CN112651335B

CN112651335B - Method, system, equipment and storage medium for identifying fellow persons

Info

Publication number: CN112651335B
Application number: CN202011566236.3A
Authority: CN
Inventors: 张倩; 王洁冰
Original assignee: Shenzhen Jizhi Digital Technology Co Ltd
Current assignee: Shenzhen Jizhi Digital Technology Co Ltd
Priority date: 2020-12-25
Filing date: 2020-12-25
Publication date: 2024-05-07
Anticipated expiration: 2040-12-25
Also published as: CN112651335A

Abstract

The application discloses a peer identification method, a system, equipment and a storage medium, wherein the method is characterized in that identification of a target person, a data searching time range and peer identification precision are obtained, a first set corresponding to the identification of the target person and the peer identification precision is obtained, data searching is carried out in the first set, a second set containing snapshot data matched with the data searching time range is obtained, the data searching range is reduced, data searching is carried out in the second set, a third set containing the target snapshot data is obtained, and the searching range is further reduced. And the first set is acquired according to the identification precision of the same person, so that the identification precision of the same person is used as one factor of the identification of the same person, and the flexibility of the identification of the same person is improved.

Description

Method, system, equipment and storage medium for identifying fellow persons

Technical Field

The present application relates to the field of object detection, and in particular, to a method, system, device, and storage medium for identifying a peer.

Background

In some scenarios (e.g., security or airport scenarios), it is desirable to be able to identify whether there is a peer relationship between different people.

But how to identify whether there is a peer relationship between different pedestrians becomes a problem.

Disclosure of Invention

In view of the above, the present application provides a method, a system, a device, and a storage medium for identifying a person in a peer relationship with a target person.

In order to achieve the above object, the following solutions have been proposed:

a method of peer identification comprising:

the method comprises the steps of obtaining identification task information of the same person, wherein the identification task information of the same person at least comprises the following steps: identification of a target person, a data searching time range and identification precision of the same person;

acquiring a first set corresponding to the identification of the target person and the identification precision of the peer person, wherein the first set comprises at least one piece of snapshot data related to the target person;

Performing data searching in the first set to obtain a second set containing snapshot data matched with the data searching time range;

acquiring the snapshot time of the target person from the snapshot data in the second set, and searching the data in the second set to obtain a third set containing target snapshot data, wherein the difference between the snapshot time of the person in the target snapshot data and the snapshot time of the target person is within a set time range;

And determining personnel having a peer relationship with the target person according to the snapshot data in the third set.

And determining the person having the peer relationship with the target person according to the snapshot data in the third set, including:

and identifying the personnel contained in the snapshot data in the third set, and taking the corresponding personnel as the personnel having a peer relationship with the target person.

Counting the number of occurrences in the third set of each person's identification contained in the snapshot data in the third set;

And carrying out descending order sequencing on the times of the occurrence of the plurality of personnel identifications in the third set to obtain a sequencing result, and taking personnel corresponding to the personnel identifications arranged in the first n in the sequencing result as personnel having a peer relationship with the target person, wherein n is an integer greater than 0.

Before the peer identification task information is obtained, the method further comprises the following steps:

Acquiring snapshot information acquired by an image recognition device, wherein the snapshot information comprises equipment identifiers of all equipment in a snapshot range, area identifiers of all areas, identifiers of all personnel, association relations between all equipment and personnel and association relations between all areas and personnel, the equipment identifiers of all the equipment are different, the area identifiers of all the areas are different, and the identifiers of all the personnel are different;

Carrying out first type structuring processing on snapshot information acquired by the image recognition device to obtain a data set conforming to zset format;

The data set includes: the key is a device set of a device identifier, the score of the device set is a first timestamp containing time information when a person is captured by the device, and the member is data containing the identifier of the person captured by the device and the first timestamp; the method comprises the steps of,

The key is an area set of area identification, the score of the area set is a second timestamp containing time information when people appear in the area, and the member is data containing the identification of the people appearing in the area and the second timestamp; the method comprises the steps of,

The key is a first personnel set of personnel identification, the score of the first personnel set is a third time stamp containing time information when the personnel is captured by equipment, and the member is data containing the identification of equipment associated with the personnel and the third time stamp; the method comprises the steps of,

A second person set with a key being a person identifier, wherein the score of the second person set is a fourth time stamp containing time information when the person appears in the area, and the member is data containing the identifier of the area associated with the person and the fourth time stamp;

the data set is stored.

