AI management method and system for security and protection monitoring

Abstract

The invention discloses an AI management method and system for security and protection monitoring, and solves the problem that whether a person is an off-campus person cannot be determined in time in the prior art. The method comprises the following steps: S1, a monitoring module used for collecting campus videos carries out video monitoring on campus personnel based on video information collected by the monitoring module to obtain a campus access personnel information set; s2, an image processing module intercepts a key image in the campus access personnel information set to obtain a character feature information set; s3, the face recognition module carries out matching on the basis of the character feature information set and the campus character information; and S4, the control center controls an early warning module to send out early warning information based on a matching result of the face recognition module.

Description

Technical Field

[0001] The present invention belongs to the technical field of monitoring equipment, and specifically relates to an AI management method and system for security monitoring. Background Art

[0002] The campus security monitoring and management system ensures the high security of the campus, the ability to collect evidence and confirm emergencies, and the monitoring of public places on campus by school security personnel. At the same time, it assists the security management department to obtain intuitive images of the on-site situation on campus and grasp the overall situation of the campus in real time, thereby strengthening campus safety prevention management, maintaining the normal security environment on campus, and improving the school's modern teaching management level.

[0003] In the campus management of primary schools, junior high schools and high schools, primary schools, junior high schools and high schools are generally managed in a closed manner to prevent outsiders from entering the campus. Existing campus security monitoring generally monitors the campus through cameras, but it cannot effectively supervise outsiders on campus, thereby increasing campus risks, and security personnel cannot determine whether the person is an insider. Summary of the invention

[0004] The present invention provides an AI management method and system for security monitoring to at least solve some of the above-mentioned technical problems.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] An AI management method for security monitoring includes the following steps:

[0007] S1, a monitoring module for collecting campus videos, performing video monitoring on campus personnel based on the video information collected by the monitoring module, marking the personnel, and obtaining a collection of information on personnel entering and leaving the campus;

[0008] S2, the image processing module intercepts the key images in the collection of campus entry and exit personnel information, and processes the intercepted key images to obtain a collection of character feature information;

[0009] S3, the face recognition module matches the person feature information collection with the person information in the school and obtains the matching result;

[0010] S4. The control center controls the warning module to issue warning information based on the matching results of the face recognition module. At the same time, the location, time and path of the outsider entering the campus are stored, and an information database of the outsider is established.

[0011] Furthermore, the specific process of the method for obtaining a collection of information on people entering and leaving the campus is as follows: the monitoring module marks the information on people in the monitoring area through an infrared human body sensor, and obtains the marking information, the marking information is 1, 2, 3...n, and the video information is intercepted according to the marking information to obtain key images, which are sorted and stored to obtain a collection of information on people entering and leaving the campus.

[0012] Furthermore, the method for obtaining a collection of character feature information includes the following two steps:

[0013] S1. Extract key images from the collection of campus entry and exit personnel information through the image segmentation module, and segment the background information and portrait information of the key images;

[0014] S2. The image enhancement module extracts the portrait information for processing, and matches the portrait information with the person tag information to obtain a collection of person feature information.

[0015] Furthermore, the method for matching the collection of character feature information with the on-campus character information is specifically as follows: when the control center receives the character feature information, the control center transmits the character feature information to the face recognition module, and the face recognition module matches the character feature information with the on-campus character information. If the collection of character feature information matches the on-campus character information successfully, the person is an on-campus person; if the collection of character feature information fails to match the on-campus character information, the person is an outsider.

[0016] Furthermore, the method for the early warning module to issue an early warning is specifically as follows: the control center controls the early warning module to issue an early warning message based on the matching result of the face recognition module. At the same time, the control center retrieves the video surveillance and stores the location, time, and path of the person entering the campus, and obtains the information database of outsiders.

[0017] An AI management system for security monitoring includes several monitoring modules for collecting campus image information, image processing modules connected to the monitoring modules for processing the collected image information, a control center connected to the image processing modules, a face recognition module connected to the control center for matching a collection of character feature information with on-campus character information, a storage module connected to the control center for storing the collected campus image information, and an alarm module connected to the control center.

