CN114821422A - Intelligent campus monitoring system and method for prejudging campus overlord - Google Patents
Intelligent campus monitoring system and method for prejudging campus overlord Download PDFInfo
- Publication number
- CN114821422A CN114821422A CN202210459747.8A CN202210459747A CN114821422A CN 114821422 A CN114821422 A CN 114821422A CN 202210459747 A CN202210459747 A CN 202210459747A CN 114821422 A CN114821422 A CN 114821422A
- Authority
- CN
- China
- Prior art keywords
- campus
- scene
- student
- identifying
- development trend
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- 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
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computational Linguistics (AREA)
- Emergency Management (AREA)
- Signal Processing (AREA)
- Business, Economics & Management (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Psychiatry (AREA)
- Social Psychology (AREA)
- Human Computer Interaction (AREA)
- Image Analysis (AREA)
Abstract
The invention relates to the technical field of smart campuses, and provides a smart campus monitoring system and a smart campus monitoring method for prejudging campus overlord, wherein the smart campus monitoring method comprises the following steps: acquiring student image information in an area and preprocessing the student image information; performing scene analysis on the preprocessed student image information, and identifying the current scene state and the scene development trend; identifying student behaviors according to the current scene state and the scene development trend, and calculating a campus tyrannosaurus occurrence index; and when the campus rabdosia occurence index is larger than a preset threshold value, giving an alarm. The invention can discover and stop the campus cheating behavior in time before the campus cheating occurs, does not need monitoring personnel to monitor and identify real-time figures of all areas of the campus, and has higher automation and intelligence degree.
Description
Technical Field
The invention relates to the technical field of smart campuses, in particular to a smart campus monitoring system and method for prejudging campus overlord.
Background
Campus rabdosia is a common social phenomenon, which mostly occurs in teenager groups. Campus overloads are considered to be an important factor in juvenile depression, learning failures and even suicide. The form of campus overlord comprises: physical violence, speech cheating, personal finance destroyed and the like, wherein the physical violence most seriously damages teenagers and the physical and mental health of the teenagers. When the student is deceived by other students in the campus, the student is difficult to take off the body and report to security personnel or teachers, and bystanders are afraid of persuading or informing the teachers, so that the deceived student can deceive for a long time by other students, and can cause physical harm and life threat.
In the conventional campus management, cameras are generally used to monitor various areas of a school. The camera is used for collecting real-time pictures in a campus and then sending the pictures to the monitoring room, monitoring personnel monitor the pictures in the control room, real-time monitoring is carried out on the pictures by the monitoring personnel in the mode, the pictures of the cameras are monitored, the deceiving behavior cannot be timely found and the pictures can be timely caught to the site to be stopped, and the monitoring efficiency is low.
Disclosure of Invention
Based on this, in order to solve the problems that the prior campus management needs monitoring personnel to monitor the pictures in real time, and needs to monitor the pictures of a plurality of cameras, so that the deception behavior cannot be found in time and the pictures can not be stopped on site in time, and the monitoring efficiency is low, the invention provides an intelligent campus monitoring system and method for prejudging campus scurry, and the specific technical scheme is as follows:
a smart campus monitoring system for prejudging campus overlord comprises an image acquisition module, a scene analysis module, a behavior prejudging module and an alarm module.
The image acquisition module is used for acquiring student image information in the area and preprocessing the student image information.
And the scene analysis module is used for carrying out scene analysis on the preprocessed student image information and identifying the current scene state and the scene development trend.
The behavior pre-judging module is used for identifying the behaviors of the students according to the current scene state and the scene development trend and calculating the campus rabdosia occurence index.
The alarm module is used for giving an alarm when the campus tyrant occurrence index is larger than a preset threshold value.
A wisdom campus monitoring system for prejudge campus scurry is through obtaining student's image and discerning current situation and sight development trend, and discern student's action according to current situation and sight development trend, calculate campus scurry emergence index, can in time discover and stop campus scurry's action before campus scurry takes place, it need not monitor each regional real-time portrait of discernment campus, automatic intelligent degree is higher, campus scurry's monitoring efficiency has been improved, it needs monitor personnel to carry out real time monitoring to the picture to solve current campus management, and need monitor the picture of a plurality of cameras, often can not in time discover the behavior of scurry and in time arrive the scene and stop, the lower problem of monitoring efficiency.
