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CN116092261A - Regional intelligent security monitoring rapid identification analysis system - Google Patents

Regional intelligent security monitoring rapid identification analysis system Download PDF

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Publication number
CN116092261A
CN116092261A CN202310040380.0A CN202310040380A CN116092261A CN 116092261 A CN116092261 A CN 116092261A CN 202310040380 A CN202310040380 A CN 202310040380A CN 116092261 A CN116092261 A CN 116092261A
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module
security monitoring
image
face
analysis system
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谢光胜
房振辉
王启明
葛洪亮
张年龙
张辉
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Anhui Huilian Information Technology Co ltd
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Anhui Huilian Information Technology Co ltd
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    • G01—MEASURING; TESTING
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    • G06V20/00—Scenes; Scene-specific elements
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    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/002—Generating a prealarm to the central station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

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Abstract

The invention discloses a regional intelligent security monitoring rapid identification analysis system, which relates to the technical field of security monitoring and comprises the following components: the environment acquisition module, the data processing module, the video monitoring and identifying module and the data storage module are respectively and electrically connected with the data processing module, and the video monitoring and identifying module comprises: face recognition module and flame recognition module. The invention can effectively monitor the current environment in real time, can monitor and identify visiting personnel in real time, can effectively remind and early warn when the identity verification is unsuccessful, and is convenient to improve the safety factor.

Description

Regional intelligent security monitoring rapid identification analysis system
Technical Field
The invention relates to the technical field of security monitoring, in particular to a regional intelligent rapid identification analysis system for security monitoring.
Background
The security monitoring system is an independent and complete system which is formed by transmitting video signals in a closed loop through optical fibers, coaxial cables or microwaves and from shooting to image display and recording; the system can reflect the monitored object in real time, image and reality, greatly prolongs the observation distance of human eyes, expands the functions of the human eyes, can replace manpower to monitor for a long time in a severe environment, and enables people to see all the situations actually happening on the monitored site and record the situations through a video recorder; and meanwhile, the alarm system equipment alarms illegal invasion, and the generated alarm signal is input into an alarm host, and the alarm host triggers the monitoring system to record video.
However, at present, the security monitoring system is lagged for face recognition early warning and fire prevention early warning, and cannot play a role in well preventing fire and timely early warning for strange people.
Therefore, a need exists for a rapid identification and analysis system for regional intelligent security monitoring.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a regional intelligent security monitoring rapid identification analysis system so as to overcome the technical problems in the prior related art.
The technical scheme of the invention is realized as follows:
an area intelligent security monitoring rapid identification analysis system, comprising: the environment acquisition module, the data processing module, the video monitoring recognition module and the data storage module, the environment acquisition module the video monitoring recognition module with the data storage module respectively with data processing module electric connection, just the video monitoring recognition module includes: the device comprises a face recognition module and a flame recognition module, wherein;
the environment acquisition module is used for deploying a plurality of data acquisition nodes to acquire environment information and transmitting the environment information to the data processing module;
the data processing module is used for acquiring the acquired environmental information, carrying out information processing, extracting video data information and transmitting the video data information to the video monitoring and identifying module;
the video monitoring and identifying module is used for receiving video data information and identifying the video data;
the face recognition module is used for recognizing the face of the video data information;
the flame identification module is used for carrying out flame identification on the video data information;
the data storage module is used for establishing a data information base.
Further, the method further comprises the following steps: and the security monitoring center is in information transmission with the data processing module through the communication module.
Further, the data acquisition node includes: camera, temperature and humidity sensor, oxygen sensor, carbon dioxide sensor, methane sensor and ammonia sensor.
Further, the data storage module includes: a face database, wherein;
the face database is used for extracting the characteristics of the local image frame sequence and forming a complete face image frame sequence to build the face database.
Further, the face recognition module comprises the following steps:
checking a face frame sequence set, denoted S { S }, of 1 ,s 2 ,s 3 ,...,s n M face data frame sequences are arranged in the face database, respectively X { X 1 ,X 2 ,X 3 ,...X m Each X i Represents a complete face data, denoted as X i {x i1 ,x i2 ,x i3 ,...,x in }；
The Euclidean distance of each set in S and X is calculated as:
Figure BDA0004050622940000021
calculate each I j Average value of (2):
Figure BDA0004050622940000022
obtaining a checked face frame sequence and a distance set of each face frame sequence in a face database, wherein the distance set is expressed as follows:
I′{I′ 1j ,I′ 2j ,I′ 3j ,...,I′ nj }；
the distance sets are ordered according to the distance size, and the converted distance sets are expressed as follows:
P{p 1 ,p 2 ,p 3 ,...,p n }；
wherein p is 1 ＜p 2 ＜p 3 ＜...＜p n ,p i ＝I′ j ；
And acquiring a face recognition result and transmitting the face recognition result to the security monitoring center.
