CN111209814A - Face recognition campus area early warning method and system for K12 education stage - Google Patents
Face recognition campus area early warning method and system for K12 education stage Download PDFInfo
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Abstract
The invention discloses a campus area early warning method and a campus area early warning system for face recognition in a K12 education stage, wherein the campus area early warning method comprises the following steps: defining an area needing to be controlled, and setting a safety and early warning range; judging whether the personnel appearing in the video are internal personnel or not, analyzing the advancing direction of the human body appearing in the video, and judging whether the personnel have the tendency of entering a dangerous area or not; and screening the personnel entering the control area, and reminding security personnel to pay attention to the abnormal state of the control area. According to the invention, by adopting a region identification mode, based on face identification and behavior analysis, the personnel entering the control region are identified doubly, so that the human body and the object can be identified clearly, adults and infants, and personnel outside and inside the school can be further distinguished, the false alarm rate in the existing dangerous region early warning technology is effectively reduced, the alarm is sent out in a targeted manner, and the early warning accuracy is improved.
Description
Technical Field
The invention relates to the technical field of face recognition safety early warning, in particular to a face recognition campus area early warning method and system used in a K12 education stage.
Background
In the operation process of a campus, in order to meet the requirements of learning and activities of children, various places with different functions are established, but not all places are suitable for children to come in and go out. Children in the infancy stage are curious and without safety awareness. Although there are many safety settings, children are numerous and careless.
The existing safety management mainly comprises video monitoring, but children cannot be found to be in dangerous areas in time only by means of management and control of staff of teachers. The current dangerous area monitoring technology is to compare a real-time shot image with an original image, and if the real-time image is detected to be inconsistent with the original image and an object which does not belong to the original image appears in several continuous frames to form a motion track, an alarm is given. The identification mode causes that any moving object can mistakenly touch the alarm, and the energy of monitoring personnel is easily consumed. When the infant actually breaks into the dangerous area, the 'wolf' effect is likely to be caused, so that the accident occurs without enough attention.
Disclosure of Invention
The invention aims to provide a campus area early warning method and system for face recognition in the K12 education stage, and solves the technical problems that any moving object is likely to mistakenly touch an alarm and the energy of monitoring personnel is easily consumed due to the recognition mode in the existing campus dangerous area monitoring technology.
The invention discloses a face recognition campus area early warning method used in a K12 education stage, which comprises the following steps:
establishing a dangerous area model, defining an area to be controlled, and setting a safety and early warning range;
judging whether the personnel appearing in the video are internal personnel or not, analyzing the advancing direction of the human body appearing in the video, and judging whether the personnel have the tendency of entering a dangerous area or not;
and screening the personnel entering the control area, and reminding security personnel to pay attention to the abnormal state of the control area.
The invention discloses a face recognition campus area early warning system for K12 education, which comprises:
the dangerous area defining module is used for defining an area needing to be controlled and setting a safety and early warning range;
the behavior analysis module is used for judging whether the person appearing in the video is an internal person, analyzing the advancing direction of the human body appearing in the video and judging whether the person has a tendency of entering a dangerous area;
and the early warning module is used for screening personnel entering the control area and reminding security personnel to pay attention to the abnormal state of the control area.
According to the campus area early warning method and system for face recognition in the K12 education stage, a regional recognition mode is adopted, based on face recognition and behavior analysis, personnel entering a control area are identified doubly, human bodies and objects can be identified clearly, and outside-school personnel and inside-school personnel are further distinguished, so that the false alarm rate in the existing dangerous area early warning technology is effectively reduced, specific alarm is given out, the early warning accuracy is improved, the probability that any moving object possibly touches the alarm by mistake in the recognition process is reduced, and the technical problem that the energy of monitoring personnel is easily consumed is solved.
