CN115223100A - Intelligent park abnormal person identification method, system, equipment and storage medium - Google Patents
Intelligent park abnormal person identification method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of personnel monitoring, and particularly discloses a method, a system, equipment and a storage medium for identifying abnormal personnel in an intelligent park. Whether a target person is a student in the park is judged by acquiring a monitoring video frame image of each monitoring point location in the park, if not, the traveling track of the target person is determined according to time information and point location number information corresponding to each frame image of the target person, and then the activity monitoring range of the target person is predicted, and through the identity recognition of the target person at the radio frequency recognition terminal of each monitoring point location in the range, if the recognition data packet fed back by each radio frequency recognition terminal does not have corresponding identity recognition information, the target person is judged to be an abnormal person. The intelligent monitoring system can realize intelligent and non-inductive monitoring and identification of abnormal personnel in the park, improve the monitoring and identification efficiency, instantaneity and accuracy of the abnormal personnel, and improve the safety precaution capability of the intelligent park.
Description
Technical Field
The invention belongs to the technical field of personnel monitoring, and particularly relates to a method, a system, equipment and a storage medium for identifying abnormal personnel in an intelligent park.
Background
The intelligent campus refers to an intelligent campus work, study and life integrated environment based on the Internet of things, and the integrated environment takes various application service systems as carriers to fully integrate teaching, scientific research, management and campus life.
The smart campus management comprises campus safety management, campus safety has close relations with teachers and students, parents and society, and good campus public security management plays an important role in guaranteeing safety of each student and each family. The occurrence of part of campus security incidents is caused by the mixing of external personnel, the current campus security management is mainly realized by means of campus door access security check management and campus duty polling, still has larger supervision loopholes and defects, cannot be well converged with the intelligent campus technology, and can timely and effectively identify, judge and track abnormal personnel mixed in a campus intelligently.
Disclosure of Invention
The invention aims to provide a method, a system, equipment and a storage medium for identifying abnormal personnel in an intelligent park, which are used for solving the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for identifying abnormal people in an intelligent park, including:
acquiring a monitoring video stream of each monitoring point location in a park, and acquiring a plurality of frame images and time information and point location number information corresponding to each frame image from the monitoring video stream;
extracting facial image feature information and physical image feature information of a target person from each frame image;
judging whether the target person is a park student or not according to the facial image feature information and the body appearance image feature information of the target person;
when the non-park students of the target person are judged, determining the traveling track of the target person according to the time information and the point number information corresponding to each frame of image of the target person;
predicting the subsequent activity monitoring range of the target personnel according to the advancing track of the target personnel;
sending an identification instruction to the radio frequency identification terminals of all monitoring point positions in the active monitoring range, and receiving identification data packets fed back by all the radio frequency identification terminals;
and if the identification data packets do not contain the identification information, judging that the target person is an abnormal person.
When the method is applied, whether a target person is a student in a park is judged by acquiring a monitoring video frame image and corresponding time information and point number information of each monitoring point in the park, if the target person is not judged to be the student in the park, the traveling track of the target person is determined according to the time information and point number information corresponding to each frame image of the target person, the subsequent activity monitoring range of the target person is predicted according to the traveling track, and then an identification instruction is sent to a radio frequency identification terminal of each monitoring point in the range to identify the identity of the target person through the radio frequency identification terminal. By the method, intelligent and noninductive monitoring and identification of abnormal personnel in the park can be realized, the monitoring and identification efficiency, instantaneity and accuracy of the abnormal personnel are improved, and the safety precaution capability of the intelligent park is improved.
In one possible design, the method further includes:
if a certain identification data packet contains identity identification information, calling corresponding pre-stored face image characteristic information from a worker database according to the identity identification information;
and performing similarity matching on the facial image feature information of the target person and the pre-stored facial image feature information, and if the matching similarity is smaller than a set similarity threshold, judging the target person to be an abnormal person.
In one possible design, the method further includes:
and after the target person is judged to be an abnormal person, early warning prompt information is sent to the on-duty terminal, and each frame image and the advancing track of the target person are sent to the on-duty terminal.
In a possible design, the sending an identification instruction to the rfid terminal at each monitoring point within the active monitoring range, and receiving an identification data packet fed back by each rfid terminal includes:
determining an information acquisition time period;
sending an identification instruction and an information acquisition time period to a radio frequency identification terminal of each monitoring point in an active monitoring range;
and receiving the identification data packet fed back by each radio frequency identification terminal in the information acquisition time period.
