CN109389793A - A kind of super anti-theft monitoring system of quotient - Google Patents
A kind of super anti-theft monitoring system of quotient Download PDFInfo
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- CN109389793A CN109389793A CN201811204789.7A CN201811204789A CN109389793A CN 109389793 A CN109389793 A CN 109389793A CN 201811204789 A CN201811204789 A CN 201811204789A CN 109389793 A CN109389793 A CN 109389793A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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Abstract
The invention discloses a kind of super anti-theft monitoring systems of quotient, the system includes being respectively arranged at each point to be monitored, facial image candid photograph and real-time video capture are carried out for treating monitoring point crowd, and by image and vision signal real-time Transmission to processor video camera, be used to carry out processing identification to the facial image signal that camera transmissions come, judge face processor whether corresponding with the face in processor black list database and display terminal in image.The super anti-theft monitoring system of quotient of the invention passes through in the crucial intelligent camera for entering and leaving bayonet, key area, the face head portrait for carrying out discrepancy crowd is captured, by carrying out recognition of face to the image of candid photograph, obtain face characteristic data, it is compared with the human face data of the thief imported in black list database, thief can be quickly and effectively screened, timely and effective prevention and the pilferage behavior of prevention thief occur.
Description
Technical field
The present invention relates to supermarket's field of intelligent monitoring, and in particular to a kind of super anti-theft monitoring system of quotient.
Background technique
As urban life rhythm gradually becomes faster, large supermarket multiple functional, that the source of goods is complete has gradually replaced traditional
Department stores become the major consumers place of urban human fast pace life.Large supermarket has considerable scale, carries out nobody and sells
Goods provides free good environment for customer.But it is creating the complete purchase of comfortable light, safety clean, the source of goods for customer
While substance environment, and the problem of bring a headache to businessman --- merchandise theft exists with fashion chain store and free supermarket
Domestic is increasingly prevailing, and commodity Loss is also on the rise, and how to prevent commodity stolen, and protection market safety is by more next
The concern of more retailers.Enterprise takes a large amount of time and manpower and money and goes tracking pilferage situation similar with prevention
Occur.
In the super antitheft link of quotient, the prevention of habitual offender, confirmed thief seem even more important.In traditional supermarket's monitoring, generally
It is guard using the shortcomings that bayonet, important area install camera, and monitoring room artificially stares at the mode kept, this mode
It is difficult effectively to screen thief, is stolen along with quotient is extra small and often flee about to commit crimes, be good at pretending, caregiver is more helpless.Quotient
Super monitoring field is badly in need of a kind of intelligent anti-theft monitoring system, avoids property loss.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of super anti-theft monitoring system of quotient, pass through people to solve traditional monitor mode
Member guard can not effectively screen commodity loss, property loss problem caused by thief.
The purpose of the present invention is realized using following technical scheme:
A kind of super anti-theft monitoring system of quotient, the anti-theft monitoring system include video camera, processor and display terminal, described to take the photograph
Camera carries out facial image candid photograph and real-time video capture for being arranged at each point to be monitored, to treat monitoring point crowd, and
Give facial image and vision signal real-time Transmission to the processor；The processor is for passing through the face to camera transmissions
Image carries out processing identification, judges whether the face in facial image is corresponding with the face in processor black list database, such as
Fruit is corresponding, then determines that the face is to identify target, its corresponding identity information is transferred to display terminal；The display terminal
Setting is used to receive the identity information for the identification target that the processor is sent at monitoring backstage, and carries out real-time display.
The utility model has the advantages that (1) the present invention is based on the super anti-theft monitoring systems of the quotient of recognition of face by entering and leaving bayonet, again in key
Video camera is installed in point region, and the face head portrait for carrying out discrepancy crowd is captured, and by carrying out recognition of face to the image of candid photograph, is obtained
Face characteristic data are compared with the human face data of the thief imported in black list database, can quickly and effectively screen small
Steathily, timely and effective prevention and the pilferage behavior of prevention thief occur.The system improves small compared with traditional supermarket's monitor mode
The efficiency and accuracy identified steathily, realizes that quotient is super and the intelligence of other commercial buildings is antitheft, to the intelligence of a suspect
Recognition of face greatly improves commercial space anti-theft horizontal and efficiency.
