Suspicious person identification method, device, equipment and storage medium

Abstract

The present application is applicable to the field of personnel identification technology, and provides a suspicious person identification method, device, equipment and storage medium. The method includes: obtaining an image to be identified collected by a security device, a member list of a local area network where the security device is located, and a historical activity image of a family member group collected by the security device; based on the historical activity image, obtaining a family member group feature set; based on the family member group feature set and the image to be identified, obtaining a first judgment result; if the first judgment result is that the person in the image to be identified is a suspicious person, then obtaining a second judgment result based on the member list and the image to be identified; based on the second judgment result, determining whether the person in the image to be identified is a suspicious person. The present application can ensure that suspicious persons are identified efficiently and accurately without obtaining user privacy data.

Description

Technical Field

[0001] The present application belongs to the technical field of personnel identification, and in particular, relates to a method, device, equipment and storage medium for identifying suspicious persons. Background Art

[0002] Most of the existing home security devices use facial recognition to deal with intrusions by suspicious persons. Although it can effectively identify suspicious persons, the use of facial recognition means obtaining the facial data of all family members in the group. If it is not stored properly, it will cause data leakage. At this time, the facial data will be leaked at the same time, causing damage to the user.

[0003] In addition, due to the need to use facial recognition, the security equipment must be able to clearly capture the facial images of suspicious persons, which imposes certain restrictions on the installation location of the equipment. At the same time, the clarity of the facial images of suspicious persons is also required, which makes the existing home security equipment very restrictive. Summary of the invention

[0004] The embodiments of the present application provide a suspicious person identification method, device, equipment and storage medium, which can efficiently and accurately identify suspicious persons while ensuring that user privacy data is not obtained.

[0005] This application is implemented through the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for identifying a suspicious person, comprising:

[0007] Obtain the image to be identified collected by the security device, the member list of the local area network where the security device is located, and the historical activity images of the family member group collected by the security device.

[0008] Based on historical activity images, a feature set of family member groups is obtained.

[0009] A first judgment result is obtained based on the feature set of the family member group and the image to be identified; if the first judgment result is that the person in the image to be identified is a suspicious person, a second judgment result is obtained based on the member list and the image to be identified.

[0010] Based on the second judgment result, it is determined whether the person in the image to be identified is a suspicious person.

[0011] In conjunction with the first aspect, in some possible implementations, a family member group feature set is obtained based on historical activity images, including:

[0012] For each member of the family member group, the height data and body shape data of the member are calculated based on the historical activity images, and the height data and body shape data of the member are used as the feature set of the member.

[0013] Summarize the height and body shape data of all members to obtain the feature set of the family member group.

[0014] In conjunction with the first aspect, in some possible implementations, obtaining a first judgment result based on the family member group feature set and the image to be recognized includes:

[0015] Based on the image to be identified, the height data and body shape data of the person in the image to be identified are calculated.

[0016] The feature set of each member in the feature set of the family member group is taken as a cluster center.

[0017] Calculate multiple distances from the height data and body shape data of the person in the image to be identified to each cluster center.

[0018] If the minimum value of the multiple distances is greater than or equal to the preset threshold, the first judgment result is that the person in the image to be identified is a suspicious person.

[0019] If the minimum value of the multiple distances is less than the preset threshold, the first judgment result is that the person in the image to be identified is not a suspicious person.

[0020] In combination with the first aspect, in some possible implementations, calculating multiple distances from the height data and body shape data of a person in the image to be identified to each cluster center includes:

[0021] Combined with the first formula, multiple distances from the height data and body shape data of the person in the image to be identified to each cluster center are calculated.

[0022] The first formula is:

[0023] d mi =h m -h i +c m -c i

[0024] Among them, d mi represents the distance from person m to cluster center i in the image to be identified, h i represents the height data of cluster center i, c i represents the body shape data of cluster center i, h m represents the height data of person m in the image to be identified, c m Represents the body shape data of person m in the image to be identified.

[0025] In combination with the first aspect, in some possible implementations, the body shape data of the person in the image to be identified is the ratio of the maximum value of the pixel block occupied by the person in the horizontal direction of the image to be identified to the maximum value of the pixel block occupied by the person in the vertical direction of the image to be identified.

[0026] The body shape data of a member in the family member group is the ratio of the maximum value of the pixel block occupied by the member in the horizontal direction in the historical moving image to the maximum value of the pixel block occupied by the member in the vertical direction in the historical moving image.