In one possible design, the acquiring the time information and the point location number information corresponding to the plurality of frame images from the surveillance video stream includes: extracting a plurality of frame images from the monitoring video stream, extracting corresponding time stamps and unique numbers from the frame images, generating time information according to the time stamps, and generating point position number information according to the unique numbers.
In one possible design, determining the travel track of the target person according to the time information and the point number information corresponding to each frame of image where the target person appears includes:
marking monitoring point positions corresponding to the point position number information in the garden map according to the point position number information corresponding to each frame of image;
and sequentially connecting the marked corresponding monitoring point positions according to the time information corresponding to each frame of image to form the advancing track of the target person.
In one possible design, the determining whether the target person is a campus student according to the facial image feature information and the physical image feature information of the target person includes: and (4) importing the facial image feature information and the body image feature information of the target person into a preset student information base for feature matching, and judging that the target person is a non-park student if the matching is not successful.
In a second aspect, the present invention provides an intelligent park abnormal person identification system, including an acquisition unit, an extraction unit, a judgment unit, a determination unit, a prediction unit, a transceiver unit, and a determination unit, wherein:
the acquisition unit is used for acquiring the monitoring video stream of each monitoring point position in the park and acquiring a plurality of frames of images and time information and point position number information corresponding to each frame of image from the monitoring video stream;
an extraction unit configured to extract facial image feature information and physical image feature information of a target person from each frame image;
the judging unit is used for judging whether the target person is a park student or not according to the facial image feature information and the body image feature information of the target person;
the determining unit is used for determining the advancing track of the target person according to the time information and the point number information corresponding to each frame of image of the target person when the non-park student of the target person is judged;
the prediction unit is used for predicting the subsequent activity monitoring range of the target personnel according to the advancing track of the target personnel;
the receiving and sending unit is used for sending an identification instruction to the radio frequency identification terminals of each monitoring point position in the activity monitoring range and receiving an identification data packet fed back by each radio frequency identification terminal;
and the judging unit is used for judging that the target person is an abnormal person when the identification data packets do not contain the identification information.
In a third aspect, the invention provides a computer apparatus comprising:
a memory to store instructions;
a processor configured to read the instructions stored in the memory and execute the method of any of the first aspects according to the instructions.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon instructions which, when run on a computer, cause the computer to perform the method of any of the first aspects described above.
In a fifth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of any one of the first aspects described above.
Has the beneficial effects that: the method comprises the steps of obtaining monitoring video frame images of monitoring points in a park and corresponding time information and point number information, extracting facial image features and physical image features of target personnel from the frame images, judging whether the target personnel are students in the park, determining the advancing track of the target personnel according to the time information and point number information corresponding to the frame images of the target personnel if the target personnel are judged not to be students in the park, predicting the subsequent activity monitoring range of the target personnel according to the advancing track, sending an identification instruction to a radio frequency identification terminal of each monitoring point in the range to identify the target personnel through the radio frequency identification terminal, and judging the target personnel to be abnormal personnel if identification data packets fed back by the radio frequency identification terminals do not have corresponding identification information, namely indicating that the identification information of the target personnel cannot be detected and the target personnel are not workers in the park. The intelligent monitoring system can realize intelligent and non-inductive monitoring and identification of abnormal personnel in the park, improve the monitoring and identification efficiency, instantaneity and accuracy of the abnormal personnel, and improve the safety precaution capability of the intelligent park.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of the process steps of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a schematic diagram of the computer device of the present invention.
Detailed Description
It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments of the present invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It is to be understood that the terms indicating an orientation or positional relationship are those conventionally used in the art of product placement or are those conventionally understood by those skilled in the art, and are used merely for convenience of description and simplicity of description, and do not indicate or imply that the device or element so referred to must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting.
In the following description, specific details are provided to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, systems may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
The embodiment is as follows:
the embodiment provides an abnormal person identification method for an intelligent park, which can be applied to a corresponding person monitoring system, wherein the system can comprise a background server, and a video monitoring terminal and a radio frequency identification terminal which are installed at each monitoring point of the park, as shown in fig. 1, the method comprises the following steps:
s101, acquiring a monitoring video stream of each monitoring point position of the park, and acquiring a plurality of frames of images and time information and point position number information corresponding to each frame of image from the monitoring video stream.