Further, the face recognition result comprises the following steps:
presetting a face recognition threshold value as delta;
if it is
Figure BDA0004050622940000031
When the door is opened, the door is opened;
if it is
Figure BDA0004050622940000032
And if the door control is not operated, pushing the notification to the security monitoring center.
Further, the flame identification module comprises the following steps:
carrying out image graying and image denoising treatment on video data information in advance;
performing differential processing on the preprocessed two frames of adjacent images, and performing threshold segmentation processing on the differential images;
performing morphological processing after threshold segmentation, eliminating object boundary points and noise points smaller than structural elements, and selecting a motion region as a target region;
splitting an image of a target area into R, G, B three channel colors, and dividing pixel values of the image;
and acquiring the identified flame for marking, and pushing the flame to a security monitoring center.
Further, the two frames of adjacent images are subjected to differential processing, which comprises the following steps:
and performing pixel value difference calculation on the front frame image and the rear frame image of the video data, wherein the pixel value difference calculation is expressed as follows:
D n (x,y)＝|f n (x,y)-f n-1 (x,y)|；
wherein f n (x, y) and f n-1 (x, y) are the nth frame and the n-1 th frame images of the video image, respectively, D n (x, y) is a pixel value obtained by performing difference operation on the two frames of images;
a threshold value T is preset, and a target object area is acquired and expressed as:
Figure BDA0004050622940000033
wherein R is n (x, y) is the binarized image, D n (x, y) is an image obtained by dividing two frames of images.
Further, the splitting of the image of the target area into R, G, B three channel colors includes the following steps:
setting a threshold value of the color component, and acquiring a pixel value range characteristic of each color component of the real-time flame daily mark, wherein the pixel value range characteristic is expressed as follows:
Figure BDA0004050622940000034
wherein R represents a red component, G represents a green component, B represents a blue component, S represents color saturation, R th Representing a red component threshold, which takes on the value 180, S th To represent the saturation threshold, the value is 1.1.
The invention has the beneficial effects that:
the regional intelligent security monitoring rapid identification analysis system acquires environment information through the deployment data acquisition node, transmits the environment information to the data processing module, processes the information, extracts video data information and transmits the video data information to the video monitoring identification module to respectively carry out face recognition and flame identification, so that the regional intelligent security monitoring rapid identification analysis system not only can effectively monitor the current environment in real time, but also can effectively carry out reminding and early warning when the identity verification is unsuccessful, is convenient for improving the safety coefficient, can monitor the current fire environment information in real time through the flame identification, and can timely push the fire environment information to the security monitoring center to carry out early warning if the abnormal condition is detected, thereby effectively avoiding the occurrence of fire accidents, and also can better protect people property and personal safety, and has wide application range.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic block diagram of a regional intelligent security monitoring rapid identification analysis system in accordance with an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a system for monitoring and rapidly identifying and analyzing regional intelligent security protection according to an embodiment of the invention;
fig. 3 is a second flow chart of a regional intelligent security monitoring rapid identification analysis system according to an embodiment of the invention.
In the figure:
1. an environment collection module; 2. a data processing module; 3. a video monitoring and identifying module; 4. a data storage module; 5. a face recognition module; 6. a flame identification module; 7. a security monitoring center; 8. and a communication module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the invention, fall within the scope of protection of the invention.
According to the embodiment of the invention, a regional intelligent security monitoring rapid identification analysis system is provided.
As shown in fig. 1, a regional intelligent security monitoring rapid identification analysis system according to an embodiment of the present invention includes: the environment acquisition module 1, the data processing module 2, the video monitoring and identifying module 3 and the data storage module 4, the environment acquisition module 1, the video monitoring and identifying module 3 and the data storage module 4 are respectively and electrically connected with the data processing module 2, and the video monitoring and identifying module 3 comprises: a face recognition module 5 and a flame recognition module 6, wherein;
the environment acquisition module 1 is used for deploying a plurality of data acquisition nodes to acquire environment information and transmitting the environment information to the data processing module 2;
the data processing module 2 is used for acquiring the acquired environmental information, carrying out information processing, extracting video data information and transmitting the video data information to the video monitoring and identifying module 3;
the video monitoring and identifying module 3 is used for receiving video data information and identifying the video data;
the face recognition module 5 is used for recognizing the face of the video data information;
a flame recognition module 6 for performing flame recognition on the video data information;
and the data storage module 4 is used for establishing a data information base.