[0018] Furthermore, the monitoring module includes a plurality of cameras distributed in the campus, and an infrared human body sensor arranged on one side of the camera.

[0019] Furthermore, the image processing module includes an image separation module connected to the monitoring module for separating the background and the face image, and an image enhancement module connected to the image separation module for the face image.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention has a simple structure, a scientific and reasonable design, and is easy to use. The present invention can identify and mark people entering and leaving the campus through a monitoring module, and intercept the marked image information, so as to obtain a collection of information on people entering and leaving the campus, and can analyze and process the image information collected by the monitoring module through an image processing module, distinguish environmental information and portrait information in the image information, and process the portrait information at the same time, and make a one-to-one correspondence with the collection of information on people entering and leaving the campus, so as to obtain a collection of character feature information, and the face recognition module matches the collection of character feature information with the collection of on-campus character information, so as to determine whether the person is an on-campus person, and the control center controls the early warning module to issue an early warning according to the result of the face recognition module, so as to prevent non-school people from entering the campus. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the present invention.

[0023] Figure 2 It is a system block diagram of the present invention.

[0024] The names corresponding to the reference numerals are:

[0025] 1. Monitoring module; 2. Image processing module; 3. Control center; 4. Face recognition module; 5. Storage module; 6. Alarm module; 7. Camera; 8. Infrared human body sensor; 9. Image separation module; 10. Image enhancement module. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore they cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0028] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; of course, it can also be a mechanical connection or an electrical connection; in addition, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] like Figure 1 and Figure 2 As shown, the present invention provides an AI management method for security monitoring, comprising the following steps:

[0030] S1, a monitoring module for collecting campus videos, performing video monitoring on campus personnel based on the video information collected by the monitoring module, marking the personnel, and obtaining a collection of information on personnel entering and leaving the campus;

[0031] S2, the image processing module intercepts the person video based on the collection of information of people entering and leaving the campus, and processes the intercepted person video to obtain a collection of person feature information;

[0032] S3, the face recognition module matches the character feature information collection with the on-campus character information collection and obtains a matching result;

[0033] S4. The control center controls the warning module to issue a warning based on the matching result of the face recognition module.

[0034] The present invention can identify and mark people entering and leaving the campus through the monitoring module, and intercept the marked image information, so as to obtain a collection of information on people entering and leaving the campus, and can analyze and process the image information collected by the monitoring module through the image processing module, distinguish the environmental information and the portrait information in the image information, and process the portrait information at the same time, and make a one-to-one correspondence with the collection of information on people entering and leaving the campus, so as to obtain a collection of character feature information, and the face recognition module matches the collection of character feature information with the collection of on-campus character information, so as to determine whether the person is an on-campus person, and the control center controls the early warning module to issue an early warning according to the result of the face recognition module, so as to prevent non-school people from entering the campus.

[0035] Example 2.

[0036] like Figure 1 and Figure 2 As shown, the present invention provides an AI management method for security monitoring, comprising the following steps:

[0037] S1, a monitoring module for collecting campus videos, performing video monitoring on campus personnel based on the video information collected by the monitoring module, marking the personnel, and obtaining a collection of information on personnel entering and leaving the campus;

[0038] S2, the image processing module intercepts the person video based on the collection of information of people entering and leaving the campus, and processes the intercepted person video to obtain a collection of person feature information;

[0039] S3, the face recognition module matches the character feature information collection with the on-campus character information collection and obtains a matching result;

[0040] S4. The control center controls the warning module to issue a warning based on the matching result of the face recognition module.

[0041] The specific process of the method for obtaining a collection of information on people entering and leaving the campus is as follows: the monitoring module marks the information on people in the monitoring area through an infrared human body sensor, and obtains the marking information, the marking information is 1, 2, 3...n, and the video information is intercepted according to the marking information to obtain key images, which are sorted and stored to obtain a collection of information on people entering and leaving the campus.