During specific implementation, video monitoring can be carried out on the corresponding monitoring point location range in real time through the video monitoring terminals of all monitoring point locations in the park, so that a monitoring video stream is generated to be transmitted to the background server, the monitoring video stream is composed of a plurality of frame images, and corresponding timestamps and unique numbers of the corresponding monitoring point locations are marked on all the frame images. After receiving the monitoring video stream, the background server extracts a plurality of frame images from the monitoring video stream, extracts corresponding time stamps and unique numbers from each frame image, generates time information according to the time stamps, and generates point position number information according to the unique numbers.
S102, extracting face image feature information and body appearance image feature information of the target person from each frame image.
In specific implementation, after the background server obtains the corresponding frame image, the background server may adopt an existing image feature extraction method to extract facial image feature information and physical image feature information of the target person from each frame image, and the optional image feature extraction methods include, but are not limited to, an HOG feature extraction method, an LBP feature extraction method, a Haar feature extraction method, a Dlib feature extraction method, a feature extraction method based on a convolutional neural network, and the like.
S103, judging whether the target person is a park student or not according to the facial image feature information and the body image feature information of the target person.
In specific implementation, after extracting the facial image feature information and the body appearance image feature information of the target person, the background server imports the corresponding facial image feature information and the body appearance image feature information into a preset student information base to perform feature matching with the facial image features and the body appearance image features of students in the campus, and if the facial image features and the body appearance image features are not successfully matched, the target person is indicated to be not a student in the campus, and further measures are required to be taken at the moment.
And S104, when the non-park students of the target person are judged, determining the traveling track of the target person according to the time information and the point number information corresponding to each frame of image of the target person.
In specific implementation, after the background server judges that the target person learns in the non-campus, the background server immediately calls time information and point location number information corresponding to each frame of image where the target person appears, marks monitoring point locations corresponding to the point location number information in a campus map according to the point location number information corresponding to each frame of image, and sequentially connects the marked monitoring point locations according to the time information corresponding to each frame of image to form a traveling track of the target person.
And S105, predicting the subsequent activity monitoring range of the target person according to the traveling track of the target person.
In specific implementation, after the traveling track of the target person is generated, the background server predicts a next activity monitoring range of the target person according to the traveling track, where the activity monitoring range may include a last monitoring point location of the traveling track, a monitoring point location after the last monitoring point location of the traveling track, and a monitoring point location before the last monitoring point location of the traveling track.
And S106, sending an identification instruction to the radio frequency identification terminals of each monitoring point position in the active monitoring range, and receiving identification data packets fed back by each radio frequency identification terminal.
During specific implementation, the background server sends an identification instruction to the radio frequency identification terminals installed at each monitoring point location within the activity monitoring range, and determines an information acquisition time period to be synchronously sent to the radio frequency identification terminals of each monitoring point location. The radio frequency identification terminal can be an RFID remote reading terminal, and when workers in a park wearing a corresponding RFID tag pass by the radio frequency identification terminal, the RFID remote reading terminal can read the identity identification information of the workers in the corresponding park, which is stored in the RFID tag. And after the radio frequency identification terminal corresponding to the monitoring point receives an identification instruction and an information acquisition time period sent by the background server, starting an identification state, reading the RFID tags of passing personnel in the information acquisition time period, generating an identification data packet containing the identification information and sending the identification data packet to the background server if the corresponding identification information is read in the information acquisition time period, and generating an empty identification data packet and sending the empty identification data packet to the background server if any identification information is not read. And the background server receives the identification data packet fed back by the radio frequency identification terminal of each monitoring point in the information acquisition time period.
And S107, if the identification data packets do not contain the identification information, judging that the target person is an abnormal person.
During specific implementation, the background server unpacks the identification data packet, and if the identification data packet fed back by the radio frequency identification terminal of each monitoring point does not contain any identity identification information, it indicates that the target person does not wear a radio frequency identification tag representing the identity of the staff in the park, and then judges that the target person is an abnormal person. If the identification data packet fed back by a certain radio frequency identification terminal contains identification information, pre-stored face image characteristic information corresponding to the identification information is called from a worker database according to the identification information, then the face image characteristic information of a target person is subjected to similarity matching with the pre-stored face image characteristic information called from the worker database, if the matching similarity is smaller than a set similarity threshold value, the target person is judged not to be a park worker corresponding to the identification information, and the target person is also judged to be an abnormal person. And after judging that the target person is an abnormal person, the background server immediately sends early warning prompt information to the duty terminal and sends each frame of image and the advancing track of the target person to the duty terminal, so that the duty person in the park can quickly find the target person according to each frame of image and the advancing track of the target person after sending the early warning prompt information through the duty terminal.