If the identification precision of the peer is low, the obtaining a first set corresponding to the identification of the target person and the identification precision of the peer includes:

Acquiring at least one second personnel set corresponding to the low precision;

Searching a second person set matched with the identification of the target person in at least one second person set;

If the target person is found, taking a second person set matched with the identification of the target person as a first set;

The step of searching the data in the first set to obtain a second set matched with the data searching time range comprises the following steps:

searching for members of the score within the data searching time range in the first set;

If so, taking the searched member as a first target member, acquiring a region set of which the key is matched with the region identifier in the first target member from at least one region set, and taking the region set of which the key is matched with the region identifier in the first target member as a second set;

the step of searching the data in the second set to obtain a third set containing target snapshot data comprises the following steps:

In the second set, searching members of which the difference between the time of appearance of the person in the area and the time of appearance of the target person in the area is within a set time range;

if so, taking the searched member as a second target member, and taking a set containing the second target member as a third set.

If the identification precision of the peer is high, the obtaining a first set corresponding to the identification of the target person and the identification precision of the peer includes:

acquiring at least one first person set corresponding to the high precision;

searching for a first person set matched with the identification of the target person in at least one first person set;

If so, taking a first person set matched with the identification of the target person as a first set;

If so, taking the searched member as a first target member, acquiring a device set with a key matched with the device identifier in the first target member from at least one device set, and taking the device set with the key matched with the device identifier in the first target member as a second set;

in the second set, searching members of which the difference between the time when the person is captured by the equipment and the time when the target person is captured by the equipment is within a set time range;

The method further comprises the steps of:

And carrying out second type of structuring processing on the snapshot information acquired by the image recognition device to obtain a data set conforming to a character string format, wherein the data set conforming to the character string format comprises at least one piece of data in the character string format, a key in the data in the character string format is an identification of a person, and a value is a storage address of a snapshot image comprising the person.

The method further comprises the steps of:

searching the data set conforming to the character string format for the character string format data matched with the identification of the target person;

If the target person is found, acquiring a snapshot image containing the target person according to a storage address in the data in a character string format matched with the identification of the target person, and outputting the snapshot image containing the target person;

and/or searching the data set conforming to the character string format for the data in the character string format matched with the identification of the peer of the target person;

If the target person is found, acquiring a snapshot image of the same person containing the target person according to a storage address in data in a character string format matched with the identification of the same person of the target person, and outputting the snapshot image of the same person containing the target person.

A peer identification system, comprising:

the first acquisition module is used for acquiring the identification task information of the same person, and the identification task information of the same person at least comprises: identification of a target person, a data searching time range and identification precision of the same person;

The second acquisition module is used for acquiring a first set corresponding to the identification of the target person and the identification precision of the peer person, wherein the first set comprises at least one piece of snapshot data related to the target person;

the first searching module is used for searching data in the first set to obtain a second set containing snapshot data matched with the data searching time range;

The second searching module is used for acquiring the snapshot time of the target person from the snapshot data in the second set, searching the data in the second set, and acquiring a third set containing target snapshot data, wherein the difference between the snapshot time of the person in the target snapshot data and the snapshot time of the target person is in a set time range;

And the determining module is used for determining personnel having a peer relationship with the target person according to the snapshot data in the third set.

The determining module is specifically configured to:

The system further comprises:

The third acquisition module is used for acquiring snapshot information acquired by the image recognition device, wherein the snapshot information comprises equipment identifiers of all the equipment in a snapshot range, area identifiers of all the areas, identifiers of all the personnel, association relations between all the equipment and the personnel and association relations between all the areas and the personnel, the equipment identifiers of all the equipment are different, the area identifiers of all the areas are different, and the identifiers of all the personnel are different;

the first processing module is used for carrying out first type of structuring processing on the snapshot information acquired by the image recognition device to obtain a data set conforming to zset format;

and the storage module is used for storing the data set.