Further, wisdom campus monitoring system still includes:
the voice acquisition module is used for acquiring voice information in the area;
the text extraction module is used for performing text conversion on the voice information and extracting keywords in the text;
the frequency calculation module is used for calculating the occurrence frequency of the keywords;
the behavior pre-judging module is used for identifying the behaviors of the students according to the current scene state, the scene development trend and the occurrence frequency of the keywords and calculating the campus tyrant occurrence index.
Further, the scene analysis module includes:
the feature extraction unit is used for extracting the posture features and the limb key points of the preprocessed student images;
and the scene analysis unit is used for identifying the current scene state and the scene development trend according to the posture characteristics and the limb key points.
Further, wisdom campus monitoring system still includes keyword memory module, and keyword memory module is used for storing predetermined campus ba rabdosia keyword text.
A smart campus monitoring method for prejudging campus overlord comprises the following steps:
acquiring student image information in an area and preprocessing the student image information;
performing scene analysis on the preprocessed student image information, and identifying the current scene state and the scene development trend;
identifying student behaviors according to the current scene state and the scene development trend, and calculating a campus tyrannosaurus occurrence index;
and when the campus rabdosia occurence index is larger than a preset threshold value, giving an alarm.
Further, the smart campus monitoring method further comprises the following steps:
acquiring voice information in an area;
performing text conversion on the voice information and extracting keywords in the text;
calculating the occurrence frequency of the keywords;
and identifying the student behaviors according to the current scene state, the scene development trend and the occurrence frequency of the keywords, and calculating the campus rabdosia occurence index.
Further, the specific method for performing scene analysis on the preprocessed student image information and identifying the current scene state and the scene development trend comprises the following steps:
extracting the posture characteristics and limb key points of the preprocessed student image;
and identifying the current scene state and the scene development trend according to the posture characteristics and the limb key points.
A computer-readable storage medium storing a computer program which, when executed, implements the smart campus monitoring method.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the smart campus monitoring method when executing the computer program.
Drawings
The invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate corresponding parts throughout the different views.
FIG. 1 is a schematic overall flow chart of a smart campus monitoring method for pre-judging campus overlord in an embodiment of the present invention;
fig. 2 is a schematic flow chart of a smart campus monitoring method for pre-judging campus overlord according to another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to embodiments thereof. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only and do not represent the only embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The terms "first" and "second" used herein do not denote any particular order or quantity, but rather are used to distinguish one element from another.
The first embodiment is as follows:
a smart campus monitoring system for prejudging campus overlord comprises an image acquisition module, a scene analysis module, a behavior prejudging module and an alarm module.
The image acquisition module is used for acquiring student image information in the area and preprocessing the student image information.
The image acquisition module comprises cameras and an image preprocessing unit which are installed in different areas of a campus. In each school garden area, install more than one camera at least to obtain student's image information in the region better, make things convenient for scene analysis module to carry out scene analysis to the student's image information after the preliminary treatment. That is, by the image obtaining unit, image information of a plurality of students in a certain area of the campus can be obtained, and the image information of the plurality of students has different visual effects, such as front image vision and side image vision.
And the scene analysis module is used for carrying out scene analysis on the preprocessed student image information and identifying the current scene state and the scene development trend.
Specifically, the scene analysis module includes a feature extraction unit and a scene analysis unit.
The feature extraction unit is used for extracting the posture features and the limb key points of the preprocessed student images.
The scene analysis unit is used for identifying the current scene state and the scene development trend according to the posture characteristics and the limb key points.
The key points of the limbs include the head, body, hands and feet. Contextual states include, but are not limited to, general state, intimate state, quarreling state, dispute state, push-pull, and fighting state.
And the scene analysis unit identifies scene states according to the distances between the key points of the limbs of different students in the student images and the keeping time of the distances. Meanwhile, the scene analysis unit identifies the scene development trend according to the increasing and decreasing trend of the distance between the key points of different student limbs in the student image,
the behavior pre-judging module is used for identifying the behaviors of the students according to the current scene state and the scene development trend and calculating the campus rabdosia occurence index.