[0042] Based on Example 1, this Example 2 gives the specific process of the method for obtaining a collection of information on people entering and leaving the campus, specifically: the specific process of the method for obtaining a collection of information on people entering and leaving the campus is: the monitoring module marks the information of people in the monitoring area through an infrared human body sensor, and obtains the marking information, the marking information is 1, 2, 3...n, and the video information is intercepted according to the marking information to obtain key images, and the key images are sorted and stored, so as to obtain a collection of information on people entering and leaving the campus.

[0043] The monitoring module of the present invention divides the public area of ​​the campus into several monitoring areas, and the infrared human body sensor is installed in the monitoring module. The infrared human body sensor can detect the number of people in the monitoring area. The control center marks the number of people in the monitoring area through the infrared human body sensor, and marks the number of people as 1, 2, 3...n. At the same time, the marked image information is intercepted through the monitoring module, and it corresponds one to one with the number of people marked as 1, 2, 3...n, so as to obtain a collection of information on people entering and leaving the campus.

[0044] Example 3.

[0045] like Figure 1 and Figure 2 As shown, the present invention provides an AI management method for security monitoring, comprising the following steps:

[0046] S1, a monitoring module for collecting campus videos, performing video monitoring on campus personnel based on the video information collected by the monitoring module, marking the personnel, and obtaining a collection of information on personnel entering and leaving the campus;

[0047] S2, the image processing module intercepts the person video based on the collection of information of people entering and leaving the campus, and processes the intercepted person video to obtain a collection of person feature information;

[0048] S3, the face recognition module matches the character feature information collection with the on-campus character information collection and obtains a matching result;

[0049] S4. The control center controls the warning module to issue a warning based on the matching result of the face recognition module.

[0050] The specific process of the method for obtaining a collection of information on people entering and leaving the campus is as follows: the monitoring module marks the information on people in the monitoring area through an infrared human body sensor, and obtains the marking information, the marking information is 1, 2, 3...n, and the video information is intercepted according to the marking information to obtain key images, which are sorted and stored to obtain a collection of information on people entering and leaving the campus.

[0051] The method for obtaining a collection of character feature information includes the following two steps:

[0052] S1. Extract key images from the collection of campus entry and exit personnel information through the image segmentation module, and segment the background information and portrait information of the key images;

[0053] S2. The image enhancement module extracts the portrait information for processing, and matches the portrait information with the person tag information to obtain a collection of person feature information.

[0054] This embodiment 3 provides a method for obtaining a collection of character feature information based on embodiment 2, specifically: the method for obtaining a collection of character feature information includes the following two steps:

[0055] S1. Extract key images from the collection of campus entry and exit personnel information through the image segmentation module, and segment the background information and portrait information of the key images;

[0056] S2. The image enhancement module extracts the portrait information for processing, and matches the portrait information with the person tag information to obtain a collection of person feature information.

[0057] The image processing module of the present invention separates the background information and the portrait in the collection of information on people entering and leaving the campus through the image separation module, and processes the portrait information separated by the image separation module through the image enhancement module, and enhances the portrait information at the same time, and organizes the portrait information and corresponds it with the information on people entering and leaving the campus, thereby obtaining a collection of character feature information, which can facilitate recognition by the face recognition module.

[0058] Example 4.

[0059] like Figure 1 and Figure 2 As shown, the present invention provides an AI management method for security monitoring, comprising the following steps:

[0060] S1, a monitoring module for collecting campus videos, performing video monitoring on campus personnel based on the video information collected by the monitoring module, marking the personnel, and obtaining a collection of information on personnel entering and leaving the campus;

[0061] S2, the image processing module intercepts the person video based on the collection of information of people entering and leaving the campus, and processes the intercepted person video to obtain a collection of person feature information;

[0062] S3, the face recognition module matches the character feature information collection with the on-campus character information collection and obtains a matching result;

[0063] S4. The control center controls the warning module to issue a warning based on the matching result of the face recognition module.