In addition, the method further comprises the steps of: the security monitoring center 7 is used for transmitting information between the security monitoring center 7 and the data processing module 2 through the communication module 8.
In addition, a data collection node comprising: camera, temperature and humidity sensor, oxygen sensor, carbon dioxide sensor, methane sensor and ammonia sensor.
In addition, the data storage module 4 includes: a face database, wherein;
the face database is used for extracting the characteristics of the local image frame sequence and forming a complete face image frame sequence to build the face database.
By means of the scheme, environmental information is collected through the deployment data collection node and transmitted to the data processing module 2, information processing is carried out, video data information is extracted, and the video data information is transmitted to the video monitoring and identifying module 3 for face recognition and flame identification respectively, so that real-time monitoring can be effectively carried out on the current environment, real-time monitoring and identification can be carried out on visiting personnel, reminding and early warning can be effectively carried out when identity verification is unsuccessful, safety coefficient is conveniently improved, in addition, the current fire-fighting environmental information can be monitored in real time through flame identification, if abnormal conditions are detected through identification, the fire accident can be effectively avoided, people and property and personal safety can be better protected, and the application range is wide.
In addition, as shown in fig. 2, the face recognition module 5 of the present technical solution includes the following steps:
checking a face frame sequence set, denoted S { S }, of 1 ,s 2 ,s 3 ,...,s n M face data frame sequences are arranged in the face database, and are respectively X { X } 1 ,X 2 ,X 3 ,...X m Each X i Represents a complete face data, denoted as X i {x i1 ,x i2 ,x i3 ,...,x in }；
The Euclidean distance of each set in S and X is calculated as:
Figure BDA0004050622940000061
calculate each I j Average value of (2):
Figure BDA0004050622940000062
obtaining a checked face frame sequence and a distance set of each face frame sequence in a face database, wherein the distance set is expressed as follows:
I′{I′ 1j ,I′ 2j ,I′ 3j ,...,I′ nj }；
the distance sets are ordered according to the distance size, and the converted distance sets are expressed as follows:
P{p 1 ,p 2 ,p 3 ,...,p n }；
wherein p is 1 ＜p 2 ＜p 3 ＜...＜p n ,p i ＝I′ j ；
The acquired face recognition result is transmitted to the security monitoring center 7.
The face recognition result comprises the following steps:
presetting a face recognition threshold value as delta;
if it is
Figure BDA0004050622940000063
When the door is opened, the door is opened;
if it is
Figure BDA0004050622940000064
And if so, the access control is not operated, and the push notification is sent to the security monitoring center 7.
Further, as shown in fig. 3, the flame identification module 6 includes the steps of:
carrying out image graying and image denoising treatment on video data information in advance;
according to the technical scheme, the gray processing can be performed by adopting a weighted average method, and the gray processing is expressed as follows:
f(i,j)＝0.30R(i,j)+0.59G(i,j)+0.11B(i,j)；
where R (i, j) is a red component pixel value, G (i, j) is a green component pixel value, B (i, j) is a blue component pixel value, and f (i, j) is a gray pixel value.
In addition, the image denoising processing can adopt median filtering processing, the median filtering can effectively remove spots, isolated and salt and pepper noise and the like, the median filtering is to sort the values of the pixel points in a selected range in the image from large to small, then select the intermediate value of the sorting result in the range to output, and the edge area of the image can be saved and noise points are filtered, and the median filtering is expressed as:
Figure BDA0004050622940000071
where g (x, y) and f (x, y) represent the pre-and post-filter pixel values, respectively, and W represents the set of all pixel points within the range.