The second obtaining module is specifically configured to obtain at least one second person set corresponding to the low precision if the identification precision of the peer person is low;

the first search module is specifically configured to:

the second search module is specifically configured to:

The second obtaining module is specifically configured to:

if the identification precision of the peer is high precision, acquiring at least one first personnel set corresponding to the high precision;

the first search module is specifically configured to:

the second search module is specifically configured to:

The system further comprises:

The second processing module is used for carrying out second type of structuring processing on the snapshot information acquired by the image recognition device to obtain a data set conforming to a character string format, wherein the data set conforming to the character string format comprises at least one piece of data in the character string format, keys in the data in the character string format are identifications of personnel, and values are storage addresses of snapshot images containing the personnel.

The system further comprises:

a third searching module, configured to search, in the data set conforming to the character string format, data in the character string format that matches the identifier of the target person;

a third obtaining module, configured to obtain a snap-shot image including the target person according to a storage address in the data in the character string format that matches the identifier of the target person if the data in the character string format that matches the identifier of the target person is found;

the first output module is used for outputting a snap-shot image containing the target person;

And/or the number of the groups of groups,

A fourth searching module, configured to search, in the data set conforming to the character string format, data in the character string format that matches the identifier of the peer of the target person;

a fourth obtaining module, configured to obtain a snap shot image of a peer person including the target person according to a storage address in data in a character string format that matches an identifier of the peer person of the target person;

And the second output module is used for outputting a snap shot image of the same person containing the target person.

A computer device comprising a processor and a memory having stored therein at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by the processor to implement a method of peer identification as claimed in any preceding claim.

A computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of peer identification as claimed in any preceding claim.

According to the technical scheme, identification of the target person, the data searching time range and the identification precision of the same person are obtained by obtaining the identification task information of the same person, a first set corresponding to the identification of the target person and the identification precision of the same person is obtained, data searching is carried out in the first set, a second set containing snapshot data matched with the data searching time range is obtained, the data searching range is reduced, data searching is carried out in the second set, a third set containing the target snapshot data is obtained, the searching range is further reduced, and therefore the accuracy of searching of the snapshot data is improved by continuously reducing the searching range, and the accuracy of the determined person having the same relationship with the target person is further guaranteed.

And the first set is acquired according to the identification precision of the same person, so that the identification precision of the same person is used as one factor of the identification of the same person, and the flexibility of the identification of the same person is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of a peer identification method provided in embodiment 1 of the present application;

Fig. 2 is a schematic flow chart of a peer identification method provided in embodiment 2 of the present application;

fig. 3 is a schematic flow chart of a peer identification method provided in embodiment 3 of the present application;

fig. 4 is a schematic flow chart of a peer identification method provided in embodiment 4 of the present application;

fig. 5 is a schematic flow chart of a peer identification method provided in embodiment 5 of the present application;

fig. 6 is a schematic diagram of a logic structure of a peer identification system according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.

Referring to fig. 1, a flowchart of a peer identification method provided in embodiment 1 of the present application may be applied to an electronic device, where the application does not limit a product type of the electronic device, and as shown in fig. 1, the method may include, but is not limited to, the following steps:

step S11, acquiring identification task information of the same person, wherein the identification task information of the same person at least comprises the following steps: identification of target person, data searching time range and identification precision of the same person.

In this embodiment, a front-end interface may be set to support the user to input the peer identification task information, such as the identification of the target task, the data search time range, and the peer identification precision, in the front-end interface. Accordingly, the peer identification system can actively acquire peer identification task information from the front-end interface.

Of course, the user can upload the query parameters through the interface provided by the peer identification system, and the peer identification system analyzes the query parameters to obtain peer identification task information.

The query parameter may or may not include the accuracy of identifying the same person. Under the condition that the query parameters contain the identification precision of the peers, the query parameters are analyzed, so that the identification precision of the peers can be directly obtained. Under the condition that the query parameters do not contain the identification precision of the same-person, the default identification precision (such as low precision) of the same-person can be used as the identification precision of the same-person in the current task.

Step S12, a first set corresponding to the identification of the target person and the identification precision of the same person is obtained, wherein the first set comprises at least one piece of snapshot data related to the target person.