[0027] In combination with the first aspect, in some possible implementations, obtaining a second judgment result according to the member list and the image to be recognized includes:

[0028] According to the member list, get the number of online members in the family member group.

[0029] If the number of online members in the family member group is less than the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is a suspicious person.

[0030] If the number of online members in the family member group is greater than or equal to the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is not a suspicious person.

[0031] In conjunction with the first aspect, in some possible implementations, the suspicious person identification method further includes:

[0032] The information of the identified suspicious persons will be pushed to the administrator of the family member group through the security APP to alert the administrator of the suspicious persons in the home.

[0033] In a second aspect, an embodiment of the present application provides a suspicious person identification device, including:

[0034] The data acquisition module is used to obtain the image to be identified collected by the security equipment, the member list of the local area network where the security equipment is located, and the historical activity images of the family member group collected by the security equipment.

[0035] The feature recognition module is used to obtain a set of family member group features based on historical activity images.

[0036] The personnel judgment module is used to obtain a first judgment result based on a feature set of a family member group and an image to be identified; if the first judgment result is that the person in the image to be identified is a suspicious person, a second judgment result is obtained based on the member list and the image to be identified.

[0037] The result output module is used to determine whether the person in the image to be identified is a suspicious person based on the second judgment result.

[0038] In a third aspect, an embodiment of the present application provides a terminal device, including: a processor and a memory, the memory is used to store a computer program, and when the processor executes the computer program, the suspicious person identification method as described in any one of the first aspects is implemented.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the suspicious person identification method as described in any one of the first aspects is implemented.

[0040] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0041] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0042] The present application identifies the person in the image to be identified in two dimensions, namely, the family member group feature set and the member list of the local area network where the security device is located, to determine whether the person is a suspicious person. The family member group feature set does not include the privacy data of the family members, but only includes the height data and body shape data of the members, so that the suspected suspicious persons can be easily distinguished. The suspicious persons can be accurately determined among the suspected suspicious persons through the member list, and the suspicious persons can be efficiently and accurately identified without obtaining the user's privacy data (face recognition data). At the same time, because there is no need to obtain the user's privacy data (face recognition data), the location of the security equipment is more flexible, the clarity requirements of the security equipment are lower, and the cost is relatively smaller than that of high-definition security equipment.

[0043] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 It is a flowchart of a suspicious person identification method provided by an embodiment of the present application;

[0046] Figure 2 It is a structural schematic diagram of a suspicious person identification device provided by an embodiment of the present application;

[0047] Figure 3 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0048] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0049] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0050] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0051] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0052] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0053] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0054] The present application embodiment provides a method for identifying suspicious persons. Figure 1 is a schematic diagram of the process of a suspicious person identification method provided by an embodiment of the present application, referring to Figure 1 , the details of the suspicious person identification method are as follows:

[0055] Step 101, obtaining the image to be identified collected by the security device, the member list of the local area network where the security device is located, and the historical activity images of the family member group collected by the security device.

[0056] In some specific embodiments, a member in the member list of the local area network where the security device is located may have many devices or some household appliances. In this solution, household appliances are ignored, that is, they do not exist in the member list. The mobile phone device in a member is used as the main identification device, that is, when the mobile phone device of member A in this local area network is not connected to the local area network, it can be considered that member A is not in the member list. The historical active image is an image or video of a non-suspicious person in the historical image to be identified.

[0057] Step 102, obtaining a set of family member group characteristics based on historical activity images.

[0058] Exemplarily, step 102 may include:

[0059] For each member of the family member group, the height data and body shape data of the member are calculated based on the historical activity images, and the height data and body shape data of the member are used as the feature set of the member.

[0060] Summarize the height and body shape data of all members to obtain the feature set of the family member group.

[0061] In some specific embodiments, in addition to considering privacy data such as face recognition, a preliminary judgment can also be made based on the height and body shape of the person. These features can simply and efficiently identify suspicious persons.

[0062] Step 103, obtaining a first judgment result based on the family member group feature set and the image to be identified; if the first judgment result is that the person in the image to be identified is a suspicious person, obtaining a second judgment result based on the member list and the image to be identified.

[0063] Exemplarily, obtaining a first judgment result based on the family member group feature set and the image to be identified may include:

[0064] Based on the image to be identified, the height data and body shape data of the person in the image to be identified are calculated.

[0065] The feature set of each member in the feature set of the family member group is taken as a cluster center.

[0066] Calculate multiple distances from the height data and body shape data of the person in the image to be identified to each cluster center.