The method of this embodiment can realize the intelligent, noninductive monitoring discernment to the unusual personnel that appear in the garden, improves monitoring discernment efficiency, instantaneity and the precision to unusual personnel, promotes the safety precaution ability in wisdom garden.
Example 2:
the present embodiment provides an abnormal person identification system for an intelligent park, as shown in fig. 2, including an obtaining unit, an extracting unit, a determining unit, a predicting unit, a transceiving unit and a determining unit, wherein:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a monitoring video stream of each monitoring point location in a park and acquiring a plurality of frame images and time information and point location number information corresponding to each frame image from the monitoring video stream;
an extraction unit configured to extract facial image feature information and physical image feature information of a target person from each frame image;
the judging unit is used for judging whether the target person is a park student or not according to the facial image feature information and the body appearance image feature information of the target person;
the determining unit is used for determining the advancing track of the target person according to the time information and the point number information corresponding to each frame of image of the target person when the non-park student of the target person is judged;
the prediction unit is used for predicting the subsequent activity monitoring range of the target personnel according to the advancing track of the target personnel;
the receiving and sending unit is used for sending an identification instruction to the radio frequency identification terminals of each monitoring point position in the activity monitoring range and receiving an identification data packet fed back by each radio frequency identification terminal;
and the judging unit is used for judging that the target person is an abnormal person when the identification data packets do not contain the identification information.
Example 3:
the embodiment provides a computer device, as shown in fig. 3, which includes, at a hardware level:
the communication interface is used for establishing communication connection between the processor and an external terminal;
a memory to store instructions;
a processor, configured to read the instruction stored in the memory, and execute the intelligent campus abnormal person identification method according to the instruction in embodiment 1:
s101, acquiring a monitoring video stream of each monitoring point position of a park, and acquiring a plurality of frames of images, and time information and point position number information corresponding to each frame of image from the monitoring video stream;
s102, extracting facial image feature information and physical image feature information of a target person from each frame image;
s103, judging whether the target person is a park student or not according to the facial image feature information and the body image feature information of the target person;
s104, when the non-park students of the target person are judged, determining the advancing track of the target person according to the time information and the point number information corresponding to each frame of image of the target person;
s105, predicting the subsequent activity monitoring range of the target personnel according to the advancing track of the target personnel;
s106, sending an identification instruction to the radio frequency identification terminals of all monitoring point positions in the activity monitoring range, and receiving identification data packets fed back by all the radio frequency identification terminals;
and S107, if the identification data packets do not contain the identification information, judging that the target person is an abnormal person.
Optionally, the computer device further comprises an internal bus. The processor, the memory, and the communication interface may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
The Memory may include, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Flash Memory (Flash Memory), a First In First Out (FIFO), a First In Last Out (FILO), and/or the like. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
Example 4:
the present embodiment provides a computer-readable storage medium having stored thereon instructions that, when executed on a computer, cause the computer to execute a method of identifying an abnormal person in an intelligent campus of embodiment 1:
s101, acquiring a monitoring video stream of each monitoring point position of a park, and acquiring a plurality of frames of images, and time information and point position number information corresponding to each frame of image from the monitoring video stream;
s102, extracting facial image feature information and physical image feature information of a target person from each frame image;
s103, judging whether the target person is a park student or not according to the facial image feature information and the body image feature information of the target person;
s104, when the non-park students of the target person are judged, determining the advancing track of the target person according to the time information and the point number information corresponding to each frame of image of the target person;
s105, predicting the subsequent activity monitoring range of the target person according to the traveling track of the target person;
s106, sending an identification instruction to the radio frequency identification terminals of all monitoring point positions in the activity monitoring range, and receiving identification data packets fed back by all the radio frequency identification terminals;
and S107, if the identification data packets do not contain the identification information, judging that the target person is an abnormal person.
The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash Memory, a flash disk and/or a Memory Stick (Memory Stick), etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system.
The present embodiment also provides a computer program product containing instructions that, when executed on a computer, cause the computer to execute the intelligent park abnormal person identification method of embodiment 1. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system.
