CN103514694A - Intrusion detection monitoring system - Google Patents
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Abstract
The invention relates to an intrusion detection monitoring system. The intrusion detection monitoring system comprises an image collecting module, an image processing module, a face detecting module, a background comparing module, a face identifying module, a face registering module, a user feedback module, a data storage module, an alarm management module and a user-defined module. The image collecting module obtains a static image, graying and other processing are conducted on the static image through the image processing module, then the image enters the face detecting module for face detection, and if the face is detected, whether the face is a stranger is judged through the face identifying module; if no face can be detected, whether article and background changes and other abnormal changes exist in the image are detected through the background comparing module. If the stranger or the abnormal changes are found, alarm information and image information are sent to a scheduled user through the alarm management module for alarm. The intrusion detection monitoring system is easy to achieve and low in cost, can automatically monitor intrusion detection of the stranger and the article changes in a specific place, and can effectively improve safety protection of the monitored place.
Description
Technical field
The present invention relates to a kind of intrusion detection supervisory system, particularly a kind of the image of guarded region is carried out to examination and controlling and in the system that is occurring personnel or object change alarm.
Background technology
In order to protect better property safety, the information security of individual, family and enterprise, a lot of places are all equipped with safety prevention measure.Traditional security protection mainly relies on manpower, as set up entrance guard to register, establishment officer goes on patrol etc.; Because this mode can not guarantee each region, there is at any time guard, easily occur security breaches.In recent years, along with computing machine, network and image are processed, the develop rapidly of transmission technology, Video Supervision Technique is widely used in various safe and secret occasions, as domestic video monitoring, office areas monitoring etc.By automatic video monitoring, the occasion that can cannot directly observe people, in good time, clear, to reflect truly monitored object picture, has saved the manpower and materials that need input greatly.
But still a kind of artificial monitor mode in essence that present most of video monitoring adopts, by the environment in guarded region is recorded a video, rely on monitor staff's naked eyes to note abnormalities, and cannot automatically detect the people in region, cannot automatically identify stranger, then take corresponding security protection measure.This artificial monitor mode cost is large compared with high, very flexible, limitation, cannot meet user's real demand.Current computer technology develop the development that has greatly driven Video Supervision Technique rapidly, the correlation technique ground in the field such as pattern-recognition, computer vision is introduced, greatly improved the intellectuality of video monitoring system, especially people's face detects recognition technology, has obtained extensive application in intelligent video monitoring system.
People's face detects, identification has the advantage such as gather convenient and good concealment, equipment cost is cheap, antifalsification is good, and intelligent video monitoring system only need to add people's face to detect identification module and a face database just automatically to detect and identify strange personnel and when the strange personnel of discovery, carry out early warning on the basis of original video monitoring system.Although through research for many years, people's face detects recognition technology and obtained good performance under controllable environment, also exists considerable problem in its specific implementation process.Facial image is as a kind of comparatively complicated pattern, is easy to be subject to illumination, attitude, blocks, the interference of appearance adjunct and image capture device etc. in gatherer process, greatly affected the apparent of facial image, thereby made recognition performance stable not.Because carry out people's face, automatically detect the front face that identification often needs to collect identified person, require identified person as far as possible over against camera, but for invasion personnel such as the thief in intelligent monitor system, terrorists, above-mentioned acquisition condition is difficult to meet, cause system to be difficult to people's face from real-time image, be detected, even if people's face detected because acquisition condition impact is also difficult to accurately identification.So in intelligent video monitoring, only end user's face detects recognition technology simply, for real-time dynamic intrusion detection and identification or inadequate.
Summary of the invention
Object of the present invention is for the problem existing in above-mentioned technology, a kind of intrusion detection supervisory system is provided, from people He Wuliangge aspect, set about simultaneously, by personnel or object change that guarded region is occurred, carry out examination and controlling, reduce the harmful effect that image acquisition condition causes system, improve the security protection efficiency of guarded region.
For achieving the above object, the technical solution used in the present invention is: a kind of intrusion detection supervisory system comprises:
Image capture module, for obtaining still image, and is transferred to image processing module.
Image processing module, for still image is carried out to pre-service, obtains gray level image; Pre-service comprises the processing such as gray processing, illumination compensation.
People's face detection module, for carrying out the detection of people's face and location to gray level image; If the Gray Face image that people's face obtains fixed size detected, and be transferred to face recognition module; If people's face detected, directly gray level image is not transferred to background contrast module;
Background contrast module, for the gray level image that the average background image of data memory module storage and people's face detection module are transmitted, be divided into respectively the subimage of several weighteds, calculate the similarity of corresponding subimage, and the similarity of all subimages is weighted to summation, if weighted sum result is greater than default similarity threshold values, as abnormal gray level image being detected, store data memory module into, and notify alarming and managing module to report to the police.