(2) increase the generation function of warning message in processor, and warning message is exported to display terminal and shown
Alarm is popped up on interface, can more rapidly be found thief's information that monitoring information identifies, be played the role of fright to thief.
(3) video information number that processor can also simultaneously carry out all camera transmissions is analyzed, and obtains signal mesh
Target real time position and mobile trajectory data are realized automatically tracking for track, and are exported to display terminal, after identifying thief
In the video pictures and action trail figure of display terminal real-time display thief, efficiency is further improved.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is the structure chart of the super anti-theft monitoring system of quotient of the present invention；
Fig. 2 is the frame construction drawing of processor 2；
Fig. 3 is the frame construction drawing of human face analysis module 4.
Appended drawing reference: video camera 1；Processor 2；Display terminal 3；Human face analysis module 4；Face characteristic contrast module 5；It is black
List data library 6；Alarm module 7；Video analysis tracing module 8；Facial image pre-processes submodule 9；Facial image feature mentions
Take submodule 10；Gray processing unit 11；Denoise unit 12；Enhancement unit 13.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of super anti-theft monitoring system of quotient, the anti-theft monitoring system includes video camera 1, processor 2 and display
Terminal 3, the video camera 1 are arranged at each point to be monitored, carry out facial image candid photograph and in real time view to treat monitoring point crowd
Frequency is shot, and gives facial image and vision signal real-time Transmission to the processor 2；The processor 2 is used for by camera shooting
The facial image that machine 1 transmits carries out processing identification, judge face in facial image whether the black list database with processor 2
In face it is corresponding, if it does correspond, then determining that the face is to identify target, its corresponding identity information is transferred to display eventually
End 3；At monitoring backstage, the display terminal 3 is used to receive the identification that the processor 2 is sent for the setting of display terminal 3
The identity information of target, and carry out real-time display.
The utility model has the advantages that (1) the present invention is based on the super anti-theft monitoring systems of the quotient of recognition of face by entering and leaving bayonet, again in key
Video camera is installed in point region, and the face head portrait for carrying out discrepancy crowd is captured, and by carrying out recognition of face to the image of candid photograph, is obtained
Face characteristic data are compared with the human face data of the thief imported in black list database, can quickly and effectively screen small
Steathily, timely and effective prevention and the pilferage behavior of prevention thief occur.The system improves small compared with traditional supermarket's monitor mode
The efficiency and accuracy identified steathily, realizes that quotient is super and the intelligence of other commercial buildings is antitheft, to the intelligence of a suspect
Recognition of face greatly improves commercial space anti-theft horizontal and efficiency.
(2) increase the generation function of warning message in processor, and warning message is exported to display terminal and shown
Alarm is popped up on interface, can more rapidly be found thief's information that monitoring information identifies, be played the role of fright to thief.
(3) video information number that processor can also simultaneously carry out all camera transmissions is analyzed, and obtains signal mesh
Target real time position and mobile trajectory data are realized automatically tracking for track, and are exported to display terminal, after identifying thief
In the video pictures and action trail figure of display terminal real-time display thief, efficiency is further improved.
Preferably, referring to fig. 2, the processor 2 includes human face analysis module 4, face characteristic contrast module 5, blacklist
Database 6, alarm module 7 and video analysis tracing module 8；
The human face analysis module 4, the facial image for transmitting to video camera 1 are analyzed, and the face figure is obtained
The face feature vector of picture；
The black list database 6, for storing the face feature vector of stealing personnel；
The face characteristic contrast module 5, for obtained face feature vector and the black list database 6 will to be extracted
In the face feature vector that prestores be compared, judge the face whether with the face pair that is prestored in the black list database 6
It answers, if it does correspond, then determining that the face is to identify target, and its corresponding identity information is transferred to display terminal；
The alarm module 7 for generating warning message after obtaining the identification target, and is exported to display terminal 3；
The video analysis tracking module 8, the vision signal for coming to all camera transmissions are analyzed, and are obtained and are known
The real time position and mobile trajectory data of other target, and export to display terminal.