In addition, the two preprocessed adjacent images are subjected to differential processing, and the differential image is subjected to threshold segmentation;
according to the technical scheme, the video acquired through the camera is a group of coherent image frames, when no moving object appears in the video, pixel values between the front frame image and the rear frame image are unchanged, if a moving object appears, obvious pixel differences are generated between the front frame image and the rear frame image, the implementation mode is mainly that the difference calculation of the pixel values is carried out on the front frame image and the rear frame image, when the calculated difference value is larger than a set value, the moving object is considered to exist, and when the calculated difference value is smaller than the set value, the moving object is considered to be absent, and the difference calculation of the pixel values is carried out on the front frame image and the rear frame image of the video data, and is expressed as follows:
D n (x,y)＝|f n (x,y)-f n-1 (x,y)|；
wherein f n (x, y) and f n-1 (x, y) are the nth frame and the n-1 th frame images of the video image, respectively, D n (x, y) is a pixel value obtained by performing difference operation on the two frames of images;
in addition, the image binarization processing is carried out, the binarization of the image is that in an image containing a daily mark object, the target object area needs to be accurately obtained from different pixel values, and some information irrelevant to the analysis flow in the image can be removed, so that useful information is highlighted. The usual method is to set a threshold T, divide the pixel value of the image into a portion larger than T and smaller than T, and generally divide the image into black and white, so as to obtain the target object area more obviously. Expressed as:
Figure BDA0004050622940000072
wherein R is n (x, y) is the binarized image, D n (x, y) is an image obtained by dividing two frames of images.
In addition, morphological processing is carried out after threshold segmentation, object boundary points and noise points smaller than structural elements are eliminated, and a motion area is selected as a target area;
according to the technical scheme, object boundary points and noise points smaller than structural elements are eliminated, and the method specifically comprises the following steps:
setting a binary image foreground object as 1 and a background as 0, corroding the original image by using a structural element, traversing each pixel in the image, and taking the minimum value of all pixels in the coverage area of the structural element to replace the pixel value of the central pixel point. Since the pixel values of the binary image are only 0 and 1, if the areas of the current structural element are all the background and the pixel value is 0, the original image cannot be changed, if the areas of the current structural element are all the foreground object and the pixel value is 1, the original image cannot be changed, and when the structural element is only positioned at the edge of the foreground object, the covered areas have 0 and 1 pixel values, and the pixel value in the areas is changed from 1 to 0, so that the original image is changed, and therefore, the effect of corrosion is seen to be that the foreground object is reduced by one circle, and the whole is darkened or even broken.
In addition, splitting the image of the target area into R, G, B three channel colors, and dividing the pixel values of the image;
according to the technical scheme, the image of the target area is split into R, G, B three channels of colors, and the method comprises the following steps:
setting a threshold value of the color component, and acquiring a pixel value range characteristic of each color component of the real-time flame daily mark, wherein the pixel value range characteristic is expressed as follows:
Figure BDA0004050622940000081
wherein R represents a red component, G represents a green component, B represents a blue component, S represents color saturation, R th Representing a red component threshold, which takes on the value 180, S th To represent the saturation threshold, the value is 1.1.
In addition, the identified flames are obtained for marking and pushed to the security monitoring center 7.
In summary, by means of the technical scheme, the environment information is collected through the deployment data collection node and is transmitted to the data processing module 2, the information is processed and extracted, the video data information is transmitted to the video monitoring and identifying module 3 to be respectively subjected to face recognition and flame identification, real-time monitoring and identification can be effectively carried out on the current environment, meanwhile, visiting personnel can be subjected to real-time monitoring and identification, when identity verification is unsuccessful, reminding and early warning can be effectively carried out, the safety coefficient is conveniently improved, in addition, the current fire-fighting environment information can be monitored in real time through flame identification, if abnormal conditions are detected through identification, the fire accident can be effectively avoided, people and property can be better protected, and the application range is wide.
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention, and other embodiments of the present disclosure will readily occur to those skilled in the art upon consideration of the specification and disclosure at the examples. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (9)

1. Regional intelligent security monitoring quick identification analysis system, characterized by comprising: the environment acquisition module (1), data processing module (2), video monitoring and identifying module (3) and data storage module (4), environment acquisition module (1) video monitoring and identifying module (3) with data storage module (4) respectively with data processing module (2) electric connection, just video monitoring and identifying module (3) include: a face recognition module (5) and a flame recognition module (6), wherein;
the environment acquisition module (1) is used for deploying a plurality of data acquisition nodes to acquire environment information and transmitting the environment information to the data processing module (2);
the data processing module (2) is used for acquiring the acquired environmental information, carrying out information processing, extracting video data information and transmitting the video data information to the video monitoring and identifying module (3);
the video monitoring and identifying module (3) is used for receiving video data information and identifying video data;
the face recognition module (5) is used for recognizing the face of the video data information;
the flame identification module (6) is used for carrying out flame identification on the video data information;
the data storage module (4) is used for establishing a data information base.