It can be appreciated that the data sets corresponding to different pedestrian recognition accuracies differ from one another.

Since the peer identification task is to identify a person having a peer relationship with respect to the target person, it is necessary to acquire snapshot data corresponding to the identification of the target task. And under the condition that the peer identification task has the peer identification precision requirement, acquiring a first set corresponding to the identification of the target person and the peer identification precision. Specifically, snapshot data matching the identification of the target person may be acquired from the image recognition apparatus. And acquiring snapshot data corresponding to the identification precision of the same person from snapshot data matched with the identification of the target person, wherein a set formed by the acquired data is used as a first set.

It will be appreciated that the image recognition device will take a snapshot of the person within the capture range and recognize the snapshot information from the captured image. The snapshot information may include, but is not limited to: the equipment identification of each equipment, the area identification of each area and the identification of each person in the snapshot range are different, wherein the equipment identification of each equipment is different, the area identification of each area is different, and the identification of each person is different.

Wherein the first set includes at least one piece of snapshot data related to the target person. Snapshot data related to the target person may include, but is not limited to: the data associated with the region where the target person appears (e.g., the region identification and the time of the snapshot of the target person in the region), the data associated with the device that is capturing the target person (e.g., the device identification and the time of the device's snapshot of the target person).

And step S13, carrying out data searching in the first set to obtain a second set containing snapshot data matched with the data searching time range.

Performing a data search in the first set to obtain a second set containing snapshot data matching the data search time range, which can be understood as:

searching snapshot data matched with the data searching time range in the first set;

and if so, taking the set containing the snapshot data matched with the data searching time range as a second set.

And S14, determining the snapshot time of the target person according to the snapshot data in the second set, and searching the data in the second set to obtain a third set containing target snapshot data, wherein the difference between the snapshot time of the person in the target snapshot data and the snapshot time of the target person is within a set time range.

Because the second set is obtained by searching the data in the first set, the data in the first set is snapshot data related to the target person, and therefore the data in the second set is snapshot data related to the target person and matched with the data searching time range. Because the snapshot data related to the target person contains the snapshot time of the target person, the snapshot time of the target person can be obtained from the snapshot data in the second set.

In this embodiment, the set time range may be set as required, and the present application is not limited thereto. Wherein, the smaller the setting time range is, the more accurate the snapshot data in the obtained third set is.

And S15, determining personnel having a peer relationship with the target person according to the snapshot data in the third set.

Because the snapshot data contains information such as personnel identification, personnel having a peer relationship with the target person can be determined according to the fear data in the third set.

In the application, identification of a target person, a data searching time range and peer identification precision are obtained by obtaining peer identification task information, a first set corresponding to the identification of the target person and the peer identification precision is obtained, data searching is carried out in the first set, a second set containing snapshot data matched with the data searching time range is obtained, the data searching range is reduced, data searching is carried out in the second set, a third set containing the target snapshot data is obtained, the searching range is further reduced, and the accuracy of searching the snapshot data is improved by continuously reducing the searching range, so that the accuracy of the determined person having peer relationship with the target person is ensured.

And the identification precision of the same person can be analyzed for the query parameters, and the first set is acquired according to the identification precision of the same person, so that the identification precision of the same person is taken as one factor of the identification of the same person, and the flexibility of the identification of the same person is improved.

As another alternative embodiment of the present application, referring to fig. 2, a flowchart of an embodiment 2 of a peer identification method provided by the present application is mainly a refinement of the peer identification method described in the foregoing embodiment 1, and as shown in fig. 2, the method may include, but is not limited to, the following steps:

step S21, acquiring identification task information of the same person, wherein the identification task information of the same person at least comprises the following steps: identification of target person, data searching time range and identification precision of the same person.

Step S22, a first set corresponding to the identification of the target person and the identification precision of the same person is obtained, wherein the first set comprises at least one piece of snapshot data related to the target person.

Step S23, data searching is carried out in the first set, and a second set containing snapshot data matched with the data searching time range is obtained.

And S24, acquiring the snapshot time of the target person from the snapshot data in the second set, and searching the data in the second set to obtain a third set containing target snapshot data, wherein the difference between the snapshot time of the person in the target snapshot data and the snapshot time of the target person is within a set time range.

