pictures capture multiple image, specifically includes following steps:
If pth frame and pth+1 frame inputted video image are respectively fp(i, j) and fp+1(i, j), wherein (i, j) represent pixel Point, by corresponding for two field pictures pixel difference:
dp(i, j)=| fp+1(i, j)-fp(i, j) |
Then can obtain difference image:
Step 3, the difference image to acquired adjacent interframe carry out binary conversion treatment, and it is black for obtaining static background, change Foreground area is white bianry image, therefore can by Prospects For Changes extracted region out, to be provided in recognition detection process.And it is right It is not required to carry out subsequent treatment to background area in the constant video image of stationary background, server overhead can be saved.
The Prospects For Changes image zooming-out of prospect in described image, particularly as follows:
Given threshold t, obtains the bianry image of Prospects For Changes image:
h p ( i , j ) = 1 , d p ( i , j ) &greaterequal; t 0 , d p ( i , j ) < t
Step 4, using the bp neural network model set up to the Prospects For Changes image detection extracted and identification judge With the presence or absence of humanoid.Specifically include following steps:
After the above-mentioned image-region that moving target in static background is located extracts, at the image of this extraction Reason and cutting, to mate neural network model input picture characteristic.Then it is inputted in bp neural network model, using bp god Through network model image is carried out humanoid with non-humanoid identification, this step is using to humanoid detection, it is to avoid house pet, sweeps the floor The interference of the inhuman factor such as robot.If bp neural network model exports y=1, there is humanoid region, continue in Prospects For Changes image Continue and human face detection and tracing operation is carried out to target area, that is, enter step 5；Otherwise, bp neural network model output y=0, table Bright do not have non-humanoid region, does not carry out following step 5, continues monitoring.I.e. when identification judges not have humanoid, show inhuman Body can reacquire monitor video picture and repeat step 3 to 4 with return to step 2, carry out continuation and regard in active state, this method Frequency monitors and identifies, until it is humanoid to identify that judgement exists.
Export y=1 in bp neural network model, when identification judgement has humanoid, detect from described Prospects For Changes image Obtain human face region image with extracting.Described human face region image acquisition procedures are specific as follows:
First, using the complexion model based on y cb cr color system, pixel (i, j) place chroma blue component can be obtained Value cbWith red chrominance component value cr；If the chromatic value x of pixelij=(cb, cr)t, then the colour of skin similarity of pixel (i, j) For:
p(cb, cr)=exp [- 0.5 (xij-m)tc-1(xij-m)]
Wherein, m is xijAverage, c is xijVariance.
Again by colour of skin similarity max p (c maximum in regionb, cr), to p (cb, cr) it is normalized:
Normalized colour of skin similarity graph can be obtained as f (i, j)=p ' (cb, cr).Setting colour of skin similarity adaptive thresholding Value q, by following formula acquisition bianry image:
f t ( i , j ) = 1 , f ( i , j ) &greaterequal; q 0 , f ( i , j ) < q
Area of skin color maximum height c then can be obtainedmaxWith maximum width lmax, face is obtained based on Face geometric eigenvector Approximate region, step is as follows:
(1) area of skin color in vertical direction is respectively widened up and down 0.5*cmax, make this point ft(x, y) is 1, as face Region.
(2) area of skin color in horizontal direction is respectively widened up and down 0.5*lmax, make this point ft(x, y) is 1, as face Region.
Thus, it is possible to obtain human face region image, then it is identified in input following step 5.
Step 5, using the bp neural network model set up to the human face region image detection extracted and identification judge Whether it is user images.Specifically include following steps:
By the human face region image zooming-out of step 4 out after, the image procossing to this extraction and cutting, to mate nerve Network model's input picture characteristic.Then it is inputted in bp neural network model, recognition of face is carried out to it.
If bp neural network model exports y=0, this face is non-user image, shows there is invader；Sentence in identification When breaking as non-user image, it is defined as non-user invasion and sends alarm signal to user.
Otherwise, bp neural network model output y=1, is identified as user images.It is judged as user's facial image in identification When, it is defined as user and monitor video picture can be reacquired according to setting time, be i.e. return to step 2 to 5, to complete to continue Video monitoring and identification process.
On this basis, the present invention also proposes a kind of Intelligent indoor intruding detection system based on deep learning, this system Image acquisition, extraction and recognition detection can be carried out using above-mentioned detection method.This system just can be divided into according to user setup Often mode of operation and park mode.When there being visitor's visiting, user can open park mode, can save power consumption, and design is more humane. And when user leaves, activation system can be selected, open normal mode of operation so that it is monitored to household.
Specifically, described system includes:
Video monitoring module, for domestic environment is entered with Mobile state monitoring, obtains monitor video picture；
Image capture module, for obtaining the difference diagram of adjacent interframe from monitor video picture using inter-frame difference algorithm Picture；
Image zooming-out module, for carrying out binary conversion treatment to the difference image of acquired adjacent interframe, and to treated Image zooming-out afterwards obtains the Prospects For Changes image under static background；
Discriminatory analysiss module, including modeling unit, recognition unit, area extracting unit and output unit；Wherein, described build Form unit, is used for setting up bp neural network model, and the training data comprising user images according to input is to bp neutral net mould Type training；Described recognition unit, for being detected the Prospects For Changes being extracted image input bp neural network model and being known Do not judge whether humanoid；Described area extracting unit is used for when identifying that judgement has humanoid, from described Prospects For Changes figure In picture, detection and extraction obtain human face region image；Described recognition unit is additionally operable to the human face region image extracting input bp god Detected through network model and identification is determined whether user images；Described output unit, for being judged as non-use in identification It is defined as non-user invasion during the image of family, and generate and output control signal；
Transmitting element, for sending invasion prompting data according to control signal to user.
Described system, starts under normal mode of operation, video monitoring module can be under the different situations of day and night Household is monitored, preferably multiframe input picture can be collected by infrared monitoring camera electronic equipment, illumination can not be subject to Limit, to obtain monitor video picture.
And, its principle of described system is as shown in figure 3, modeling unit in discriminatory analysiss module, after setting up model, Training data to be inputted using model for the first time, bp neural network model is trained, its step includes initializing weights and threshold Value, successively adjusts weights and threshold value respectively using prp conjugate gradient algorithms, iteration is to maximum iteration time.And recognition unit, area Described in the engineering process of domain extraction unit and output unit method all described above.
In order to monitoring to household is better achieved, described system can also include alarm module, and described alarm module is used According to control signal generation sound and light alarm.If system is under normal mode of operation, when being defined as non-user invasion, to User sends invasion prompting data and by alarm module, sound and light alarm occurs；Wherein, invasion prompting data can include extracting The invader humanoid figure picture obtaining or invader's facial image, or both of which transmission.
If system is in park mode, transmitting element and alarm module all receive control signal, but do not send invasion and carry Registration evidence and generation are reported to the police.
To sum up, the Intelligent indoor intrusion detection method based on deep learning provided by the present invention and system, using being based on The bp neutral net of prp conjugate gradient algorithms, improves network training speed it is ensured that restraining correctness and ensure that real-time.Carry Take the target area input neural network detecting, be not required to identify complex background and other interference factors it is ensured that classifies is correct Property.Therefore can expeditiously complete to identify, power consumption can be saved.Can with effectively solving to user and invader, moving object with Invader can not accurately detect the problem of identification.
Above in conjunction with accompanying drawing, embodiments of the present invention are explained in detail, but the present invention is not limited to above-mentioned enforcement Mode, in the ken that those of ordinary skill in the art possess, can also be on the premise of without departing from present inventive concept Make a variety of changes.