Drawings
FIG. 1 is a flow chart of pedestrian trend determination according to the present invention;
FIG. 2 is a flow chart of the present invention for defining a hazardous area;
FIG. 3 is a flow chart of the face detection of the present invention;
FIG. 4 is a flow chart of behavior determination according to the present invention;
fig. 5 is a schematic diagram of an operation flow of a campus risk area early warning system module based on face recognition in the K12 education stage.
Detailed Description
As shown in fig. 1, a campus area early warning method for face recognition in K12 education stage includes the following steps:
establishing a dangerous area model, defining an area to be controlled, and setting a safety and early warning range;
judging whether the person appearing in the video is an internal person, analyzing the advancing direction of the human body appearing in the video, and judging whether the person has a tendency of entering a dangerous area;
and screening the personnel entering the control area, and reminding security personnel to pay attention to the abnormal state of the control area.
As shown in fig. 2, defining an area to be managed and controlled, and setting a safety and early warning range specifically includes:
establishing a dangerous area model in a dangerous area needing to be controlled;
intercepting the shot video according to a preset time period to form a plurality of video streams for storage;
and randomly extracting a frame of picture of the video stream, judging whether an object conforming to the dangerous area model exists in the identification area or not according to a pre-designed dangerous area model, and taking the peripheral fixed distance conforming to the dangerous area model as an early warning range.
In the operation process of the camera device, the shot video is intercepted according to a preset time period to form a plurality of video streams for storage. And randomly extracting a frame of picture according to the video stream, and judging whether an object conforming to the dangerous area model exists in the identification area or not according to a pre-designed dangerous area model. If so, judging the area as a dangerous area, and taking the fixed distance at the periphery of the area as an early warning range.
As shown in fig. 3, determining whether a person appearing in a video is an insider specifically includes:
randomly extracting a frame of picture according to the video stream, carrying out face detection, and judging whether the picture has facial features meeting the requirements;
and cutting the picture with the detected facial features, extracting a portrait template in the repository, and comparing the portrait template with the portrait features.
According to the video stream, a frame of picture is randomly extracted, face detection is carried out, and whether the picture has facial features meeting requirements or not is judged. If the facial features meeting the requirements are detected, cutting the pictures of the detected facial features, extracting a portrait template in the repository, and comparing the portrait features for comparison. If the comparison is successful, judging the personnel to be internal personnel; if the comparison is unsuccessful, the personnel is judged to be external personnel.
As shown in fig. 4, analyzing the human body traveling direction appearing in the video to determine whether the human body has a tendency to enter a dangerous area specifically includes:
randomly extracting a frame of picture according to the video stream, detecting the skeleton characteristics of the human body, and judging whether a pedestrian exists in the picture;
according to the human skeleton positioning of the pedestrians in the video, the human body advancing trend is calculated by applying the distance from the point to the hyperplane, and the calculation equation of the hyperplane is written into the following form:
calculating the distance from the sample point to the plane, wherein x is a point in the sample, and w is expressed as a characteristic variable, and the distance from the point to the hyperplane can be calculated by the following formula:
The method comprises the steps of randomly extracting a frame of picture according to a video stream, detecting human skeleton characteristics, judging whether a pedestrian exists in the picture, positioning according to the human skeleton of the pedestrian in the video if the pedestrian exists, calculating the advancing trend of the human body by using the distance from a point to a hyperplane, and continuously acquiring radio frequency through a camera if the pedestrian does not exist.
And when the trend that the human body moves in the direction is close to the dangerous area is calculated, if the obtained result is judged to be close, screening the personnel entering the control area, and reminding security personnel to pay attention to the abnormal state of the control area. And for the video stream with the early warning, the local server uploads the video stream to the cloud server for backup, and other video streams are abandoned. If the obtained result is judged to have no approaching trend, after the pedestrian leaves the monitoring range of the camera, the video stream is automatically abandoned, and only the extracted picture is stored.