The detailed procedure of steps S21-S24 can be referred to in the related description of steps S11-S14 in embodiment 1, and will not be described herein.

And S25, identifying the personnel contained in the snapshot data in the third set, and taking the corresponding personnel as the personnel having a congruent relationship with the target person.

In this embodiment, the person identifier and the corresponding person contained in the snapshot data in the third set are used as the person having the peer relationship with the target person, so that omission of the person having the peer relationship with the target person can be avoided, and reliability of peer identification is ensured.

As another alternative embodiment of the present application, referring to fig. 3, a flowchart of an embodiment 3 of a peer identification method provided by the present application is mainly a refinement of the peer identification method described in the foregoing embodiment 1, and as shown in fig. 3, the method may include, but is not limited to, the following steps:

step S31, acquiring identification task information of the same person, wherein the identification task information of the same person at least comprises the following steps: identification of target person, data searching time range and identification precision of the same person.

Step S32, a first set corresponding to the identification of the target person and the identification precision of the same person is obtained, wherein the first set comprises at least one piece of snapshot data related to the target person.

And step S33, carrying out data searching in the first set to obtain a second set containing snapshot data matched with the data searching time range.

And step S34, acquiring the snapshot time of the target person from the snapshot data in the second set, and searching the data in the second set to acquire a third set containing target snapshot data, wherein the difference between the snapshot time of the person in the target snapshot data and the snapshot time of the target person is within a set time range.

The detailed procedure of steps S31-S34 can be referred to in the related description of steps S11-S14 in embodiment 1, and will not be repeated here.

Step S35, counting the number of occurrences of each person identifier included in the snapshot data in the third set.

And S36, performing descending order sorting on the times of occurrence of the plurality of person identifiers in the third set to obtain a sorting result, and taking the person corresponding to the person identifiers arranged in the first n in the sorting result as the person having the same line relation with the target person, wherein n is an integer greater than 0.

In this embodiment, the size of n may be set as needed, and the present application is not limited thereto.

In this embodiment, by counting each person identifier included in the snapshot data in the third set, ordering the number of times of occurrence in the third set, and descending order the number of times of occurrence of a plurality of person identifiers in the third set, an ordering result is obtained, and the person corresponding to the person identifiers arranged in the first n in the ordering result is used as the person having the peer relationship with the target person, so that the person having the higher occurrence frequency is used as the person having the peer relationship with the target person, and the accuracy of identifying the peer person is improved.

As another alternative embodiment of the present application, referring to fig. 4, a flowchart of an embodiment 4 of a peer identification method provided by the present application is mainly an extension of the peer identification method described in the foregoing embodiment 1, and as shown in fig. 4, the method may include, but is not limited to, the following steps:

Step S41, capturing information acquired by the image recognition device is acquired.

In this embodiment, the image recognition device may capture a person in the capturing range, and recognize capturing information from the captured image. The snapshot information may include, but is not limited to: the method comprises the steps of capturing equipment identifiers of all the equipment in a range, identifying the area of each area, identifying each person, associating each equipment with the person and associating each area with the person, wherein the equipment identifiers of all the equipment are different, the area identifiers of all the areas are different, and the identifiers of all the persons are different.

The association relationship between each device and the person can be understood as: information of the person captured by each device.

The association relationship between each area and the person can be understood as: information of persons present in each area.

And step S42, carrying out first type structuring processing on the snapshot information acquired by the image recognition device to obtain a data set conforming to zset format.

The key is a second person set identified by a person, the score of the second person set is a fourth timestamp containing time information when the person appears in the area, and the member is data containing the identification of the area associated with the person and the fourth timestamp.

In this embodiment, the form of the member is not limited. For example, a member of a set of devices may be represented as: a first timestamp @ the identity of the person that the device is snap-shot; the members of the region set can be expressed as: a second timestamp @ the identity of the person present in the area; the members of the first set of people may be represented as: a third timestamp @ identification of a device associated with the person; the members of the second person set may be represented as: the fourth timestamp @ is an identification of the area associated with the person.