Claims (10)

1. a kind of Intelligent indoor intrusion detection method based on deep learning is it is characterised in that comprise the following steps:
Set up bp neural network model, and according to the training data that input comprises user images, bp neural network model is trained；
The difference image of adjacent interframe is obtained using inter-frame difference algorithm from monitor video picture；
Binary conversion treatment is carried out to above-mentioned difference image, the Prospects For Changes region in the image after extraction process；
To the Prospects For Changes image detection extracted and identify it with the presence or absence of humanoid using the bp neural network model set up； When identification has humanoid, detection and extraction from described Prospects For Changes image obtains human face region image；
Using the bp neural network model set up, user is determined whether to the human face region image detection extracted and identification Image；And when identification is judged as non-user image, is defined as non-user invasion and sends alarm signal to user.
2. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that methods described Middle human face region image is obtained by following steps:
Set up based on colored complexion model, and normalization colour of skin similarity；
Using the complexion model set up to Prospects For Changes image detection, obtain normalized colour of skin similarity graph picture；
Set colour of skin similarity threshold, acquired normalized colour of skin similarity graph picture is carried out with binary conversion treatment, obtain the colour of skin Region；
According to the scope setting, area of skin color is chosen, obtain human face region image.
3. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described Middle utilization prp conjugate gradient algorithms are trained to bp neural network model.
4. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described Middle training data at least includes user's whole body images and the user's facial image possessing different attitudes.
5. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described Middle training data also includes non-humanoid image.
6. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described In when identification judge do not have humanoid when, reacquire monitor video picture.
7. according to claim 1 the Intelligent indoor intrusion detection method based on deep learning it is characterised in that: methods described In when identification is judged as user images, be defined as user and according to setting time reacquire monitor video picture.
8. a kind of Intelligent indoor intruding detection system based on deep learning is it is characterised in that include:
Video monitoring module, for domestic environment is entered with Mobile state monitoring, obtains monitor video picture；
Image capture module, for obtaining the difference image of adjacent interframe from monitor video picture using inter-frame difference algorithm；
Image zooming-out module, for carrying out binary conversion treatment to the difference image of acquired adjacent interframe, and extracts after treatment Image in Prospects For Changes region；
Discriminatory analysiss module, including modeling unit, recognition unit, area extracting unit and output unit；Wherein, described modeling is single Unit, is used for setting up bp neural network model, and according to the training data that input comprises user images, bp neural network model is instructed Practice；Described recognition unit, for inputting the Prospects For Changes being extracted image, bp neural network model is detected and identification is sentenced Break with the presence or absence of humanoid；Described area extracting unit is used for when identifying that judgement has humanoid, from described Prospects For Changes image Detection and extraction obtain human face region image；Described recognition unit is additionally operable to the human face region image extracting input bp nerve net Network model is detected and identification determines whether user images；Described output unit, for being judged as non-user figure in identification As when be defined as non-user invasion, and generate and output control signal；
Transmitting element, for sending invasion prompting data according to control signal to user.
9. according to claim 8 the Intelligent indoor intruding detection system based on deep learning it is characterised in that: described transmission The invasion prompting data that unit sends includes invader's image.
10. according to claim 8 the Intelligent indoor intruding detection system based on deep learning it is characterised in that: also include Alarm module, described alarm module is used for producing sound and light alarm according to control signal.
CN201610705858.7A 2016-08-23 2016-08-23 Deep learning-based intelligent indoor intrusion detection method and system Pending CN106372576A (en)

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