Specifically, in the smart campus monitoring system, different scene state coefficients and scene development trend values are preset, for example, coefficients that are different and gradually increase or decrease may be set for a normal state, an intimate state, a quarrel state, a dispute state, a push-pull state, and a fighting state. More specifically, one of the schemes is that the ordinary state, the close state, the quarrel state, the dispute state, the push-pull state, and the fighting state coefficients are set to 0.1, 0.3, 0.5, 0.7, 0.9, and 1.1, respectively.
The scenario development trend value is calculated according to the trend of increasing and decreasing distances between the student limb key points. For example, the situation trend value may be a variation of a distance between the key points of the limbs of the student within a preset time period.
The campus rabdosia occurrence index is the product of the scene state coefficient and the scene development trend value.
The alarm module is used for giving an alarm when the campus tyrant occurrence index is larger than a preset threshold value.
A wisdom campus monitoring system for prejudge campus scurry is through obtaining student's image and discerning current situation and sight development trend, and discern student's action according to current situation and sight development trend, calculate campus scurry emergence index, can in time discover and stop campus scurry's action before campus scurry takes place, it need not monitor each regional real-time portrait of discernment campus, automatic intelligent degree is higher, campus scurry's monitoring efficiency has been improved, it needs monitor personnel to carry out real time monitoring to the picture to solve current campus management, and need monitor the picture of a plurality of cameras, often can not in time discover the behavior of scurry and in time arrive the scene and stop, the lower problem of monitoring efficiency.
The second embodiment:
a smart campus monitoring system for prejudging campus overlord comprises an image acquisition module, a scene analysis module, a behavior prejudging module and an alarm module.
The image acquisition module is used for acquiring student image information in the area and preprocessing the student image information.
The image acquisition module comprises cameras and an image preprocessing unit which are installed in different areas of a campus. In each school garden area, install more than one camera at least to obtain student's image information in the region better, make things convenient for scene analysis module to carry out scene analysis to the student's image information after the preliminary treatment. That is, by the image obtaining unit, image information of a plurality of students in a certain area of the campus can be obtained, and the image information of the plurality of students has different visual effects, such as front image vision and side image vision.
And the scene analysis module is used for carrying out scene analysis on the preprocessed student image information and identifying the current scene state and the scene development trend.
Specifically, the scene analysis module includes a feature extraction unit and a scene analysis unit.
The feature extraction unit is used for extracting the posture features and the limb key points of the preprocessed student images.
The scene analysis unit is used for identifying the current scene state and the scene development trend according to the posture characteristics and the limb key points.
The key points of the limbs include the head, body, hands and feet. Contextual states include, but are not limited to, general state, intimate state, quarreling state, dispute state, push-pull, and fighting state.
And the scene analysis unit identifies scene states according to the distances between the key points of the limbs of different students in the student images and the keeping time of the distances. Meanwhile, the scene analysis unit identifies the scene development trend according to the increasing and decreasing trend of the distance between the key points of different student limbs in the student image,
the behavior pre-judging module is used for identifying the behaviors of the students according to the current scene state and the scene development trend and calculating the campus rabdosia occurence index.
Specifically, in the smart campus monitoring system, different scene state coefficients and scene development trend values are preset, for example, coefficients that are different and gradually increase or decrease may be set for a normal state, an intimate state, a quarrel state, a dispute state, a push-pull state, and a fighting state. More specifically, one of the schemes is that the ordinary state, the close state, the quarrel state, the dispute state, the push-pull state, and the fighting state coefficients are set to 0.1, 0.3, 0.5, 0.7, 0.9, and 1.1, respectively.
The scenario development trend value is calculated according to the trend of increasing and decreasing distances between the student limb key points. For example, the situation trend value may be a variation of a distance between the key points of the limbs of the student within a preset time period.
The campus rabdosia occurrence index is the product of the scene state coefficient and the scene development trend value.
The alarm module is used for giving an alarm when the campus tyrant occurrence index is larger than a preset threshold value.