Preferably, referring to Fig. 3, the human face analysis module 4 includes that facial image pretreatment submodule 9 and facial image are special
Levy extracting sub-module 10.The facial image pre-processes submodule 9, and the facial image for transmitting to video camera 1 is located in advance
Reason；The facial image feature extraction submodule 10, for being extracted in the facial image from pretreated facial image
Face feature vector.
Preferably, the facial image pretreatment submodule 9 includes gray processing unit 11, denoising unit 12 and enhancement unit
13；
The gray processing unit 11, the facial image for transmitting to video camera 1 carry out gray processing processing；The denoising is single
Member 12, for removing the random noise in the facial image after gray processing；The enhancement unit 12, for the face after denoising
Image carries out enhancing processing.
Preferably, the random noise in the facial image after the removal gray processing, specifically:
(1) K layers of wavelet decomposition are carried out to the facial image after gray processing using wavelet transformation, obtains one group of wavelet coefficient；
(2) threshold process is carried out to the wavelet coefficient of each decomposition layer respectively using thresholding functions, wherein kth layer
The thresholding functions of wavelet coefficient are as follows:
In formula, b 'J, kFor j-th of wavelet coefficient of the kth layer after denoising, bJ, kFor j-th of small echo of the kth layer before denoising
Coefficient, λ1, kFor the bottom threshold value of the kth layer wavelet coefficient of setting, λ2, kFor the upper threshold of the kth layer wavelet coefficient of setting
Value, and λ2, k=ζ λ1, k, ζ is a proportionality coefficient, meets 0 < ζ < 1, and a, η are form factor, and sgn (r) is sign function, works as r
When for positive number, 1 is taken, when being negative, takes -1；
(3) wavelet coefficient after denoising is reconstructed using wavelet transformation, the facial image after being denoised.
The utility model has the advantages that carrying out threshold process, the threshold to the wavelet coefficient of different decomposition layer respectively using thresholding functions
Value Processing Algorithm can adaptively remove the random noise in facial image according to the wavelet coefficient of each decomposition layer；In threshold value
It handles in function, a, η are form factor, which is used to control the shape of threshold function table in each section, i.e. control decaying journey
Degree；According to λ1, k、λ2, kWith bJ, kAbsolute value size relation, select different threshold function tables to be denoised, which can have
Random noise in effect ground removal facial image, retains the effective information in facial image, while the thresholding functions are in λ1, k
And λ2, kPlace is continuous, the additional concussion that the facial image after capable of effectively avoiding denoising generates, in Near Threshold, the threshold process
Function has preferable smooth transition band, so that the facial image after the denoising arrived is closer to true picture, after being conducive to
Accurately identifying for the continuous personnel identity to candid photograph, improves the recognition accuracy of the super burglary-resisting system of the quotient.
In the embodiment that one can be realized, the facial image of candid photograph is carried out at denoising using the method for threshold value
Reason, can be by setting fixed a upper threshold value and bottom threshold value
In one more preferably embodiment, by solving the upper threshold value of each decomposition layer, and then realize to face
The denoising process of image.Wherein, upper threshold value can be calculated using following formula:
In formula, λ2, kFor the upper threshold value of kth layer wavelet coefficient, CJ, kFor j-th of wavelet systems of kth layer wavelet coefficient
Number, JkFor the number of kth layer wavelet coefficient,For the average value of all wavelet coefficients, ξ1、ξ2For weight coefficient, meet ξ1+ξ2
=1.
The utility model has the advantages that when solving the upper threshold value of each decomposition layer, by the average value for seeking all wavelet coefficients
And the mean value of the quadratic sum of kth layer wavelet coefficient, and then the upper threshold value of kth layer wavelet coefficient is solved, which can
The case where according to each decomposition layer adaptive each layer of determination of upper threshold value and bottom threshold value, and then select different
To realize denoising, which avoids setting fixed threshold bring noise wavelet coefficients quilt for upper threshold value and bottom threshold value
It remains, and to still remain much noise in the facial image after denoising, while also avoiding useful wavelet systems
Number treats as noise information, and makes the target after denoising too smooth, has lost detailed information；And different threshold values is selected to be gone
It makes an uproar and also improves the accuracy of denoising.