[0067] If the minimum value of the multiple distances is greater than or equal to the preset threshold, the first judgment result is that the person in the image to be identified is a suspicious person.

[0068] If the minimum value of the multiple distances is less than the preset threshold, the first judgment result is that the person in the image to be identified is not a suspicious person.

[0069] In some specific embodiments, in order to preliminarily detect whether a person is a suspicious person, a preliminary judgment can be made based on a person's height and body shape data, which can very efficiently determine whether the person in the image to be identified is a member of the family group.

[0070] Exemplarily, calculating multiple distances from the height data and body shape data of a person in the image to be identified to each cluster center may include:

[0071] Combined with the first formula, multiple distances from the height data and body shape data of the person in the image to be identified to each cluster center are calculated.

[0072] The first formula can be:

[0073] d mi =h m -h i +c m -c i

[0074] Among them, d mi represents the distance from person m to cluster center i in the image to be identified, h i represents the height data of cluster center i, c i represents the body shape data of cluster center i, h m represents the height data of person m in the image to be identified, c m Represents the body shape data of person m in the image to be identified.

[0075] In some specific embodiments, the influence of height data and body shape data are considered at the same time. When the difference between the two data is small, the person can be considered as a member of the family group. When there is an obvious error in one data, the person will be listed as a suspicious person. Such a design can improve the accuracy of personnel identification and accurately distinguish suspicious persons.

[0076] Exemplarily, the body shape data of the person in the image to be identified is the ratio of the maximum value of the pixel block occupied by the person in the horizontal direction of the image to be identified to the maximum value of the pixel block occupied by the person in the vertical direction of the image to be identified.

[0077] The body shape data of a member in the family member group is the ratio of the maximum value of the pixel block occupied by the member in the horizontal direction in the historical moving image to the maximum value of the pixel block occupied by the member in the vertical direction in the historical moving image.

[0078] In some specific embodiments, in order to improve the accuracy of scheme recognition and the adaptability of the scheme, the body shape data of the members in the family member group and the height data of the members in the family member group can be adaptively adjusted by the mean method. For example, the height data of member A entered for the first time is 0.72, and the height data in the image to be identified is 0.73. When the person in the image to be identified is determined to be A, then the height data of member A at this time is 0.725 (the average of the sum of 0.72 and 0.73). In short, after the person in the image to be identified is identified as a non-suspicious person, the data in the image to be identified will be used as historical data, and the parameters related to identification will be adaptively adjusted. This can improve the practicality of the method and reduce the need for users to frequently correct the parameters related to identification due to changes in height or body shape.

[0079] Exemplarily, obtaining the second judgment result according to the member list and the image to be recognized may include:

[0080] According to the member list, get the number of online members in the family member group.

[0081] If the number of online members in the family member group is less than the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is a suspicious person.

[0082] If the number of online members in the family member group is greater than or equal to the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is not a suspicious person.

[0083] In some specific embodiments, in order to avoid misidentification due to family members growing taller or gaining weight, the persons in the image to be identified are re-identified based on the data of online members in the family member group, thereby reducing the possibility of errors in the method and making the identification result more accurate.

[0084] Step 104: Based on the second judgment result, determine whether the person in the image to be identified is a suspicious person.

[0085] An exemplary method for identifying a suspicious person further includes:

[0086] The information of the identified suspicious persons will be pushed to the administrator of the family member group through the security APP to alert the administrator of the suspicious persons in the home.

[0087] In some specific embodiments, in order to further ensure the safety of family property, after identifying a suspicious person, the administrator of the family member group can view the image to be identified by the security device in real time. The administrator can choose to call the police or add a temporary label (suspicious person whitelist) to the person in the image to be identified according to the actual situation. The whitelist is cleared for one week. After adding to the whitelist, the person will no longer be included in the suspicious person.

[0088] In some specific embodiments, since the security equipment does not need to perform face recognition, the clarity of the image acquisition device (camera) of the security equipment does not need to be very high, and the installation location of the security equipment is also more flexible than that of traditional security equipment that performs face recognition.

[0089] The above-mentioned suspicious person identification method identifies the person in the image to be identified in two dimensions according to the family member group feature set and the member list of the local area network where the security equipment is located, and determines whether the person is a suspicious person. The family member group feature set does not include the privacy data of the family members, but only includes the height data and body shape data of the members, which can simply distinguish the suspected suspicious persons. The suspicious persons can be accurately determined among the suspected suspicious persons through the member list, and the suspicious persons can be identified efficiently and accurately without obtaining the user's privacy data (face recognition data). At the same time, because there is no need to obtain the user's privacy data (face recognition data), the location of the security equipment is more flexible, the clarity requirements of the security equipment are lower, and the cost is relatively smaller than that of high-definition security equipment.