A wisdom campus monitoring system for prejudge campus scurry is through obtaining student's image and discerning current situation and sight development trend, and discern student's action according to current situation and sight development trend, calculate campus scurry emergence index, can in time discover and stop campus scurry's action before campus scurry takes place, it need not monitor each regional real-time portrait of discernment campus, automatic intelligent degree is higher, campus scurry's monitoring efficiency has been improved, it needs monitor personnel to carry out real time monitoring to the picture to solve current campus management, and need monitor the picture of a plurality of cameras, often can not in time discover the behavior of scurry and in time arrive the scene and stop, the lower problem of monitoring efficiency.
In this embodiment, the smart campus monitoring system further includes a voice obtaining module, a text extracting module, a frequency calculating module, and a keyword storing module.
The voice acquisition module is used for acquiring voice information in the region, the text extraction module is used for performing text conversion on the voice information and extracting keywords in the text, and the frequency calculation module is used for calculating the occurrence frequency of the keywords. The keyword storage module is used for storing preset campus overlord keyword texts. The behavior pre-judging module is used for identifying the student behaviors according to the current scene state, the scene development trend and the occurrence frequency of the keywords and calculating the campus tyrant occurrence index.
Here, keywords refer to words related to campus overlook. Generally speaking, the keywords related to the campus tyrant can be stored in the keyword storage module through a presetting and screening mode.
Specifically, the campus tyrant occurrence index is a product of a scene state coefficient, a scene development trend and a keyword occurrence frequency value.
Through setting up pronunciation acquisition module, text extraction module, frequency calculation module and keyword storage module, can calculate campus rabdosia emergence index more accurately to monitor and prejudge campus rabdosia more accurately.
Example three:
as shown in fig. 1, the present invention provides a smart campus monitoring method for prejudging campus overlord, which includes the following steps:
and S1, acquiring the image information of the students in the area and preprocessing the image information of the students.
And S2, performing scene analysis on the preprocessed student image information, and identifying the current scene state and the scene development trend.
And S3, recognizing the student behaviors according to the current scene state and the scene development trend, and calculating the campus blurry incidence index.
And S4, when the campus rabdosia occurence index is larger than a preset threshold value, giving an alarm.
Specifically, the contextual status includes, but is not limited to, a normal status, an intimate status, a quarreling status, a dispute status, a push-pull status, and a fighting status. The method for acquiring the scene state comprises the following steps: and acquiring the distance between the key points of the limbs of the students and the keeping time of the distance, and judging and identifying different scene states according to the distance between the key points of the limbs of the students and the keeping time of the distance. More specifically, the method for acquiring a context state further includes: acquiring position coordinates of each student in the student image information and position variation in a preset time period; and judging and identifying different scene states according to the distance between the key points of the limbs of the students, the keeping time of the distance, the position coordinate of each student and the position variation in a preset time period.
Due to different scene states, the distance between the key points of the limbs of the students, the keeping time of the distance, the position coordinates of each student and the position variation in the preset time period are different. Different scene states can be judged and identified better by obtaining the distance between the key points of the limbs of the students, the keeping time of the distance, the position coordinates of each student and the position variation in a preset time period.
The application is practical, and the distance between the key points of the limbs of the students, the keeping time of the distance, the position coordinates of each student and the position variation in the preset time period correspond to different preset range values corresponding to different scene states. According to the method, different scene states are judged and identified according to the distance between the key points of the limbs of the students, the keeping time of the distance, the position coordinates of each student and the size relation between the position variation and the corresponding preset range value in the preset time period.
The intelligent campus monitoring method is characterized in that the images of students are obtained, the current scene state and the scene development trend are identified, the behaviors of the students are identified according to the current scene state and the scene development trend, the campus bloom occurrence index is calculated, the campus bloom behaviors can be timely found and stopped before the campus bloom occurs, monitoring personnel are not needed to monitor and identify real-time images of all areas of a campus, the automation intelligence degree is high, the monitoring efficiency of the campus bloom behaviors is improved, the problem that the prior campus management needs the monitoring personnel to monitor the images in real time, the images of a plurality of cameras need to be monitored, the bloom behaviors cannot be timely found and stopped timely on site, and the monitoring efficiency is low is solved.
Example four:
it should be understood that this embodiment at least includes all technical solutions of the above embodiments, and further detailed description is provided on the basis of the above embodiments.