Preferably, the facial image after described pair of denoising carries out enhancing processing, specifically:
(1) facial image after denoising is transformed from a spatial domain into fuzzy field using customized subordinating degree function, and counted
All pixels point is subordinate to angle value in facial image after calculating denoising, wherein customized subordinating degree function are as follows:
In formula, μxyIt is the angle value that is subordinate to of the pixel at coordinate (x, y), pxyBe denoising after facial image in coordinate (x,
Y) gray value of the pixel at place, pTFor preset threshold value, P is the maximum gradation value in the facial image after denoising；
(2) in fuzzy field, it can be modified, be obtained by the angle value that is subordinate to of the nonlinear transformation to obtained each pixel
It is subordinate to angle value to each pixel is revised, wherein customized nonlinear transformation formula are as follows:
In formula, μ 'xyRevised for the pixel at coordinate (x, y) is subordinate to angle value, μxyFor the picture at coordinate (x, y)
Vegetarian refreshments is subordinate to angle value, μTFor pTIt is corresponding to be subordinate to angle value, μTIt can be calculated by the subordinating degree function of step (1)；
(3) the gray value for being subordinate to angle value and being converted to respective pixel point of revised pixel, after obtaining enhanced fuzzy
Facial image, wherein revised the pixel at coordinate (x, y) is subordinate to angle value μ 'xyBe converted to its gray value p 'xy
Formula are as follows:
In formula, p 'xyIt is the gray value of the pixel at the coordinate (x, y) after inverse transformation；
All pixels point in fuzzy field is traversed, the set that all pixels point is constituted after inverse transformation is enhanced face
Image.
The utility model has the advantages that the facial image after denoising transformed from a spatial domain to using customized subordinating degree function fuzzy
Domain is allowed in fuzzy field, and each pixel gray value is mapped in [0,1] section；By setting a threshold value pT, after denoising
Facial image is divided into the higher region of gray level and the lower region of gray level, and respectively in the two regions with different persons in servitude
Pixel is subordinate to angle value in category degree function domain, and the lower part of gray level can be weakened by doing so, make corresponding picture
The gray level of vegetarian refreshments is lower, while enhancing the higher part of gray level, keep the gray level of corresponding pixel higher, is reached with this
To the purpose of image enhancement；By completing the enhancing processing to the facial image after denoising in fuzzy field, so that after denoising
Facial image is effectively enhanced, and while so that entire enhanced facial image brightens, can preferably retain face figure
Minutia as in, is conducive to subsequent feature extraction and identification to facial image, convenient for the subsequent facial image to candid photograph
Accurately identify, effectively screen out the identity of thief, and then effectively prevent and prevents the generation for stealing behavior of thief.
Preferably, the face characteristic that obtained face feature vector will be extracted and prestored in the black list database
Vector is compared, and judges whether the face is corresponding with the face prestored in the black list database, if it does correspond, then determining
The face is to identify target, and its corresponding identity information is transferred to display terminal, specifically: the face for obtaining extraction
Feature vectorWith the face feature vector prestored in the black list databaseIt is compared, if metThen judge that the face is to identify target, and its corresponding identity information is transferred to display terminal, whereinFor the face feature vector of the facial image of camera transmissions,For the face characteristic that is prestored in the black list database to
Amount, δ are the customized similarity factor.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (6)
1. a kind of super anti-theft monitoring system of quotient, which is characterized in that including video camera, processor and display terminal, the video camera,
It is arranged at each point to be monitored, carries out facial image candid photograph and real-time video capture to treat monitoring point crowd, and by face figure
Picture and vision signal real-time Transmission give the processor；The processor is used to carry out by the facial image to camera transmissions
Processing identification, judges whether the face in facial image is corresponding with the face in processor black list database, if it does correspond, then
Determine that the face is to identify target, and its corresponding identity information is transferred to display terminal；The terminal exists
It at monitoring backstage, is used to receive the identity information for the identification target that the processor is sent, and carries out real-time display.