[0090] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0091] Corresponding to the suspicious person identification method described in the above embodiment, Figure 2 A structural block diagram of a suspicious person identification device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0092] See also Figure 2 The suspicious person identification device in the embodiment of the present application may include:

[0093] The data acquisition module 201 is used to acquire the image to be identified collected by the security device, the member list of the local area network where the security device is located, and the historical activity images of the family member group collected by the security device.

[0094] The feature recognition module 202 is used to obtain a set of family member group features based on historical activity images.

[0095] The personnel judgment module 203 is used to obtain a first judgment result based on the family member group feature set and the image to be identified; if the first judgment result is that the person in the image to be identified is a suspicious person, a second judgment result is obtained based on the member list and the image to be identified.

[0096] The result output module 204 is used to determine whether the person in the image to be identified is a suspicious person based on the second judgment result.

[0097] Exemplarily, the feature recognition module 202 may also be used for:

[0098] For each member of the family member group, the height data and body shape data of the member are calculated based on the historical activity images, and the height data and body shape data of the member are used as the feature set of the member.

[0099] Summarize the height and body shape data of all members to obtain the feature set of the family member group.

[0100] Exemplarily, the personnel determination module 203 may also be used for:

[0101] Based on the image to be identified, the height data and body shape data of the person in the image to be identified are calculated.

[0102] The feature set of each member in the feature set of the family member group is taken as a cluster center.

[0103] Calculate multiple distances from the height data and body shape data of the person in the image to be identified to each cluster center.

[0104] If the minimum value of the multiple distances is greater than or equal to the preset threshold, the first judgment result is that the person in the image to be identified is a suspicious person.

[0105] If the minimum value of the multiple distances is less than the preset threshold, the first judgment result is that the person in the image to be identified is not a suspicious person.

[0106] Exemplarily, the personnel determination module 203 may also be used for:

[0107] Combined with the first formula, multiple distances from the height data and body shape data of the person in the image to be identified to each cluster center are calculated.

[0108] The first formula is:

[0109] d mi =h m -h i +c m -c i

[0110] Among them, d mirepresents the distance from person m to cluster center i in the image to be identified, h i represents the height data of cluster center i, c i represents the body shape data of cluster center i, h m represents the height data of person m in the image to be identified, c m Represents the body shape data of person m in the image to be identified.

[0111] Exemplarily, the body shape data of the person in the image to be identified is the ratio of the maximum value of the pixel block occupied by the person in the horizontal direction of the image to be identified to the maximum value of the pixel block occupied by the person in the vertical direction of the image to be identified.

[0112] The body shape data of a member in the family member group is the ratio of the maximum value of the pixel block occupied by the member in the horizontal direction in the historical moving image to the maximum value of the pixel block occupied by the member in the vertical direction in the historical moving image.

[0113] Exemplarily, the personnel determination module 203 may also be used for:

[0114] According to the member list, get the number of online members in the family member group.

[0115] If the number of online members in the family member group is less than the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is a suspicious person.

[0116] If the number of online members in the family member group is greater than or equal to the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is not a suspicious person.

[0117] Exemplarily, the result output module 204 may also be used to:

[0118] The information of the identified suspicious persons will be pushed to the administrator of the family member group through the security APP to alert the administrator of the suspicious persons in the home.

[0119] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0120] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0121] The present application also provides a terminal device, see Figure 3 The terminal device 300 may include: at least one processor 310 and a memory 320, wherein the memory 320 is used to store a computer program 321, and the processor 310 is used to call and run the computer program 321 stored in the memory 320 to implement the steps in any of the above-mentioned method embodiments, for example Figure 1 Steps 101 to 104 in the illustrated embodiment. Alternatively, when the processor 310 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 2 The functions of each module are shown.

[0122] Exemplarily, the computer program 321 may be divided into one or more modules / units, one or more modules / units are stored in the memory 320, and are executed by the processor 310 to complete the present application. The one or more modules / units may be a series of computer program segments that can complete specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 300.

[0123] Those skilled in the art will understand that Figure 3 It is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0124] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0125] The memory 320 may be an internal storage unit of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

[0126] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0127] The suspicious person identification method provided in the embodiment of the present application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, laptops, netbooks, etc. The embodiment of the present application does not impose any restrictions on the specific type of terminal devices.