Next, a description will be given, by way of example, of a device set whose keys are device identifications, a region set whose keys are region identifications, a first person set whose keys are person identifications, and a second person set whose keys are person identifications:

For example, the acquired snapshot information acquired by the image recognition device includes: involves the fear of information of two persons. The snapshot information comprises: the method comprises the steps of identifying two persons, identifying 5 devices in a snapshot range, identifying 3 areas in the snapshot range, including a time stamp of time information when each person is snapshot by the device, and a time stamp including time information of each person in the area, and associating relation between each device and the person and associating relation between each area and the person. The identity of the two persons is denoted personid and personid, respectively; the identities of the 5 devices are denoted cameraid1, cameraid2, cameraid3, cameraid4, cameraid5, respectively; the identification of the 3 regions is denoted areaid, areaid2, areaid3, respectively.

Wherein the data in the device set with key cameraid1 (which may be denoted as key: CID: 1) may be seen in table 1,

TABLE 1

The data in the set of regions with key cameraid1 (which may be expressed as key: AID: 1), may be seen in table 2,

TABLE 2

Score of	Member(s)
		20200101120000	20200101120000@personid1
20200101120001	20200101120001@personid1
		20200101121001	20200101120001@personid1
20200101121101	20200101121101@personid1
		20200101121201	20200101121201@personid1
20200101120000	20200101120000@personid2
		20200101120001	20200101120001@personid2
20200101121001	20200101120001@personid2
		20200101121101	20200101120001@personid2
20200101121201	20200101120001@personid2

The data in the first set of people with key personid (which may be denoted as key: PID: 1:C) may be found in table 3,

TABLE 3 Table 3

Score of	Member(s)
		20200101120000	20200101120000@cameraid1
20200101120001	20200101120001@cameraid1
		20200101121001	20200101120001@cameraid2
20200101121101	20200101121101@cameraid3
		20200101121201	20200101121201@cameraid3

The data in the second set of people with key personid (which may be represented as keys: PID: 1:A) may be found in table 4,

TABLE 4 Table 4

Score of	Member(s)
		20200101120000	20200101120000@areaid1
20200101120001	20200101120001@areaid1
		20200101121001	20200101120001@areaid2
20200101121101	20200101121101@areaid2
		20200101121201	20200101121201@areaid2

It should be noted that, the members in the device set are the identifier of the person that includes the device and the data of the first timestamp, the members in the region set are the identifier of the person that includes the area and the data of the second timestamp, the members in the first person set are the identifier of the device that includes the person and the data of the third timestamp, and the members in the second person set are the identifier of the area that includes the person and the data of the fourth timestamp, which can ensure that the members in each set do not have repetition, reduce the workload of data searching, improve the efficiency of data searching, and further improve the efficiency of identifying the same person.

Step S43, storing the data set.

Step S44, acquiring identification task information of the same person, wherein the identification task information of the same person at least comprises the following steps: identification of target person, data searching time range and identification precision of the same person.

The detailed process of step S44 can be referred to the related description of step S11 in embodiment 1, and will not be repeated here.

And step S45, if the identification precision of the same person is low, acquiring at least one second person set corresponding to the low precision.

If the identification precision of the same person is low, the area level can be located for searching, so that at least one second person set corresponding to the low precision can be acquired.

Step S46, searching for a second person set matching with the identification of the target person in at least one second person set.

If so, step S47 is performed.

Step S47, a second person set matched with the identification of the target person is taken as a first set.

Steps S45-S47 are a specific embodiment of step S12 in example 1.

Step S48, searching for a member whose score is within the data searching time range in the first set.

If so, step S49 is performed.

Step S49, taking the searched member as a first target member, acquiring a region set with a key matched with the region identifier of the first target member from at least one region set, and taking the region set with the key matched with the region identifier of the first target member as a second set.

Steps S48-S49 are a specific implementation of step S13 in example 1.

Step S410, searching members of which the difference between the time of the appearance of the person in the area and the time of the appearance of the target person in the area is in a set time range in the second set.

If so, step S411 is performed.

Step S411, using the searched member as a second target member, and using a set containing the second target member as a third set.

Step S411 is a specific implementation of step S14 in example 1.