2. the super anti-theft monitoring system of quotient according to claim 1, which is characterized in that the processor includes human face analysis mould
Block, face characteristic contrast module, black list database, alarm module and video analysis tracing module；
The human face analysis module, is analyzed for the facial image to camera transmissions, obtains the people of the facial image
Face feature vector；
The black list database, for storing the face feature vector of stealing personnel；
The face characteristic contrast module, for will extract obtained face feature vector and prestored in the black list database
Face feature vector be compared, judge whether the face corresponding with the face prestored in the black list database, if
It is corresponding, then determine that the face is to identify target, and its corresponding identity information is transferred to display terminal；
The alarm module for generating warning message after obtaining the identification target, and is exported to display terminal；
The video analysis tracking module, the vision signal for coming to all camera transmissions are analyzed, and identification mesh is obtained
Target real time position and mobile trajectory data, and export to display terminal.
3. the super anti-theft monitoring system of quotient according to claim 2, which is characterized in that the human face analysis module includes face
Image preprocessing submodule and facial image feature extraction submodule；
The facial image pre-processes submodule, pre-processes for the facial image to camera transmissions；
The facial image feature extraction submodule, for from the people extracted in pretreated facial image in the facial image
Face feature vector.
4. the super anti-theft monitoring system of quotient according to claim 3, which is characterized in that the facial image pre-processes submodule
Including gray processing unit, denoising unit and enhancement unit；
The gray processing unit carries out gray processing processing for the facial image to camera transmissions；
The denoising unit, for removing the random noise in the facial image after gray processing；
The enhancement unit, for carrying out enhancing processing to the facial image after denoising.
5. the super anti-theft monitoring system of quotient according to claim 4, which is characterized in that the face figure after the removal gray processing
Random noise as in, specifically:
(1) K layers of wavelet decomposition are carried out to the facial image after gray processing using wavelet transformation, obtains one group of wavelet coefficient；
(2) threshold process is carried out to the wavelet coefficient of each decomposition layer respectively using thresholding functions, wherein kth layer small echo
The thresholding functions of coefficient are as follows:
In formula, b 'j,kFor j-th of wavelet coefficient of the kth layer after denoising, bj,kFor j-th of wavelet systems of the kth layer before denoising
Number, λ1,kFor the bottom threshold value of the kth layer wavelet coefficient of setting, λ2,kFor the upper threshold value of the kth layer wavelet coefficient of setting,
And λ2,k=ζ λ1,k, ζ is a proportionality coefficient, meets 0 < ζ < 1, and a, η are form factor, and sgn (r) is sign function, when r is
When positive number, 1 is taken, when being negative, takes -1；
(3) wavelet coefficient after denoising is reconstructed using wavelet transformation, the facial image after being denoised.
6. the super anti-theft monitoring system of quotient according to claim 5, which is characterized in that the face characteristic for obtaining extraction
The face feature vector prestored in black list database described in vector sum is compared, judge the face whether with the blacklist
The face that prestores in database is corresponding, if it does correspond, then determining that the face is to identify target, and by its corresponding identity information
It is transferred to display terminal, specifically: the face feature vector for obtaining extractionWith the face prestored in the black list database
Feature vectorIt is compared, if metThen judge that the face is to identify target, and by its corresponding body
Part information is transferred to display terminal, whereinFor the face feature vector of the facial image of camera transmissions,For the black name
The face feature vector prestored in single database, δ are the customized similarity factor.
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| CN110379108A (en) * | 2019-08-19 | 2019-10-25 | 铂纳思(东莞)高新科技投资有限公司 | A kind of method and its system of unmanned shop anti-thefting monitoring |
| CN110688912A (en) * | 2019-09-09 | 2020-01-14 | 南昌大学 | IPv6 cloud interconnection-based online face search positioning system and method |
| CN110993122A (en) * | 2019-11-01 | 2020-04-10 | 广东炬海科技股份有限公司 | Medical health information management system based on cloud computing |
| CN111783594A (en) * | 2020-06-23 | 2020-10-16 | 杭州海康威视数字技术股份有限公司 | Alarm method and device and electronic equipment |
| CN112422499A (en) * | 2020-09-14 | 2021-02-26 | 深圳英飞拓科技股份有限公司 | Method and system for identifying black and white lists of human face based on 5G transmission |
| CN113378622A (en) * | 2021-04-06 | 2021-09-10 | 青岛以萨数据技术有限公司 | Specific person identification method, device, system and medium |
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