In this embodiment, the smart campus monitoring method further includes the steps of:
and S5, acquiring the voice information in the region.
S6, performing text conversion on the voice information and extracting keywords in the text.
S7, calculating the occurrence frequency of the keyword.
And identifying the student behaviors according to the current scene state, the scene development trend and the occurrence frequency of the keywords, and calculating the campus rabdosia occurence index.
In step S2, as shown in fig. 2, the specific method for performing scene analysis on the preprocessed student image information and identifying the current scene state and the scene development trend includes the following steps:
and S20, extracting the posture features and the limb key points of the preprocessed student image.
And S21, identifying the current scene state and the scene development trend according to the posture characteristics and the limb key points.
Specifically, the campus tyrant occurrence index is a product of a scene state coefficient, a scene development trend and a keyword occurrence frequency value.
Through setting up pronunciation acquisition module, text extraction module, frequency calculation module and keyword storage module, can calculate campus rabdosia emergence index more accurately to monitor and prejudge campus rabdosia more accurately.
As a preferred technical solution, in this embodiment, the smart campus monitoring method further includes the following steps:
identifying each student body contour line in the preprocessed student image information;
judging and identifying whether a student is in a curling defense state and an attack state according to the body contour line of the student;
if the students are in the curling defense state and the attack state, the campus overlook is judged to occur, and the alarm module sends out an alarm and sends alarm information to the campus management platform or intelligent terminals bound with different school managers.
Specifically, the method for judging and identifying the anti-roll-back state comprises the following steps: whether the hands and the feet of the student are in the contraction state or not is judged according to the body contour line analysis of the student, if yes, whether the hands and the feet of the student have contact areas or not is judged, and if yes, whether the students are in the curling defense state or not is judged and recognized.
The method for judging and identifying the attack state comprises the following steps: and if judging and identifying that at least one student is in the curling defense state, analyzing and judging body contour lines of other students, and if the hands and/or feet of the students are in the over-extension state in a preset time period and the extension frequency is greater than a preset value, judging that the student is in the attack state.
By the method, whether the campus rabdosia occurs can be identified, and further development of the campus rabdosia is prevented in time.
The present embodiment provides a computer-readable storage medium and an electronic device, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the computer program implements the smart campus monitoring method. The electronic equipment comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and the intelligent campus monitoring method is realized when the processor executes the computer program.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (9)
1. The utility model provides a wisdom campus monitoring system for prejudging campus overlord, its characterized in that wisdom campus monitoring system includes:
the image acquisition module is used for acquiring student image information in the area and preprocessing the student image information;
the scene analysis module is used for carrying out scene analysis on the preprocessed student image information and identifying the current scene state and the scene development trend;
the behavior pre-judging module is used for identifying the behaviors of the students according to the current scene state and the scene development trend and calculating the campus blurry incidence index;
and the alarm module is used for giving an alarm when the campus tyrant occurrence index is greater than a preset threshold value.
2. The smart campus monitoring system of claim 1 wherein said smart campus monitoring system further comprises:
the voice acquisition module is used for acquiring voice information in the area;
the text extraction module is used for performing text conversion on the voice information and extracting keywords in the text;
the frequency calculation module is used for calculating the occurrence frequency of the keywords;
the behavior pre-judging module is used for identifying the behaviors of the students according to the current scene state, the scene development trend and the occurrence frequency of the keywords and calculating the campus tyrant occurrence index.
3. The intelligent campus monitoring system of claim 2 wherein the context analysis module comprises:
the feature extraction unit is used for extracting the posture features and the limb key points of the preprocessed student images;
and the scene analysis unit is used for identifying the current scene state and the scene development trend according to the posture characteristics and the limb key points.
4. The smart campus monitoring system of claim 3, wherein the smart campus monitoring system further comprises a keyword storage module, the keyword storage module is configured to store a predetermined campus overlord keyword text.
5. A smart campus monitoring method for prejudging campus overlord is characterized by comprising the following steps:
acquiring student image information in an area and preprocessing the student image information;
performing scene analysis on the preprocessed student image information, and identifying the current scene state and the scene development trend;
identifying student behaviors according to the current scene state and the scene development trend, and calculating a campus tyrannosaurus occurrence index;
and when the campus rabdosia occurence index is larger than a preset threshold value, giving an alarm.