Referring to the above tables 1,2, 3 and 4, for example, the steps S44-S411 will be described, for example, if the target person is identified as personid a 1, the data search time range is 2020-01-12:00:00 to 2020-01-13:00:00, and the peer identification precision is low, the key is found according to personId (value personid 1) of the target person: PID 1:A. According to the data lookup time range 2020-01-0112:00:00 to 2020-01-13:00:00, in key: obtaining members with scores of [20200101120000,20200101130000] from PID 1:A to obtain a set of members and a set of areaid; find AID:1 and AID from the values in areaid set: 2 two sets of regions. Traversing AID1 and AID:2 members of the two regional sets find the time information and key contained in the time stamp: members of PID 1:A whose difference between the time information contained in the time stamp corresponding to personid1 is within 5 seconds. For example, if there is a value 2020010120000@area 1 in the member set, it is stated that the person corresponding to personid a appears in areaid a time indicated by 20200101120000, then a member whose time difference from the time indicated by 20200101120000 is not more than 5 seconds is found, and the person recorded in the found member identifies the corresponding person as a candidate co-person.

And step S412, determining personnel having a peer relationship with the target person according to the snapshot data in the third set.

The detailed process of step S412 can be referred to the related description of step S15 in embodiment 1, and will not be repeated here.

In this embodiment, after capturing information acquired by the image recognition device is acquired, a second type of structuring process may be further performed on the capturing information acquired by the image recognition device, so as to obtain a data set conforming to a character string format, where the data set conforming to the character string format includes at least one piece of data in the character string format, and a key in the data in the character string format is an identifier of a person, and a value is a storage address of a captured image including the person.

Accordingly, after acquiring the identification task information of the same person, the following steps may be further performed:

In this embodiment, capturing images of the same person including the target person are supported, capturing images of the target person are obtained, and capturing images are output, so that more processing is realized according to the capturing images.

In this embodiment, if the identification precision of the peer is low, the data can be located at the area level to perform data searching, so that the data coverage area is wide, omission of people having peer relationship with the target person is avoided, and reliability of peer identification is ensured.

As another alternative embodiment of the present application, referring to fig. 5, a flowchart of an embodiment 5 of a peer identification method provided by the present application is mainly an extension of the peer identification method described in the foregoing embodiment 1, and as shown in fig. 5, the method may include, but is not limited to, the following steps:

step S51, capturing information acquired by the image recognition device is acquired.

And step S52, carrying out first type structuring processing on the snapshot information acquired by the image recognition device to obtain a data set conforming to zset format.

Step S53, storing the data set.

Step S54, acquiring identification task information of the same person, wherein the identification task information of the same person at least comprises the following steps: identification of target person, data searching time range and identification precision of the same person.

The detailed procedure of steps S51-S54 can be referred to the related description of steps S41-S44 in embodiment 4, and will not be repeated here.

And step 55, if the identification precision of the same person is high precision, acquiring at least one first person set corresponding to the high precision.

If the identification precision of the same person is high, the device level can be located for searching, and at least one first person set corresponding to the high precision can be acquired.

Step S56, searching for a first person set matching with the identification of the target person in at least one of the first person sets.

If so, step S57 is performed.

Step S57, a first person set matching the identification of the target person is set as a first set.

Steps S55 to S57 are a specific embodiment of step S12 in example 1.

Step S58, searching for a member whose score is within the data searching time range in the first set.

If so, step S59 is performed.

Step S59, taking the searched member as a first target member, acquiring a device set with a key matched with the device identifier in the first target member from at least one device set, and taking the device set with the key matched with the device identifier in the first target member as a second set.

Steps S58-S59 are a specific implementation of step S13 in example 1.

Step S510, in the second set, searching members whose difference between the time when the person is captured by the device and the time when the target person is captured by the device is within a set time range.

If so, step S511 is performed.

Step S511, using the searched member as a second target member, and using a set containing the second target member as a third set.

Step S511 is a specific implementation of step S14 in example 1.