Finally, it should be noted that: the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An abnormal person identification method for an intelligent park is characterized by comprising the following steps:
acquiring a monitoring video stream of each monitoring point position in the park, and acquiring a plurality of frame images and time information and point position number information corresponding to each frame image from the monitoring video stream;
extracting facial image feature information and physical image feature information of a target person from each frame image;
judging whether the target person is a park student or not according to the facial image feature information and the body appearance image feature information of the target person;
when the non-park students of the target person are judged, determining the traveling track of the target person according to the time information and the point number information corresponding to each frame of image of the target person;
predicting the subsequent activity monitoring range of the target personnel according to the advancing track of the target personnel;
sending an identification instruction to the radio frequency identification terminals of all monitoring point positions in the activity monitoring range, and receiving identification data packets fed back by all the radio frequency identification terminals;
and if the identification data packets do not contain the identification information, judging that the target person is an abnormal person.
2. The intelligent park exception personnel identification method according to claim 1, wherein the method further comprises:
if a certain identification data packet contains identity identification information, calling corresponding pre-stored face image characteristic information from a worker database according to the identity identification information;
and performing similarity matching on the facial image feature information of the target person and the pre-stored facial image feature information, and if the matching similarity is smaller than a set similarity threshold, judging the target person to be an abnormal person.
3. The intelligent park exception personnel identification method according to claim 1 or 2, wherein the method further comprises:
and after the target person is judged to be an abnormal person, sending early warning prompt information to the duty terminal, and sending each frame of image and the advancing track of the target person to the duty terminal.
4. The method as claimed in claim 1, wherein the step of sending an identification command to the rfid terminals of each monitoring point in the active monitoring range and receiving an identification data packet fed back from each rfid terminal comprises:
determining an information acquisition time period;
sending an identification instruction and an information acquisition time period to a radio frequency identification terminal of each monitoring point position in the activity monitoring range;
and receiving the identification data packet fed back by each radio frequency identification terminal in the information acquisition time period.
5. The method as claimed in claim 1, wherein the surveillance video stream comprises a plurality of frames of images, each of the frames of images is attached with a time stamp and a unique number, and the acquiring the plurality of frames of images from the surveillance video stream and the time information and the point location number information corresponding to each of the frames of images comprises: extracting a plurality of frame images from the monitoring video stream, extracting corresponding time stamps and unique numbers from the frame images, generating time information according to the time stamps, and generating point position number information according to the unique numbers.
6. The intelligent park abnormal person identification method according to claim 1, wherein the step of determining the travel track of the target person according to the time information and the point number information corresponding to each frame of image in which the target person appears comprises:
marking monitoring point positions corresponding to the point position number information in the garden map according to the point position number information corresponding to each frame of image;
and sequentially connecting the marked corresponding monitoring point positions according to the time information corresponding to each frame of image according to the time sequence to form the traveling track of the target person.
7. The intelligent park abnormal person identification method according to claim 1, wherein the judging whether the target person is a park student according to the facial image feature information and the physical image feature information of the target person comprises: and (4) importing the facial image feature information and the body image feature information of the target person into a preset student information base for feature matching, and judging that the target person is a non-park student if the matching is not successful.
8. The utility model provides an unusual personnel identification system in wisdom garden, its characterized in that includes acquisition unit, extraction element, judgement unit, determining element, prediction unit, transceiver unit and decision unit, wherein:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a monitoring video stream of each monitoring point location in a park and acquiring a plurality of frame images and time information and point location number information corresponding to each frame image from the monitoring video stream;
an extraction unit configured to extract facial image feature information and physical image feature information of a target person from each frame image;
the judging unit is used for judging whether the target person is a park student or not according to the facial image feature information and the body image feature information of the target person;
the determining unit is used for determining the advancing track of the target person according to the time information and the point number information corresponding to each frame of image of the target person when the non-park student of the target person is judged;
the prediction unit is used for predicting the subsequent activity monitoring range of the target personnel according to the traveling track of the target personnel;
the receiving and sending unit is used for sending an identification instruction to the radio frequency identification terminals of each monitoring point position in the activity monitoring range and receiving an identification data packet fed back by each radio frequency identification terminal;
and the judging unit is used for judging that the target person is an abnormal person when the identification data packets do not contain the identification information.
9. A computer device, comprising:
a memory to store instructions;
a processor configured to read the instructions stored in the memory and execute the method of any one of claims 1-7 according to the instructions.
10. A computer-readable storage medium having stored thereon instructions which, when executed on a computer, cause the computer to perform the method of any one of claims 1-7.
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