6. The smart campus monitoring method of claim 5 wherein said smart campus monitoring method further comprises the steps of:
acquiring voice information in an area;
performing text conversion on the voice information and extracting keywords in the text;
calculating the occurrence frequency of the keywords;
and identifying the student behaviors according to the current scene state, the scene development trend and the occurrence frequency of the keywords, and calculating the campus rabdosia occurence index.
7. The intelligent campus monitoring method for prejudging campus overlord as claimed in claim 5, wherein the specific method for performing scene analysis on the preprocessed student image information and identifying the current scene state and the scene development trend comprises the following steps:
extracting the posture characteristics and limb key points of the preprocessed student image;
and identifying the current scene state and the scene development trend according to the posture characteristics and the limb key points.
8. A computer readable storage fluid medium storing a computer program which when executed implements a method for smart campus monitoring as claimed in any one of claims 5 to 7.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the smart campus monitoring method of any one of claims 5 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210459747.8A CN114821422A (en) | 2022-04-24 | 2022-04-24 | Intelligent campus monitoring system and method for prejudging campus overlord |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210459747.8A CN114821422A (en) | 2022-04-24 | 2022-04-24 | Intelligent campus monitoring system and method for prejudging campus overlord |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114821422A true CN114821422A (en) | 2022-07-29 |
Family
ID=82509053
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210459747.8A Pending CN114821422A (en) | 2022-04-24 | 2022-04-24 | Intelligent campus monitoring system and method for prejudging campus overlord |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114821422A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117237155A (en) * | 2023-10-28 | 2023-12-15 | 南京达尔晟信息科技有限公司 | Intelligent campus student behavior analysis system based on artificial intelligence |
-
2022
- 2022-04-24 CN CN202210459747.8A patent/CN114821422A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117237155A (en) * | 2023-10-28 | 2023-12-15 | 南京达尔晟信息科技有限公司 | Intelligent campus student behavior analysis system based on artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI686774B (en) | Human face live detection method and device | |
| CN109670441A (en) | A kind of realization safety cap wearing knows method for distinguishing, system, terminal and computer readable storage medium | |
| CN113705425B (en) | Training method of living body detection model, and method, device and equipment for living body detection | |
| CN110674790B (en) | Abnormal scene processing method and system in video monitoring | |
| CN113191699A (en) | Power distribution construction site safety supervision method | |
| CN112437280A (en) | Intelligent monitoring security data processing management system based on big data analysis | |
| CN109639700A (en) | Personal identification method, device, equipment, cloud server and storage medium | |
| CN113221771B (en) | Living body face recognition method, device, apparatus, storage medium and program product | |
| CN115547497A (en) | Myopia prevention and control system and method based on multi-source data | |
| CN114821422A (en) | Intelligent campus monitoring system and method for prejudging campus overlord | |
| CN112819068A (en) | Deep learning-based real-time detection method for ship operation violation behaviors | |
| CN109993044A (en) | Telecommunication fraud identifying system, method, apparatus, electronic equipment and storage medium | |
| CN111191507A (en) | Safety early warning analysis method and system for smart community | |
| CN113408464A (en) | Behavior detection method and device, electronic equipment and storage medium | |
| CN111104917A (en) | Face-based living body detection method and device, electronic equipment and medium | |
| CN112183356A (en) | Driving behavior detection method and device and readable storage medium | |
| CN110795971B (en) | User behavior identification method, device, equipment and computer storage medium | |
| CN113393628A (en) | Intelligent monitoring alarm method, system, intelligent terminal and storage medium | |
| CN115690892B (en) | Mitigation method and device, electronic equipment and storage medium | |
| CN111738043A (en) | Pedestrian re-identification method and device | |
| CN115049988A (en) | Edge calculation method and device for power distribution network monitoring and prejudging | |
| CN110334669B (en) | Morphological feature recognition method and equipment | |
| US11019087B1 (en) | Computer vision-based intelligent anomaly detection using synthetic and simulated data-system and method | |
| CN114241359A (en) | Abnormal behavior detection method, system and device in industrial environment | |
| CN113835950A (en) | Interface display stuck identification method and device, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |