CN114022841A - Personnel monitoring and identifying method and device, electronic equipment and readable storage medium - Google Patents
Personnel monitoring and identifying method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The invention relates to a data analysis technology, and discloses a personnel monitoring and identifying method, which comprises the following steps: framing the monitoring video to obtain video frames and time of each video frame; selecting a face region image and a human body region image in a video frame, and respectively obtaining a face image to be recognized and a human body image to be recognized; calculating the similarity between the face image of the person to be recognized and the face image to be recognized to obtain the face similarity; calculating the similarity between the physical sign image of the person to be identified and the human body image to be identified to obtain the human body similarity; weighting and calculating the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame; screening all video frames according to the recognition probability and a preset recognition threshold value to obtain a target video frame; and sending the target video frame and the corresponding time to a monitoring person. The invention also provides a personnel monitoring and identifying device, an electronic device and a readable storage medium. The invention can improve the accuracy of monitoring and identifying personnel.
Description
Technical Field
The present invention relates to data analysis technologies, and in particular, to a method and an apparatus for monitoring and identifying a person, an electronic device, and a readable storage medium.
Background
With the wider and wider use of video monitoring, the personnel monitoring and identifying method based on video monitoring is also widely applied.
However, the existing personnel monitoring and identifying method only relies on face information for identification, and when the face information is not completely captured by a video, the identifying effect is poor and the accuracy is low.
Disclosure of Invention
The invention provides a personnel monitoring and identifying method, a personnel monitoring and identifying device, electronic equipment and a computer readable storage medium, and mainly aims to improve the accuracy of personnel monitoring and identifying.
In order to achieve the above object, the present invention provides a method for monitoring and identifying a person, comprising:
acquiring a face image and a physical sign image of a person to be identified;
acquiring a monitoring video of a camera, and performing framing operation on the monitoring video to obtain a plurality of video frames and time corresponding to each video frame;
selecting a face region image and a human body region image of each person in the video frame to respectively obtain a face image to be recognized and a human body image to be recognized;
calculating the similarity between the face image and the face image to be recognized to obtain the face similarity;
calculating the similarity between the sign image and the human body image to be recognized to obtain the human body similarity;
performing weighted calculation according to the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame;
screening all the video frames according to the identification probability and a preset identification threshold value to obtain target video frames;
and sending the target video frame and the corresponding time to preset terminal equipment of a monitoring person.
Optionally, the calculating the similarity between the facial image and the facial image to be recognized to obtain the similarity of the facial image includes:
carrying out vector transformation on the face image to be recognized to obtain a face vector to be recognized;
carrying out vector conversion on the face image to obtain a target face vector;
and calculating the similarity between the face vector to be recognized and the target face vector to obtain the face similarity value.
Optionally, the vector conversion of the face image to be recognized to obtain a face vector to be recognized includes:
inputting the face image to be recognized into a pre-constructed deep learning network model;
acquiring all node output values of the last full-connection layer in the deep learning network model;
and longitudinally combining the output values of all the nodes according to the sequence of the corresponding nodes in the full connection layer to obtain the face vector to be recognized.
Optionally, the performing weighted calculation according to the human face similarity and the human body similarity to obtain the identification probability corresponding to each video frame includes:
calculating by using the face similarity and a preset first weight to obtain a face weight value;
calculating by using the human body similarity and a preset second weight to obtain a human body weight value;
calculating according to the human face weight value and the human body weight value to obtain a target weight value;
and selecting the maximum value of all the target weight values corresponding to each video frame to obtain the corresponding recognition probability.
Optionally, the performing weighted calculation according to the human face similarity and the human body similarity to obtain the identification probability corresponding to each video frame includes:
selecting the maximum value of the face similarity and the human body similarity to obtain a target similarity;
when the target similarity value is larger than a preset similarity threshold value, determining the target similarity as the target weight value;
optionally, the performing weighted calculation according to the human face similarity and the human body similarity to obtain the identification probability corresponding to each video frame includes:
and when the face similarity and the human body similarity are both larger than a preset similarity threshold value, determining the face similarity as the target weight value.
Optionally, the sending the target video frame and the corresponding time to a preset terminal device of a monitoring person includes:
and sending the target video frame with the latest time in all the target video frames and the corresponding time to preset terminal equipment.
In order to solve the above problem, the present invention further provides a person monitoring and identifying device, including:
the video framing module is used for acquiring a face image and a physical sign image of a person to be identified; acquiring a monitoring video of a camera, and performing framing operation on the monitoring video to obtain a plurality of video frames and time corresponding to each video frame;
the frame identification module is used for selecting a face area image and a human body area image of each person in the video frame to respectively obtain a face image to be identified and a human body image to be identified; calculating the similarity between the face image and the face image to be recognized to obtain the face similarity; calculating the similarity between the sign image and the human body image to be recognized to obtain the human body similarity; performing weighted calculation according to the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame;
the calculation screening module is used for screening all the video frames according to the identification probability and a preset identification threshold value to obtain target video frames; and sending the target video frame and the corresponding time to preset terminal equipment of a monitoring person.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
and the processor executes the computer program stored in the memory to realize the personnel monitoring and identifying method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the above-mentioned person monitoring and identifying method.
According to the embodiment of the invention, weighting calculation is carried out according to the human face similarity and the human body similarity, and the corresponding recognition probability of each video frame is obtained; screening all the video frames according to the identification probability and a preset identification threshold value to obtain target video frames; the two dimensions are identified by face identification and human body identification, and the identification accuracy is higher, so that the personnel monitoring and identifying method, the personnel monitoring and identifying device, the electronic equipment and the readable storage medium provided by the embodiment of the invention improve the personnel monitoring and identifying accuracy.
Drawings
Fig. 1 is a schematic flow chart of a method for monitoring and identifying a person according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a personnel monitoring and identification device according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a method for monitoring and identifying a person according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a personnel monitoring and identifying method. The execution subject of the personnel monitoring and identification method includes, but is not limited to, at least one of electronic devices such as a server, a terminal and the like that can be configured to execute the method provided by the embodiment of the present application. In other words, the personnel monitoring and identification method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: the cloud server can be an independent server, or can be a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of a personnel monitoring and identifying method according to an embodiment of the present invention is shown, in the embodiment of the present invention, the personnel monitoring and identifying method includes:
in an embodiment of the present invention, the method for monitoring and identifying a person includes:
s1, acquiring a face image and a sign image of a person to be recognized;
in detail, the person to be identified in the embodiment of the present invention is a person to be monitored, identified and searched, wherein the face image is an image of a face region of the person to be identified, and the sign image is an image of a human body region of the person to be identified.
S2, acquiring a monitoring video of the camera, and performing framing operation on the monitoring video to obtain a plurality of video frames and time corresponding to each video frame;
in detail, the surveillance video in the embodiment of the present invention is a surveillance video of a camera corresponding to an area that needs to be monitored and identified, for example: the person to be identified is a lost student after school, and the monitoring video can be a camera video at the door of the school.
S3, selecting a face area image and a human body area image of each person in the video frame to respectively obtain a face image to be recognized and a human body image to be recognized;
in detail, in the embodiment of the present invention, a preset image segmentation model may be used to select a face region image and a corresponding body region image of each person in the video frame.
Optionally, in the embodiment of the present invention, the image segmentation model is a full convolution neural network model.
S4, calculating the similarity between the face image and the face image to be recognized to obtain the face similarity;
in detail, in the embodiment of the present invention, calculating a similarity between the face image and the face image to be recognized to obtain a face similarity, includes:
step A: carrying out vector transformation on the face image to be recognized to obtain a face vector to be recognized;
optionally, in the embodiment of the present invention, vector conversion is performed on the facial image to be recognized to obtain the facial image to be recognized, where the vector conversion includes:
inputting the face image to be recognized into a pre-constructed deep learning network model;
and acquiring all node output values of the last full-connection layer in the deep learning network model, and constructing the face vector to be recognized according to the acquired all node output values.
Optionally, in the embodiment of the present invention, the deep learning network model is an artificial intelligent model, and the deep learning network model is a CNN (Convolutional Neural network), and in the embodiment of the present invention, the deep learning model may include multiple fully-connected layers, and only an output value of the last fully-connected layer is a final image feature value, so that output values of all nodes of the last fully-connected layer in the deep learning network model are obtained to perform vector construction on the face vector to be recognized.
In detail, the obtaining of the output values of all nodes of the last layer of the fully-connected layer in the deep learning network model in the embodiment of the present invention to construct the face vector to be recognized includes:
and longitudinally combining the output values of all the nodes according to the sequence of the corresponding nodes in the full connection layer to obtain the face vector to be recognized.
For example: the total connection layer is provided with 3 nodes which are respectively a first node, a second node and a third node in sequence, the output value of the first node is 1, the output value of the second node is 3, the output value of the third node is 5, and then the corresponding face vector to be recognized is
And B: carrying out vector conversion on the face image to obtain a target face vector;
in the embodiment of the present invention, the vector transformation method in this step is similar to that in step 1, and is not described herein again.
And C: and calculating the similarity between the face vector to be recognized and the target face vector to obtain the face similarity value.
Optionally, in the embodiment of the present invention, similarity calculation may be performed by using a similarity algorithm such as cosine similarity and pearson correlation coefficient.
S5, calculating the similarity between the sign image and the human body image to be recognized to obtain the human body similarity;
in detail, in the embodiment of the present invention, calculating a similarity between the sign image and the human body image to be recognized to obtain a human body similarity, includes:
carrying out vector transformation on the human body image to be identified to obtain a human body vector to be identified;
performing vector conversion on the sign image to obtain a target human body vector;
and calculating the similarity between the human body vector to be identified and the human body vector to obtain the human body similarity value.
S6, carrying out weighted calculation according to the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame;
in detail, in the embodiment of the present invention, performing weighted calculation according to the human face similarity and the human body similarity to obtain the identification probability corresponding to each video frame, includes:
calculating by using the face similarity and a preset first weight to obtain a face weight value;
for example: the face similarity is 0.8, the first weight is 0.8, and the corresponding face weight value is 0.8 × 0.8 — 0.64.
Calculating by using the human body similarity and a preset second weight to obtain a human body weight value;
for example: the human similarity is 0.7, and the second weight is 0.2, so that the corresponding human weight value is 0.7 × 0.2 — 0.14.
Calculating according to the human face weight value and the human body weight value to obtain a target weight value;
for example: the face weight value corresponding to the video frame is 0.64, the human body weight value corresponding to the video frame is 0.14, and then the target weight value is 0.64+ 0.14-0.78.
And selecting the maximum value of all the target weight values corresponding to each video frame to obtain the corresponding recognition probability.
In the embodiment of the present invention, the first weight value and the second weight value are preset weight parameters, and a sum of the first weight value and the second weight value is 1.
In another embodiment of the present invention, the performing a weighted calculation according to the human face similarity and the human body similarity to obtain an identification probability corresponding to each video frame includes:
selecting the maximum value of the face similarity and the human body similarity to obtain a target similarity;
when the target similarity value is larger than a preset similarity threshold value, determining the target similarity as the target weight value;
in another embodiment of the present invention, the performing a weighted calculation according to the human face similarity and the human body similarity to obtain an identification probability corresponding to each video frame includes:
and when the face similarity and the human body similarity are both larger than a preset similarity threshold value, determining the face similarity as the target weight value.
For example: the face similarity corresponding to a person in the video frame is 0.94, the corresponding human body similarity is 0.93, the similarity threshold is 0.92, and then the target weight value corresponding to the person is 0.94.
In another embodiment of the present invention, the performing a weighted calculation according to the human face similarity and the human body similarity to obtain an identification probability corresponding to each video frame includes:
and when the face similarity and the human body similarity corresponding to each video frame are both greater than a preset similarity threshold, determining the human body similarity as the target weight value.
For example: the face similarity corresponding to a person in the video frame is 0.94, the corresponding human body similarity is 0.93, the similarity threshold is 0.92, and then the target weight value corresponding to the person is 0.93.
S7, screening all the video frames according to the recognition probability and a preset recognition threshold value to obtain a target video frame;
in detail, in the embodiment of the present invention, the video frame with the recognition probability greater than or equal to the recognition threshold is selected to obtain the target video frame.
And S8, sending the target video frame and the corresponding time to preset terminal equipment of a monitoring person.
In detail, in the embodiment of the present invention, the time corresponding to the target video frame is sent to a preset terminal device of a monitoring person.
And sending the target video frame with the latest time in all the target video frames and the corresponding time to preset terminal equipment.
Optionally, in the embodiment of the present invention, the terminal device is an intelligent terminal capable of receiving and displaying information, which includes but is not limited to: mobile phones, computers, tablets, etc.