[0064] The method for matching a collection of character feature information with on-campus character information is specifically as follows: when a control center receives the character feature information, the control center transmits the character feature information to a face recognition module, and the face recognition module matches the character feature information with the on-campus character information. If the collection of character feature information matches the on-campus character information successfully, the person is an on-campus person; if the collection of character feature information fails to match the on-campus character information, the person is an off-campus person.

[0065] Based on Example 1, this Example 4 provides a method for matching a collection of character feature information with on-campus character information, specifically: when the control center receives the character feature information, the control center transmits the character feature information to the face recognition module, and the face recognition module matches the character feature information with the on-campus character information. If the collection of character feature information matches the on-campus character information successfully, the person is an on-campus person; if the collection of character feature information fails to match the on-campus character information, the person is an off-campus person.

[0066] The control center of the present invention transmits the character feature information to the face recognition module, and the face recognition module matches the character feature information with the character information on campus. If the character feature information collection matches the character information on campus successfully, the person is an on-campus person; if the character feature information collection fails to match the character information on campus, the person is an off-campus person. At the same time, the control center controls the early warning device to issue an early warning. At the same time, the control center retrieves the location, time, and path of the person entering the campus from the monitoring module based on the matching result of the face recognition module, and stores it through the storage module to obtain a collection of outsider information. The collection of outsider information can quickly determine the movement trajectory of the outsider on campus to facilitate subsequent investigation and punishment.

[0067] Example 5.

[0068] like Figure 1 and Figure 2 As shown, the present invention provides an AI management method for security monitoring, comprising the following steps:

[0069] S1, a monitoring module for collecting campus videos, performing video monitoring on campus personnel based on the video information collected by the monitoring module, marking the personnel, and obtaining a collection of information on personnel entering and leaving the campus;

[0070] S2, the image processing module intercepts the person video based on the collection of information of people entering and leaving the campus, and processes the intercepted person video to obtain a collection of person feature information;

[0071] S3, the face recognition module matches the character feature information collection with the on-campus character information collection and obtains a matching result;

[0072] S4. The control center controls the warning module to issue a warning based on the matching result of the face recognition module.

[0073] The method for the early warning module to issue an early warning is specifically as follows: the control center controls the early warning module to issue an early warning message based on the matching result of the face recognition module. At the same time, the control center retrieves the video surveillance and stores the location, time, and path of the person entering the campus, and obtains the information database of outsiders.

[0074] Based on Example 1, this Example 5 provides a method for the early warning module to issue an early warning, specifically: the control center controls the early warning module to issue an early warning message based on the matching result of the face recognition module. At the same time, the control center retrieves the video surveillance and stores the location, time, and path of the person entering the campus, and obtains the information database of outsiders.

[0075] The face recognition module of the present invention transmits the recognition result to the control center, and the control center controls the early warning module to issue a warning message according to the recognition result of the face recognition module. If the character feature information collection successfully matches the character information in the school, the early warning module is controlled not to issue a warning message. If the character feature information collection fails to match the character information in the school, the control center controls the early warning module to issue a warning message. At the same time, the control center stores the location, time and path of the person entering the campus through the monitoring module, and obtains an information database of outsiders, thereby facilitating the supervision of outsiders.

[0076] Example 6.

[0077] like Figure 1 and Figure 2 As shown, the present invention provides an AI management system for security monitoring, including several monitoring modules 1 for collecting campus image information, an image processing module 2 connected to the monitoring module 1 for processing the collected image information, a control center 3 connected to the image processing module 2, a face recognition module 4 connected to the control center 3 for matching a collection of character feature information with on-campus character information, a storage module 5 connected to the control center 3 for storing the collected campus image information, and an alarm module 6 connected to the control center 3.