[0128] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in each embodiment of the above-mentioned suspicious person identification method can be implemented.

[0129] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned suspicious person identification method when executing the computer program product.

[0130] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0131] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0133] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A suspicious person identification method, characterized in that: include: Obtain the image to be identified collected by the security equipment, the member list of the local area network where the security equipment is located, and the historical activity images of the family member group collected by the security equipment; Based on the historical activity images, a family member group feature set is obtained; Based on the family member group feature set and the image to be identified, a first judgment result is obtained; if the first judgment result is that the person in the image to be identified is a suspicious person, a second judgment result is obtained based on the member list and the image to be identified; Based on the second judgment result, determining whether the person in the image to be identified is a suspicious person; The obtaining of a second judgment result according to the member list and the image to be recognized includes: According to the member list, obtaining the number of online members in the family member group; If the number of online members in the family member group is less than the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is a suspicious person; If the number of online members in the family member group is greater than or equal to the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is not a suspicious person.

2. The suspicious person identification method according to claim 1, characterized in that: The obtaining of a family member group feature set based on the historical activity images includes: For each member of the family group, the height data and body shape data of the member are calculated based on the historical activity images of the member, and the height data and body shape data of the member are used as the feature set of the member; The height data and body shape data of all members are summarized to obtain the feature set of the family member group.

3. The suspicious person identification method according to claim 2, characterized in that: The obtaining of a first judgment result based on the family member group feature set and the image to be identified includes: Based on the image to be identified, calculate the height data and body shape data of the person in the image to be identified; Taking the feature set of each member in the feature set of the family member group as a cluster center; Calculating multiple distances from the height data and body shape data of the person in the image to be identified to each cluster center; If the minimum value among the multiple distances is greater than or equal to the preset threshold, the first judgment result is that the person in the image to be identified is a suspicious person; If the minimum value of the multiple distances is less than the preset threshold, the first judgment result is that the person in the image to be identified is not a suspicious person.

4. The suspicious person identification method according to claim 3, characterized in that: The calculating of multiple distances from the person in the to-be-identified image to each cluster center includes: Combined with the first formula, multiple distances from the height data and body shape data of the person in the image to be identified to each cluster center are calculated; The first formula is: d mi = | h m -h i | + | c m -c i Among them, d mi represents the distance from person m to cluster center i in the image to be identified, h i represents the height data of cluster center i, c i represents the body shape data of cluster center i, h m represents the height data of person m in the image to be identified, c m Represents the body shape data of person m in the image to be identified.

5. The suspicious person identification method according to claim 4, characterized in that: The body shape data of the person in the image to be identified is the ratio of the maximum value of the pixel block occupied by the person in the horizontal direction of the image to be identified to the maximum value of the pixel block occupied by the person in the vertical direction of the image to be identified; The body shape data of a member in the family member group is the ratio of the maximum value of the pixel blocks occupied by the member in the horizontal direction in the historical moving image to the maximum value of the pixel blocks occupied by the member in the vertical direction in the historical moving image.

6. The suspicious person identification method according to claim 1, characterized in that: The suspicious person identification method further includes: The information of the identified suspicious persons will be pushed to the administrator of the family member group through the security APP to alert the administrator of the suspicious persons in the home.

7. A suspicious person identification device, characterized in that: include: A data acquisition module, used to acquire the image to be identified collected by the security equipment, the member list of the local area network where the security equipment is located, and the historical activity images of the family member group collected by the security equipment; A feature recognition module, used to obtain a set of family member group features based on the historical activity images; A personnel judgment module, configured to obtain a first judgment result based on a family member group feature set and the image to be recognized; if the first judgment result is that the person in the image to be recognized is a suspicious person, obtain a second judgment result based on the member list and the image to be recognized; A result output module, used to determine whether the person in the image to be identified is a suspicious person based on the second judgment result; The personnel judgment module is also used for: According to the member list, obtaining the number of online members in the family member group; If the number of online members in the family member group is less than the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is a suspicious person; If the number of online members in the family member group is greater than or equal to the number of persons in the image to be identified, the second judgment result is that the person in the image to be identified is not a suspicious person.

8. A terminal device, comprising: A processor and a memory, wherein the memory stores a computer program that can be run on the processor, wherein when the processor executes the computer program, the method for identifying a suspicious person as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the suspicious person identification method according to any one of claims 1 to 6 is implemented.

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