In another embodiment of the invention, in order to better facilitate the monitoring personnel to better monitor and identify the personnel, the position of the camera is obtained, and the position of the camera, the latest time and the corresponding target video frame are sent to the terminal equipment.
Fig. 2 is a functional block diagram of the person monitoring and identifying device of the present invention.
The person monitoring and identifying device 100 of the present invention may be installed in an electronic device. According to the implemented functions, the personnel monitoring and identifying device may include a video framing module 101, a framing identification module 102, and a calculation screening module 103, which may also be referred to as a unit, and refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform fixed functions, and are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the video framing module 101 is used for acquiring a face image and a sign image of a person to be identified; acquiring a monitoring video of a camera, and performing framing operation on the monitoring video to obtain a plurality of video frames and time corresponding to each video frame;
the framing identification module 102 is configured to select a face region image and a body region image of each person in the video frame, and obtain a face image to be identified and a body image to be identified respectively; calculating the similarity between the face image and the face image to be recognized to obtain the face similarity; calculating the similarity between the sign image and the human body image to be recognized to obtain the human body similarity; performing weighted calculation according to the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame;
the calculation screening module 103 is configured to screen all the video frames according to the identification probability and a preset identification threshold to obtain a target video frame; and sending the target video frame and the corresponding time to preset terminal equipment of a monitoring person.
In detail, in the embodiment of the present invention, when the modules in the personnel monitoring and identifying device 100 are used, the same technical means as the personnel monitoring and identifying method described in fig. 1 are used, and the same technical effects can be produced, which is not described herein again.
Fig. 2 is a schematic structural diagram of an electronic device for implementing the method for monitoring and identifying a person according to the present invention.
The electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further include a computer program, such as a security pre-warning program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various types of data, such as codes of a security precaution program, but also temporarily store data that has been output or will be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., a person monitoring and identifying program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The communication bus 12 may be a PerIPheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
Fig. 2 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power source may also include any component of one or more dc or ac power sources, recharging devices, power failure classification circuits, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Optionally, the communication interface 13 may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further include a user interface, which may be a Display (Display), an input unit (such as a Keyboard (Keyboard)), and optionally, a standard wired interface, or a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The person monitoring and identification program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, which when executed in the processor 10, can implement:
acquiring a face image and a physical sign image of a person to be identified;
acquiring a monitoring video of a camera, and performing framing operation on the monitoring video to obtain a plurality of video frames and time corresponding to each video frame;
selecting a face region image and a human body region image of each person in the video frame to respectively obtain a face image to be recognized and a human body image to be recognized;
calculating the similarity between the face image and the face image to be recognized to obtain the face similarity;
calculating the similarity between the sign image and the human body image to be recognized to obtain the human body similarity;
performing weighted calculation according to the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame;
screening all the video frames according to the identification probability and a preset identification threshold value to obtain target video frames;
and sending the target video frame and the corresponding time to preset terminal equipment of a monitoring person.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program may implement:
acquiring a face image and a physical sign image of a person to be identified;
acquiring a monitoring video of a camera, and performing framing operation on the monitoring video to obtain a plurality of video frames and time corresponding to each video frame;
selecting a face region image and a human body region image of each person in the video frame to respectively obtain a face image to be recognized and a human body image to be recognized;
calculating the similarity between the face image and the face image to be recognized to obtain the face similarity;
calculating the similarity between the sign image and the human body image to be recognized to obtain the human body similarity;
performing weighted calculation according to the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame;
screening all the video frames according to the identification probability and a preset identification threshold value to obtain target video frames;
and sending the target video frame and the corresponding time to preset terminal equipment of a monitoring person.
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A person monitoring and identification method, the method comprising:
acquiring a face image and a physical sign image of a person to be identified;
acquiring a monitoring video of a camera, and performing framing operation on the monitoring video to obtain a plurality of video frames and time corresponding to each video frame;
selecting a face region image and a human body region image of each person in the video frame to respectively obtain a face image to be recognized and a human body image to be recognized;
calculating the similarity between the face image and the face image to be recognized to obtain the face similarity;
calculating the similarity between the sign image and the human body image to be recognized to obtain the human body similarity;
performing weighted calculation according to the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame;
screening all the video frames according to the identification probability and a preset identification threshold value to obtain target video frames;
and sending the target video frame and the corresponding time to preset terminal equipment of a monitoring person.