[0078] The monitoring module 1 of the present invention divides the campus into several monitoring areas, and monitors the campus personnel through the monitoring module 1, and marks the personnel in the monitoring area, so as to obtain a collection of information about the personnel entering and leaving the campus. The monitoring module 1 transmits the collected monitoring video information to the image processing module 2, and the image processing module 2 processes the video in the collection of information about the personnel entering and leaving the campus, and extracts key portrait images from the video. At the same time, the image processing module 2 processes the key portrait images, so as to obtain a collection of character feature information. The image processing module 2 transmits the collection of character feature information to the control center 3, and the control center 3 transmits the collection of character feature information to the face Identification module 4, face recognition module 4 compares the character feature information collection with the on-campus character information collection. If the character feature information collection matches the on-campus character information successfully, the person is an on-campus person. If the character feature information collection fails to match the on-campus character information, the person is an outsider. At the same time, the control center 3 controls the early warning device to issue an early warning. At the same time, the control center 3 retrieves the location, time, and path of the person entering the campus from the monitoring module 1 based on the matching result of the face recognition module 4, and stores it through the storage module 5 to obtain a collection of outsider information, thereby facilitating the monitoring of the movement trajectory of outsiders on campus.

[0079] Example 7.

[0080] like Figure 1 and Figure 2As shown, the present invention provides an AI management system for security monitoring, including several monitoring modules 1 for collecting campus image information, an image processing module 2 connected to the monitoring module 1 for processing the collected image information, a control center 3 connected to the image processing module 2, a face recognition module 4 connected to the control center 3 for matching a collection of character feature information with on-campus character information, a storage module 5 connected to the control center 3 for storing the collected campus image information, and an alarm module 6 connected to the control center 3.

[0081] The monitoring module 1 includes a plurality of cameras 7 distributed in the campus and connected to the image processing module 2 , and an infrared human body sensor 8 arranged at one side of the camera 7 and connected to the control center 3 .

[0082] This embodiment 7 provides a more preferred structure of the monitoring module 1 based on the embodiment 6, specifically: the monitoring module 1 includes a plurality of cameras 7 distributed in the campus and connected to the image processing module 2, and an infrared human body sensor 8 arranged on one side of the camera 7 and connected to the control center 3.

[0083] The cameras 7 of the present invention are distributed in the campus and monitor the campus in real time. At the same time, the cameras 7 transmit the collected video information to the image processing module 2. The image processing module 2 processes the video information and extracts portrait information therefrom. At the same time, the infrared human body sensor 8 is installed on one side of the camera 7. The infrared human body sensor 8 can mark the campus personnel in the monitoring area, and transmit the marking result to the control center 3. The control center 3 matches the marking result of the infrared human body sensor 8 with the portrait information extracted by the image processing module 2, so that the marking result of the infrared human body sensor 8 corresponds one to one with the portrait information extracted by the image processing module 2, thereby facilitating the marking of personnel information.

[0084] Example 8.

[0085] like Figure 1 and Figure 2 As shown, the present invention provides an AI management system for security monitoring, including several monitoring modules 1 for collecting campus image information, an image processing module 2 connected to the monitoring module 1 for processing the collected image information, a control center 3 connected to the image processing module 2, a face recognition module 4 connected to the control center 3 for matching a collection of character feature information with on-campus character information, a storage module 5 connected to the control center 3 for storing the collected campus image information, and an alarm module 6 connected to the control center 3.

[0086] The image processing module 2 includes an image separation module 9 connected to the monitoring module 1 for separating the background from the face image, and an image enhancement module 10 connected to the image separation module 9 for the face image.

[0087] This embodiment 8 provides a more preferred structure of the image processing module 2 based on the embodiment 6, specifically: the image processing module 2 includes an image separation module 9 connected to the monitoring module 1 for separating the background and the face image, and an image enhancement module 10 connected to the image separation module 9 for the face image.