2. The area-intelligent security monitoring rapid identification analysis system of claim 1, further comprising: the security monitoring center (7), the security monitoring center (7) is in information transmission with the data processing module (2) through the communication module (8).
3. The area intelligent security monitoring rapid identification analysis system of claim 1, wherein the data acquisition node comprises: camera, temperature and humidity sensor, oxygen sensor, carbon dioxide sensor, methane sensor and ammonia sensor.
4. The area-intelligent security monitoring rapid identification analysis system according to claim 1, characterized in that the data storage module (4) comprises: a face database, wherein;
the face database is used for extracting the characteristics of the local image frame sequence and forming a complete face image frame sequence to build the face database.
5. The regional intelligent security monitoring rapid identification analysis system according to claim 4, wherein the face recognition module (5) comprises the following steps:
checking a face frame sequence set, denoted S { S }, of 1 ,s 2 ,s 3 ,...,s n M face data frame sequences are arranged in the face database, respectively X { X 1 ,X 2 ,X 3 ,...X m Each X i Represents a complete face data, denoted as X i {x i1 ,x i2 ,x i3 ,...,x in }；
The Euclidean distance of each set in S and X is calculated as:
Figure FDA0004050622910000021
calculate each I j Average value of (2):
Figure FDA0004050622910000022
obtaining a checked face frame sequence and a distance set of each face frame sequence in a face database, wherein the distance set is expressed as follows:
I′{I′ 1j ,I′ 2j ,I′ 3j ,...,I′ nj }；
the distance sets are ordered according to the distance size, and the converted distance sets are expressed as follows:
P{p 1 ,p 2 ,p 3 ,...,p n }；
wherein p is 1 ＜p 2 ＜p 3 ＜...＜p n ,p i ＝I′ j ；
And the acquired face recognition result is transmitted to the security monitoring center (7).
6. The regional intelligent security monitoring rapid identification analysis system of claim 5, wherein the face recognition result comprises the following steps:
presetting a face recognition threshold value as delta;
if it is
Figure FDA0004050622910000023
When the door is opened, the door is opened;
if it is
Figure FDA0004050622910000024
And if the door control is not operated, pushing a notification to the security monitoring center (7).
7. The area-intelligent security monitoring rapid identification analysis system according to claim 1, characterized in that the flame identification module (6) comprises the following steps:
carrying out image graying and image denoising treatment on video data information in advance;
performing differential processing on the preprocessed two frames of adjacent images, and performing threshold segmentation processing on the differential images;
performing morphological processing after threshold segmentation, eliminating object boundary points and noise points smaller than structural elements, and selecting a motion region as a target region;
splitting an image of a target area into R, G, B three channel colors, and dividing pixel values of the image;
and acquiring the identified flame for marking, and pushing the flame to a security monitoring center (7).
8. The regional intelligent security monitoring rapid identification analysis system according to claim 7, wherein the two frames of adjacent images are subjected to differential processing, and the method comprises the following steps:
and performing pixel value difference calculation on the front frame image and the rear frame image of the video data, wherein the pixel value difference calculation is expressed as follows:
D n (x,y)＝|f n (x,y)-f n-1 (x,y)|；
wherein f n (x, y) and f n-1 (x, y) are the nth frame and the n-1 th frame images of the video image, respectively, D n (x, y) is a pixel value obtained by performing difference operation on the two frames of images;
a threshold value T is preset, and a target object area is acquired and expressed as:
Figure FDA0004050622910000031
wherein R is n (x, y) is the binarized image, D n (x, y) is an image obtained by dividing two frames of images.
9. The area intelligent security monitoring rapid identification analysis system according to claim 8, wherein the image of the target area is split into R, G, B three-channel colors, comprising the steps of:
setting a threshold value of the color component, and acquiring a pixel value range characteristic of each color component of the real-time flame daily mark, wherein the pixel value range characteristic is expressed as follows:
Figure FDA0004050622910000032
wherein R represents a red component, G represents a green component, B represents a blue component, S represents color saturation, R th Representing a red component threshold, which takes on the value 180, S th To represent the saturation threshold, the value is 1.1.
CN202310040380.0A 2023-01-13 2023-01-13 Regional intelligent security monitoring rapid identification analysis system Pending CN116092261A (en)

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