Face recognition module, for the registered facial image of Gray Face image and data memory module storage is carried out feature extraction, calculates similarity, determines whether stranger according to similarity, if stranger notifies alarming and managing module to report to the police.
People's face Registering modules, for detecting in real time Gray Face image, the row labels of going forward side by side.
User feedback module, for being registered facial image by the Gray Face Image Adjusting that is mistaken for stranger.
Data memory module, for storing registered facial image, is judged to stranger's Gray Face image, abnormal gray level image detected, average background image and customized information.
Alarming and managing module, for when receiving the alert notice of background contrast module or face recognition module, warning message is sent to and presetted user by note, and the Gray Face image that abnormal gray level image detected or be judged to stranger is sent to and presetted user by network mail, also can remind addressee by the mobile phone reminding function of mailbox.
User defined logic interface, for arrange background contrast module subimage dividing mode, the shared weight of each subimage, similarity threshold values, receive subscriber phone and the network email address of warning message.
Further, described image capture module is camera.
Further, described average background image is the picture that gathers same fixed background under several different light environment, averages to obtain.When fixed background changes, have object when change, user need to upgrade in time.
Further, described background contrast module has first been carried out local binary patterns conversion to image when calculating the similarity of corresponding subimage, the impact bringing in order to reduce the variations such as illumination, the image difference that reduces that illumination variation is caused is mistaken as abnormal possibility.
Further, the subimage division in described background contrast module, the weight of each sub-image area, the parameters such as similarity threshold values between image need user to preset.Described system is first divided into average background image according to user's requirement the subimage of several equal and opposite in direction non-overlapping copies, user can merge mutually contiguous several number of sub images to obtain a new subimage as required, while user can give larger weight to the shared sub-image area of object of import and export position (comprising door and window, passageway etc.) and needs monitoring and carry out key monitoring, can set three weight grades according to the importance of regional, value is respectively 1,2,3, and numerical value shows that more greatly shared weight is larger; Similarity threshold values between image starts set strictlyer, to reduce that stranger is identified as to registered possibility by mistake, along with user's continuous feedback in use, described system is more and more detailed to registered user's description, progressively relax again the setting of threshold values, reduce registered user's misclassification rate, as started to suppose certain width image
in number of sub images piece, have and more than 10% be considered to data memory module storage
registered facial image dissmilarity this image is stranger or unregistered, and this scope can expand 20%-30% in the back.
Further, described face recognition module has been used the people's face method based on stochastic subspace when carrying out feature extraction, calculating similarity, comprises the steps:
A) to Gray Face image to be identified
with data memory module storage
registered facial image
(
) carry out local binary patterns conversion, the impact bringing in order to reduce the variations such as illumination;
B) by step a) image after converting be divided into
the subimage block of the identical and non-overlapping copies of individual size;
C) to Gray Face image to be identified
each subimage block
subimage block with the registered facial image of all correspondences
carry out stochastic sampling, select at random
with
the numerical value of several same positions, obtains test feature subset
with training characteristics subset
, wherein
,
;
D) calculate hamming distance (the Hamming Distance between test feature subset and training characteristics subset, HD) as measuring similarity, if the hamming distance between test feature subset and the most similar training characteristics subset surpasses predefined similarity threshold values, think the subimage block of this stochastic sampling representative
with
(
) dissmilarity;
E) step c) and d) repeat
inferior, carry out
inferior stochastic sampling, adds up Gray Face image to be identified
's
the dissimilar number of times that number of sub images piece obtains altogether, thinks Gray Face image to be identified if dissimilar number of times surpasses reservation threshold
with data memory module storage
registered facial image is all dissimilar, is stranger or unregistered, otherwise is registered.
Further, when described people's face Registering modules detects Gray Face image, people's face if unsuccessful, detected, Resurvey image; If people's face successfully detected, identified by face recognition module.With data memory module in the registration facial image stored contrast, prevent repeated registration.People's face Registering modules is subject to illumination, attitude, the impact of condition such as expresses one's feelings, blocks and the repeatedly registration that causes can not correctly identify time is not repeated registration owing to gathering the facial image of same person.
Further, described alarming and managing module, except note and the warning of network mailbox, also arranges loudspeaker and carries out audible alarm, for reminding the other staff in monitoring place.
In sum, the beneficial effect that the present invention can reach is:
1. take into full account the impact that man face image acquiring condition is brought to recognition of face, a kind of feedback mechanism is provided, constantly improved the description of described system to registered user, can effectively improve recognition efficiency and accuracy rate.