[0088] The image separation module 9 of the present invention is connected to the camera 7, and the camera 7 transmits the collected video information to the image separation module 9. The image separation module 9 extracts the key image from the video information and processes the key image, thereby separating the background information from the face image. At the same time, the image separation module 9 transmits the face image to the image enhancement module 10, and the image enhancement module 10 processes the face image to obtain a collection of character feature information.

[0089] Finally, it should be noted that the above embodiments are only preferred embodiments of the present invention to illustrate the technical solutions of the present invention, rather than limiting them, and certainly not limiting the patent scope of the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention. In other words, any changes or modifications made to the main design concept and spirit of the present invention that have no substantive significance, and the technical problems they solve are still consistent with the present invention, should be included in the protection scope of the present invention. In addition, the direct or indirect application of the technical solutions of the present invention in other related technical fields is also included in the patent protection scope of the present invention.

Claims

1. An AI management method for security monitoring, characterized in that: The following steps are involved: S1, a monitoring module for collecting campus videos, performing video monitoring on campus personnel based on the video information collected by the monitoring module, marking the personnel, and obtaining a collection of information on personnel entering and leaving the campus; S2, the image processing module intercepts the key images in the collection of campus entry and exit personnel information, and processes the intercepted key images to obtain a collection of character feature information; S3, the face recognition module matches the person feature information collection with the person information in the school and obtains the matching result; S4. The control center controls the warning module to issue warning information based on the matching results of the face recognition module. At the same time, the location, time and path of the outsider entering the campus are stored, and an information database of the outsider is established.

2. The AI ​​management system for security monitoring according to claim 1, characterized in that: The specific process of the method for obtaining a collection of information on people entering and leaving the campus is as follows: the monitoring module marks the information on people in the monitoring area through an infrared human body sensor, and obtains the marking information, the marking information is 1, 2, 3...n, and the video information is intercepted according to the marking information to obtain key images, which are sorted and stored to obtain a collection of information on people entering and leaving the campus.

3. The AI ​​management method for security monitoring according to claim 2 is characterized in that: The method for obtaining a collection of character feature information includes the following two steps: S1. Extract key images from the collection of campus entry and exit personnel information through the image segmentation module, and segment the background information and portrait information of the key images; S2. The image enhancement module extracts the portrait information for processing, and matches the portrait information with the person tag information to obtain a collection of person feature information.

4. The AI ​​management method for security monitoring according to claim 1, characterized in that: The method for matching the collection of character feature information with the on-campus character information is specifically as follows: when the control center receives the character feature information, the control center transmits the character feature information to the face recognition module, and the face recognition module matches the character feature information with the on-campus character information. If the collection of character feature information matches the on-campus character information successfully, the person is an on-campus person; if the collection of character feature information fails to match the on-campus character information, the person is an off-campus person.

5. The AI ​​management method for security monitoring according to claim 1, characterized in that: The method for the early warning module to issue an early warning is specifically as follows: the control center controls the early warning module to issue an early warning message based on the matching result of the face recognition module. At the same time, the control center retrieves the video surveillance and stores the location, time, and path of the person entering the campus, and obtains the information database of outsiders.

6. An AI management system for security monitoring, based on the AI ​​management method for security monitoring according to claim 1, characterized in that: The invention comprises a plurality of monitoring modules (1) for collecting campus image information, an image processing module (2) connected to the monitoring module (1) for processing the collected image information, a control center (3) connected to the image processing module (2), a face recognition module (4) connected to the control center (3) for matching a collection of character feature information with on-campus character information, a storage module (5) connected to the control center (3) for storing the collected campus image information, and an alarm module (6) connected to the control center (3).

7. The AI ​​management system for security monitoring according to claim 6, characterized in that: The monitoring module (1) comprises a plurality of cameras (7) distributed in the campus, and an infrared human body sensor (8) arranged on one side of the camera (7).

8. The AI ​​management system for security monitoring according to claim 6, characterized in that: The image processing module (2) comprises an image separation module (9) connected to the monitoring module (1) and used for separating the background and the face image, and an image enhancement module (10) connected to the image separation module (9) and used for the face image.

Images