Referring to the above tables 1,2,3 and 4, for example, the steps S54-S511 will be described, for example, if the target person is identified as personid a1, the data search time range is 2020-01-12:00:00 to 2020-01-13:00:00, and the peer identification precision is high, the key is found according to personId (value personid 1) of the target person: PID 1:C. According to the data lookup time range 2020-01-0112:00:00 to 2020-01-13:00:00, in key: obtaining members with scores of [20200101120000,20200101130000] from PID 1:C to obtain a set of members and a set of cameraid; from the values in the cameraid set, find CID:1 and CID:2 two sets of devices. Traversing CID 1 and CID:2 members of the two device sets find the time information and key contained in the time stamp: members of the PID 1, in which the difference between the time information contained in the time stamp corresponding to personid s1 is within 5 seconds. For example, if there is a value 2020010120000@camel 1 in the member set, it is stated that the person corresponding to personid1 is snapped by the device corresponding to cameraid1 at the time indicated by 20200101120000, then a member with a time difference of no more than 5 seconds from the time indicated by 20200101120000 is searched, and the person recorded in the searched member identifies the corresponding person as a candidate co-worker.

And step S512, determining personnel having a peer relationship with the target person according to the snapshot data in the third set.

The detailed process of step S512 may be referred to the related description of step S15 in embodiment 1, and will not be described herein.

In this embodiment, if the identification precision of the peer is high, the peer can be located to the equipment level to perform data searching, so as to ensure the accuracy of the data and the identification precision of the peer.

Next, the peer identification system provided by the present application will be described, and the peer identification system described below and the peer identification method described above may be referred to correspondingly.

Referring to fig. 6, the peer identification system includes: the first acquisition module 100, the second acquisition module 200, the first lookup module 300, the second lookup module 400, and the determination module 500.

The first obtaining module 100 is configured to obtain peer identification task information, where the peer identification task information at least includes: identification of target person, data searching time range and identification precision of the same person.

A second obtaining module 200, configured to obtain a first set corresponding to the identification of the target person and the identification precision of the peer person, where the first set includes at least one piece of snapshot data related to the target person.

A first searching module 300, configured to perform data searching in the first set, and obtain a second set including snapshot data that matches the data searching time range.

And the second searching module 400 is configured to obtain the snapshot time of the target person from the snapshot data in the second set, and perform data searching in the second set to obtain a third set including the target snapshot data, where a difference between the snapshot time of the person in the target snapshot data and the snapshot time of the target person is within a set time range.

And the determining module 500 is configured to determine, according to the snapshot data in the third set, a person having a peer relationship with the target person.

In this embodiment, the determining module 500500 may specifically be configured to:

In this embodiment, the system may further include:

and the storage module is used for storing the data set.

In this embodiment, the second obtaining module 200 may be specifically configured to obtain, if the identification precision of the peer is low, at least one second person set corresponding to the low precision;

the first search module 300 may specifically be configured to:

The second search module 400 may specifically be configured to:

In this embodiment, the second obtaining module 200 may specifically be configured to:

the first search module 300 may specifically be configured to:

the second search module 400 is specifically configured to:

In this embodiment, the system may further include:

And/or the number of the groups of groups,

In another embodiment of the present application, a computer device is provided, where the computer device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, where the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement a peer identification method as described in any of the above method embodiments 1-5.

In another embodiment of the present application, a computer readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, implements a peer identification method as described in any of the above method embodiments 1-5.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying a peer, comprising:

2. The method of claim 1, wherein the determining, based on the snapshot data in the third set, a person in a peer relationship with the target person comprises:

3. The method of claim 1, wherein the determining, based on the snapshot data in the third set, a person in a peer relationship with the target person comprises:

4. The method of claim 1, wherein prior to the obtaining the peer identification task information, further comprising:

the data set is stored.

5. The method of claim 4, wherein if the accuracy of peer identification is low, the obtaining the first set corresponding to the identification of the target persona and the accuracy of peer identification comprises:

Acquiring at least one second personnel set corresponding to the low precision;

6. The method of claim 4, wherein if the accuracy of peer identification is high, the obtaining the first set corresponding to the identification of the target persona and the accuracy of peer identification comprises:

acquiring at least one first person set corresponding to the high precision;

7. The method according to claim 4, wherein the method further comprises:

8. The method of claim 7, wherein the method further comprises:

9. A peer identification system, comprising:

10. A computer device comprising a processor and a memory having stored therein at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by the processor to implement the peer identification method of any of claims 1 to 8.

11. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the peer identification method according to any of claims 1 to 8.