The method adopts a region identification mode, based on face identification and behavior analysis, doubly identifies personnel entering a control region, can clearly identify human bodies and objects, and further distinguishes personnel outside and inside the school, effectively reduces the false alarm rate in the existing dangerous region early warning technology, has the advantages of pertinently sending out an alarm, improves the early warning accuracy, and eliminates the technical problem that any moving object generated in the identification process is likely to mistakenly touch the alarm and easily consumes the energy of monitoring personnel.
As shown in fig. 5, a face recognition campus area early warning system for K12 education stage includes:
the dangerous area defining module is used for defining an area needing to be controlled and setting a safety and early warning range;
the behavior analysis module is used for judging whether the person appearing in the video is an internal person, analyzing the advancing direction of the human body appearing in the video and judging whether the person has a tendency of entering a dangerous area;
and the early warning module is used for screening personnel entering the control area and reminding security personnel to pay attention to the abnormal state of the control area.
Wherein, the hazardous area defining module comprises:
the danger area model module is used for establishing a danger area model in a danger area needing to be controlled;
the storage module is used for intercepting the shot video according to a preset time period to form a plurality of video streams for storage;
and the judgment and identification module is used for randomly extracting a frame of picture of the video stream, judging whether an object conforming to the dangerous area model exists in the identification area or not according to a pre-designed dangerous area model, and taking the peripheral fixed distance conforming to the dangerous area model as an early warning range.
The behavior analysis module comprises:
the face recognition module is used for judging whether the personnel appearing in the video are internal personnel or not;
and the human body advancing trend analysis module is used for analyzing the advancing direction of the human body appearing in the video and judging whether the human body has a trend of entering a dangerous area.
The face recognition module includes:
the face detection module is used for randomly extracting a frame of picture according to the video stream, carrying out face detection and judging whether the picture has facial features meeting the requirements;
and the face and face feature comparison module is used for cutting the pictures with the detected face features, extracting a portrait template in the repository, and comparing the portrait features.
The human body advancing trend analysis module comprises:
the human body skeleton characteristic detection module is used for randomly extracting a frame of picture according to the video stream, detecting human body skeleton characteristics and judging whether a pedestrian exists in the picture;
the hyperplane calculation module is used for calculating the human body advancing trend by applying the distance from the point to the hyperplane according to the human body skeleton positioning of the pedestrian in the video, and the calculation equation of the hyperplane is written into the following form:
calculating the distance from the sample point to the plane, wherein x is a point in the sample, and w is expressed as a characteristic variable, and the distance from the point to the hyperplane can be calculated by the following formula:
The method adopts a region identification mode, based on face identification and behavior analysis, doubly identifies personnel entering a control region, can clearly identify human bodies and objects, and further distinguishes personnel outside and inside the school, effectively reduces the false alarm rate in the prior dangerous region early warning technology, sends out alarms in a targeted manner, improves the early warning accuracy, reduces the probability that any moving object is likely to be mistakenly touched by the alarm in the identification process, and solves the technical problem that the energy of monitoring personnel is easily consumed by false alarm.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A face recognition campus area early warning method for K12 education is characterized by comprising the following steps:
establishing a dangerous area model, defining an area to be controlled, and setting a safety and early warning range;
judging whether the person appearing in the video is an internal person, analyzing the advancing direction of the human body appearing in the video, and judging whether the person has a tendency of entering a dangerous area;
and screening the personnel entering the control area, and reminding security personnel to pay attention to the abnormal state of the control area.
2. The campus area early warning method for face recognition in the K12 education phase as claimed in claim 1, wherein the method for establishing a danger area model, defining an area to be controlled, and setting a safety and early warning range specifically includes:
establishing a dangerous area model in a dangerous area needing to be controlled, and setting a safety and early warning range;
intercepting the shot video according to a preset time period to form a plurality of video streams for storage;
and randomly extracting a frame of picture of the video stream, judging and identifying whether an object conforming to the dangerous area model exists in the shooting area or not according to a pre-designed dangerous area model, and taking the peripheral fixed distance conforming to the dangerous area model as an early warning range.