2. The people monitoring and recognizing method according to claim 1, wherein the calculating the similarity between the facial image and the facial image to be recognized to obtain the similarity of the facial image comprises:
carrying out vector transformation on the face image to be recognized to obtain a face vector to be recognized;
carrying out vector conversion on the face image to obtain a target face vector;
and calculating the similarity between the face vector to be recognized and the target face vector to obtain the face similarity value.
3. The personnel monitoring and recognizing method as claimed in claim 2, wherein said vector conversion of the face image to be recognized to obtain the face vector to be recognized comprises:
inputting the face image to be recognized into a pre-constructed deep learning network model;
acquiring all node output values of the last full-connection layer in the deep learning network model;
and longitudinally combining the output values of all the nodes according to the sequence of the corresponding nodes in the full connection layer to obtain the face vector to be recognized.
4. The people monitoring and identifying method according to claim 1, wherein the obtaining of the identification probability corresponding to each video frame by performing the weighted calculation according to the human face similarity and the human body similarity comprises:
calculating by using the face similarity and a preset first weight to obtain a face weight value;
calculating by using the human body similarity and a preset second weight to obtain a human body weight value;
calculating according to the human face weight value and the human body weight value to obtain a target weight value;
and selecting the maximum value of all the target weight values corresponding to each video frame to obtain the corresponding recognition probability.
5. The people monitoring and identifying method according to claim 4, wherein the obtaining of the identification probability corresponding to each video frame by performing the weighted calculation according to the human face similarity and the human body similarity comprises:
selecting the maximum value of the face similarity and the human body similarity to obtain a target similarity;
and when the target similarity value is larger than a preset similarity threshold value, determining the target similarity as the target weight value.
6. The people monitoring and identifying method according to claim 4, wherein the obtaining of the identification probability corresponding to each video frame by performing the weighted calculation according to the human face similarity and the human body similarity comprises:
and when the face similarity and the human body similarity are both larger than a preset similarity threshold value, determining the face similarity as the target weight value.
7. The personnel monitoring and identification method as claimed in any one of claims 1 to 6, wherein said sending said target video frame and said corresponding time to a preset terminal device of a monitoring personnel comprises:
and sending the target video frame with the latest time in all the target video frames and the corresponding time to preset terminal equipment.
8. A person monitoring and identification device, comprising:
the video framing module is used for acquiring a face image and a physical sign image of a person to be identified; acquiring a monitoring video of a camera, and performing framing operation on the monitoring video to obtain a plurality of video frames and time corresponding to each video frame;
the frame identification module is used for selecting a face area image and a human body area image of each person in the video frame to respectively obtain a face image to be identified and a human body image to be identified; calculating the similarity between the face image and the face image to be recognized to obtain the face similarity; calculating the similarity between the sign image and the human body image to be recognized to obtain the human body similarity; performing weighted calculation according to the human face similarity and the human body similarity to obtain the corresponding recognition probability of each video frame;
the calculation screening module is used for screening all the video frames according to the identification probability and a preset identification threshold value to obtain target video frames; and sending the target video frame and the corresponding time to preset terminal equipment of a monitoring person.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the person monitoring identification method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a person monitoring and identification method according to any one of claims 1 to 7.
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| CN202111234186.3A CN114022841A (en) | 2021-10-22 | 2021-10-22 | Personnel monitoring and identifying method and device, electronic equipment and readable storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115171028A (en) * | 2022-09-06 | 2022-10-11 | 深圳市天趣星空科技有限公司 | Intelligent glasses adjustment control method and system based on image processing |
| CN116030417A (en) * | 2023-02-13 | 2023-04-28 | 四川弘和通讯集团有限公司 | Employee identification method, device, equipment, medium and product |
-
2021
- 2021-10-22 CN CN202111234186.3A patent/CN114022841A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115171028A (en) * | 2022-09-06 | 2022-10-11 | 深圳市天趣星空科技有限公司 | Intelligent glasses adjustment control method and system based on image processing |
| CN115171028B (en) * | 2022-09-06 | 2022-11-18 | 深圳市天趣星空科技有限公司 | Intelligent glasses adjustment control method and system based on image processing |
| CN116030417A (en) * | 2023-02-13 | 2023-04-28 | 四川弘和通讯集团有限公司 | Employee identification method, device, equipment, medium and product |
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