2. from the essential object of usage monitoring System Assurance property and information security, utilization detects the supplementary means of identification as people's face to the monitoring of certain objects and fixed background, when changing, the position of object or background report to the police, improve the probability of finding strange personnel's invasion, strengthened the security protection in monitoring place.
3. according to the difference of monitoring place and monitoring condition, user can adjust key monitoring region, monitoring grade etc. voluntarily by revising the parameters such as subimage division, the shared weight of subimage, image similarity threshold values, and provide multiple type of alarm, a kind of solution of personalization is provided to user, meets user's actual demand.
Accompanying drawing explanation
Fig. 1 is the modular structure figure of intrusion detection supervisory system of the present invention;
Fig. 2 is the one action process flow diagram of intrusion detection supervisory system of the present invention;
Fig. 3 is the process flow diagram of background contrast module in Fig. 1;
Fig. 4 is the process flow diagram of people's face Registering modules in Fig. 1.
Embodiment
Below in conjunction with accompanying drawing, the present invention will be further described.Below explanation, just in order better to understand the present invention, is not that protection scope of the present invention is limited.
With reference to figure 1, intrusion detection supervisory system described in the present invention, comprises image capture module 101, image processing module 102, people's face detection module 103, background contrast module 104, face recognition module 105, people's face Registering modules 106, user feedback module 107, data memory module 108, alarming and managing module 109 and user defined logic interface 110.
Described image capture module 101 is cameras, intercepts a frame frame still image and be transferred to described image processing module 102 and carry out gray processing and illumination compensation from the video flowing of monitoring
[1]after processing, obtain gray level image.
The gray level image that 103 pairs of image processing modules 102 of described people's face detection module obtain detects, if detect people's face, from gray level image, is partitioned into the facial image of fixed size; If detect unsuccessfully, carry out background contrast or the real-time still image of reminding user Resurvey.
Described user feedback module 107 can be the facial image of registration by the Gray Face Image Adjusting that is mistaken as stranger in data memory module 108, makes registered user increase again a different facial image and describes.
The stranger's who detects in the abnormal gray level image detecting in Gray Face image, average background image and background contrast module 104 in described data memory module 108 storage people face Registering modules 106, face recognition module 105 Gray Face image and the information in user defined logic interface 110.
Described alarming and managing module 109 provides multiple type of alarm, when face recognition module 105 detects stranger or background contrast module 104 and object, change of background detected, alarming and managing module 109 can provide local sound early warning, mobile phone short message to report to the police and network mailbox is reported to the police, wherein can utilize the mobile phone reminding function of network mailbox, user mobile phone and network mailbox are bound, by network mail, warning message and pictorial information can be sent to predesignated subscriber and no longer need to notify user by mobile phone short message.
Described user defined logic interface 110 provides the parameter informations such as registration facial image in subimage division in background contrast module 104, the shared weight of subimage, similarity threshold values, face recognition module 105 and the subscriber phone in alarming and managing module 109, network email address.
With reference to figure 2, it has provided the main flow process of intrusion detection supervisory system of the present invention.First, from image capture module 101, obtain a width realtime graphic at regular intervals, then by entering people's face detection module 103 after the processing such as image processing module 102 gray processings, carry out the detection of people's face and location, if people's face detected, by face recognition module 105 visit data memory modules 108, identify, determine whether stranger; If whether can't detect people's face is contrasted in module 104 detection pictures and is existed object, change of background etc. abnormal by background.If find stranger or detect extremely, generating warning message, and send to predesignated subscriber to report to the police warning message and pictorial information by alarm module 109.
With reference to figure 3, it has provided the process flow diagram of the background contrast module 104 described in Fig. 1.Described background contrast module 104 is when people's face detection module detects the failure of people's face, first by detecting failed image, has carried out local binary patterns conversion with average background image
[2], the image difference occurring while as far as possible reducing to change due to acquisition conditions such as illumination, according to the setting in user defined logic interface, the two is divided into the subimage of several non-overlapping copies and gives corresponding weight again, general subimage weight can be made as 1, door and window, the import and export positions such as passageway and subimage weight corresponding to object that need to monitor can be made as 2 or 3 by importance, then calculate the similarity between the two correspondence position subimage and carry out weighted sum, if the Weighted Similarity of gained surpasses predefined similarity threshold values, think that variation has occurred monitored object or fixed background, there is stranger to invade, then produce warning message, and unsuccessfully exist abnormal image to pass to alarming and managing module with detecting warning message.