3. The campus area early warning method for face recognition in the K12 education stage as claimed in claim 1, wherein the determining whether the people appearing in the video are insiders specifically includes:
randomly extracting a frame of picture according to the video stream, carrying out face detection, and judging whether the picture has facial features meeting the requirements;
and cutting the picture with the detected facial features, extracting a portrait template in the repository, comparing the portrait template with the portrait features, and further distinguishing the persons in the school and the persons outside the school.
4. The campus area early warning method for face recognition in the K12 education stage as claimed in claim 1, wherein analyzing the human body traveling direction appearing in the video to determine whether it has a tendency to enter a dangerous area includes:
randomly extracting a frame of picture according to the video stream, detecting the skeleton characteristics of the human body, and judging whether a pedestrian exists in the picture;
according to the human skeleton positioning of the pedestrians in the video, the human body advancing trend is calculated by applying the distance from the point to the hyperplane, and the calculation equation of the hyperplane is written into the following form:
calculating the distance from the sample point to the plane, wherein x is a point in the sample, and w is expressed as a characteristic variable, and the distance from the point to the hyperplane can be calculated by the following formula:
5. The campus area pre-warning method for face recognition in the K12 educational phase, wherein when it is calculated that the human body moving trend is not close to the dangerous area trend, the video stream is abandoned and only the extracted pictures are saved.
6. A face identification campus area early warning system for K12 education phase, its characterized in that includes:
the dangerous area defining module is used for establishing a dangerous area model, defining an area needing to be controlled and setting a safety and early warning range;
the behavior analysis module is used for judging whether the person appearing in the video is an internal person or not, analyzing the advancing direction of the human body appearing in the video and judging whether the person has a tendency of entering a dangerous area or not;
and the early warning module is used for screening personnel entering the control area and reminding security personnel to pay attention to the abnormal state of the control area.
7. The face recognition campus area early warning system for the K12 education session as claimed in claim 6, wherein the danger area defining module includes:
the danger area model module is used for establishing a danger area model in a danger area needing to be controlled;
the storage module is used for intercepting the shot video according to a preset time period to form a plurality of video streams for storage;
and the judgment and identification module is used for randomly extracting a frame of picture of the video stream, judging whether an object conforming to the dangerous area model exists in the identification area or not according to a pre-designed dangerous area model, and taking the peripheral fixed distance conforming to the dangerous area model as an early warning range.
8. The face recognition campus area early warning system for the K12 educational phase of claim 6, wherein the behavior analysis module comprises:
the face recognition module is used for judging whether the personnel appearing in the video are internal personnel or not;
and the human body advancing trend analysis module is used for analyzing the advancing direction of the human body appearing in the video and judging whether the human body has a trend of entering a dangerous area.
9. The face recognition campus area early warning system for the K12 educational phase, wherein the face recognition module comprises:
the face detection module is used for randomly extracting a frame of picture in the video stream, carrying out face detection and judging whether the picture has facial features meeting requirements;
and the face and face feature comparison module is used for cutting the pictures with the detected face features, extracting a portrait template in the repository, and comparing the portrait features.
10. The face recognition campus area early warning system for the K12 educational phase, wherein the human body progression tendency analysis module comprises:
the human body skeleton characteristic detection module is used for randomly extracting a frame of picture according to the video stream, detecting human body skeleton characteristics and judging whether a pedestrian exists in the picture;
the hyperplane calculation module is used for calculating the human body advancing trend by applying the distance from the point to the hyperplane according to the human body skeleton positioning of the pedestrian in the video, and the calculation equation of the hyperplane is written into the following form:
calculating the distance from the sample point to the plane, wherein x is a point in the sample, and w is expressed as a characteristic variable, and the distance from the point to the hyperplane can be calculated by the following formula:
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