With reference to figure 4, it has provided the process flow diagram of the people's face Registering modules 106 described in Fig. 1.First, image capture module gathers the real-time still image of a width, processes laggard pedestrian's face detect by image processing module, people's face detected if fail, and reminding user is used image capture module Resurvey piece image, and re-starts detection; If people's face detected, by people's face Registering modules visit data memory module, identify, identify and successfully illustrate that user registers, can not repeated registration, recognition failures explanation is also unregistered, and registers.
list of references
[1] Chen Hengxin. the variation illumination human face Study of recognition [D] based on multiscale analysis. Chongqing: University Of Chongqing, 2010.
[2]?Huang?Di,?Shan?Caifeng,?Ardabilian?M.?Local?Binary?Patterns?and?its?application?to?facial?image?analysis:?a?survey[J].?IEEE?Transactions?on?Systems,?Man,?and?Cybernetics,?Part?C:?Applications?and?Reviews,?2011,?41(6):?765-781。
Claims (8)
1. an intrusion detection supervisory system, is characterized in that, comprising:
Image capture module, for obtaining still image, and is transferred to image processing module;
Image processing module, for still image is carried out to pre-service, obtains gray level image;
People's face detection module, for carrying out the detection of people's face and location to gray level image; If the Gray Face image that people's face obtains fixed size detected, and be transferred to face recognition module; If people's face detected, directly gray level image is not transferred to background contrast module;
Background contrast module, for the gray level image that the average background image of data memory module storage and people's face detection module are transmitted, be divided into respectively the subimage of several weighteds, calculate the similarity of corresponding subimage, and the similarity of all subimages is weighted to summation, if weighted sum result is greater than default similarity threshold values, as abnormal gray level image being detected, store data memory module into, and notify alarming and managing module to report to the police;
Face recognition module, for the registered facial image of Gray Face image and data memory module storage is carried out feature extraction, calculates similarity, determines whether stranger according to similarity, if stranger notifies alarming and managing module to report to the police;
People's face Registering modules, for detecting in real time Gray Face image, the row labels of going forward side by side;
User feedback module, for being registered facial image by the Gray Face Image Adjusting that is mistaken for stranger;
Data memory module, for storing registered facial image, be judged to stranger Gray Face image, abnormal gray level image, average background image and customized information detected;
Alarming and managing module, for when receiving the alert notice of background contrast module or face recognition module, warning message is sent to and presetted user by note, and the Gray Face image that abnormal gray level image detected or be judged to stranger is sent to and presetted user by network mail.
2. a kind of intrusion detection supervisory system according to claim 1, it is characterized in that: also comprise user defined logic interface, for arrange background contrast module subimage dividing mode, the shared weight of each subimage, similarity threshold values, receive subscriber phone and the network email address of warning message.
3. according to a kind of intrusion detection supervisory system described in claim 1 or 2, it is characterized in that: described image capture module is camera.
4. a kind of intrusion detection supervisory system according to claim 1, is characterized in that: described average background image is the picture that gathers same fixed background under several different light environment, averages to obtain.
5. a kind of intrusion detection supervisory system according to claim 1, is characterized in that: described background contrast module has first been carried out local binary patterns conversion to image when calculating the similarity of corresponding subimage.
6. a kind of intrusion detection supervisory system according to claim 1, is characterized in that: described face recognition module has been used the people's face method based on stochastic subspace carrying out feature extraction, while calculating similarity, comprises the steps:
A) to Gray Face image to be identified
with data memory module storage
registered facial image
,
carry out local binary patterns conversion;
B) by step a) image after converting be divided into
the subimage block of the identical and non-overlapping copies of individual size;
C) to Gray Face image to be identified
each subimage block
subimage block with the registered facial image of all correspondences
carry out stochastic sampling and obtain test feature subset and training characteristics subset, wherein
;
D) distance of the hamming between calculating test feature subset and training characteristics subset is as measuring similarity, if the hamming distance between test feature subset and the most similar training characteristics subset surpasses predefined similarity threshold values, think the subimage block of this stochastic sampling representative
with
dissmilarity, wherein
;
E) step c) and d) repeat
inferior, carry out
inferior stochastic sampling, adds up Gray Face image to be identified
's
the dissimilar number of times that number of sub images piece obtains altogether, thinks Gray Face image to be identified if dissimilar number of times surpasses reservation threshold
with data memory module storage
registered facial image is all dissimilar, is stranger or unregistered, otherwise is registered.
7. a kind of intrusion detection supervisory system according to claim 1, is characterized in that: when described people's face Registering modules detects Gray Face image, people's face detected, Resurvey image if unsuccessful; If people's face successfully detected, identified by face recognition module.
8. a kind of intrusion detection supervisory system according to claim 1, is characterized in that: described alarming and managing module also arranges loudspeaker and carries out audible alarm.
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