CN111476685B - Behavior analysis method, device and equipment - Google Patents

Behavior analysis method, device and equipment Download PDF

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CN111476685B
CN111476685B CN202010224671.1A CN202010224671A CN111476685B CN 111476685 B CN111476685 B CN 111476685B CN 202010224671 A CN202010224671 A CN 202010224671A CN 111476685 B CN111476685 B CN 111476685B
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stranger
vertex
face data
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strangers
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CN111476685A (en
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秦基伟
员晓毅
林大镰
裴卫斌
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Shenzhen ZNV Technology Co Ltd
Nanjing ZNV Software Co Ltd
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Nanjing ZNV Software Co Ltd
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Abstract

The embodiment of the invention provides a behavior analysis method, a behavior analysis device and behavior analysis equipment. The method comprises the following steps: the method comprises the steps of carrying out strange face image recognition on face data in a preset time period acquired by at least one image acquisition device, wherein the face data comprises face images, acquisition time information and acquisition position information; clustering the face images of the identified strangers to acquire face data of the same stranger; constructing a graph database according to the face data of each stranger; and identifying abnormal behaviors of strangers according to the graph database. The method provided by the embodiment of the invention realizes the analysis of the behaviors of strangers based on the acquired face data, and timely discovers the abnormal behaviors of the strangers, so that the safety of communities can be improved.

Description

Behavior analysis method, device and equipment
Technical Field
The invention relates to the technical field of community security, in particular to a behavior analysis method, a behavior analysis device and behavior analysis equipment.
Background
Cities are the product of the development of human civilization, communities are the most basic components of the cities, communities serve as carriers for the survival and development of urban residents, and the safety index of the communities is the core of the residents. However, with the continuous improvement of the social development level, the third party service industries such as express delivery, takeaway and the like are raised, so that a large number of strangers are surmounted every day in an originally relatively closed community. Lawbreakers may be incorporated therein, creating a threat to the personal and property safety of community residents.
The intelligent community is used as a new idea of community management, and the safety index of the community is effectively improved. By installing various sensing devices such as a face snapshot camera and a face access control, face data are collected, and an artificial intelligence algorithm is combined, so that effective screening of resident population and strange population is realized, but no effective solution is available for behavioral analysis of screened strangers at present. How to analyze the behaviors of strangers based on the collected face data and discover abnormal behaviors in time is a problem to be solved urgently.
Disclosure of Invention
The embodiment of the invention provides a behavior analysis method, device and equipment, which are used for analyzing the behavior of strangers based on acquired face data and timely finding out abnormal behaviors of the strangers.
In a first aspect, an embodiment of the present invention provides a behavior analysis method, including:
the method comprises the steps of carrying out strange face image recognition on face data in a preset time period acquired by at least one image acquisition device, wherein the face data comprises face images, acquisition time information and acquisition position information;
clustering the face images of the identified strangers to acquire face data of the same stranger;
constructing a graph database according to the face data of each stranger;
and identifying abnormal behaviors of strangers according to the graph database.
In one embodiment, the step of identifying strange face images of face data in a preset time period acquired by at least one image acquisition device includes:
matching the collected face data with a resident face database;
if the matching fails, the face data is the face data of strangers.
In one embodiment, constructing a graph database from face data of strangers includes:
identifying each stranger as a first vertex, wherein the first vertex comprises identity information; identifying the acquisition devices at each acquisition location as a second vertex, the second vertex comprising acquisition location information, the acquisition location comprising at least an entrance to the area and an exit to the area; and one edge between the first vertex and the second vertex corresponds to one piece of face data, and the edge comprises acquisition time information.
In one embodiment, identifying abnormal behavior of strangers from a graph database includes:
if the number of edges between the vertex of a stranger and the vertex of the acquisition device at the entrance of the region in the graph database is larger than a preset frequency threshold, identifying the behavior of the stranger as a first type of abnormal behavior, wherein the first type of abnormal behavior is used for indicating the stranger to frequently enter the region.
In one embodiment, identifying abnormal behavior of strangers from a graph database includes:
if β2- β1> α, where β1 is the acquisition time represented by the edge between a stranger vertex in the graph database and the acquisition device vertex at the entrance of the region, β2 is the acquisition time represented by the edge between the stranger vertex in the graph database and the acquisition device vertex at the exit of the region, and α is a preset residence time threshold, identifying the behavior of the stranger as a second type of abnormal behavior, where the second type of abnormal behavior is used to indicate that the stranger is in residence in the region for a long time.
In one embodiment, identifying abnormal behavior of strangers from a graph database includes:
if an edge exists between a vertex of a stranger and a second vertex in the graph database in the preset abnormal time period, the behavior of the stranger is identified as a third abnormal behavior, and the third abnormal behavior is used for indicating the stranger to enter and exit the corresponding area in the abnormal time period.
In one embodiment, the method further comprises:
if the abnormal behavior is identified, the abnormal information is sent to related workers, wherein the abnormal information comprises picture information of strangers, abnormal behavior occurrence time information and abnormal behavior reasons.
In a second aspect, an embodiment of the present invention provides a behavior analysis apparatus, including:
the identification module is used for identifying strange face images of face data in a preset time period acquired by at least one image acquisition device, wherein the face data comprises face images, acquisition time information and acquisition position information;
the clustering module is used for clustering the face images of the identified strangers to acquire face data of the same stranger;
the construction module is used for constructing a graph database according to the face data of each stranger;
and the analysis module is used for identifying abnormal behaviors of strangers according to the graph database.
In a third aspect, an embodiment of the present invention provides a behavior analysis apparatus, including:
at least one processor and memory;
the memory stores computer-executable instructions;
at least one processor executes computer-executable instructions stored in a memory, causing the at least one processor to perform the behavior analysis method of any one of the first aspects.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, are configured to implement a behavior analysis method according to any one of the first aspects.
According to the behavior analysis method, device and equipment provided by the embodiment of the invention, the face data of strangers are firstly identified from the collected face data, so that the data processing amount can be greatly reduced, the data processing efficiency is improved, and the behavior analysis is more targeted; then clustering the face data of the identified strangers to obtain face data of the same strangers, constructing a graph database according to the face data of each stranger, and vividly reflecting the association relationship between the strangers and different acquisition devices at different moments by utilizing the relationship between the vertexes and the edges of the graph database; and finally, identifying abnormal behaviors of the strangers according to the graph database, and rapidly and effectively analyzing the behaviors of the strangers based on the graph database, so that the behaviors of the strangers are analyzed based on the acquired face data, the abnormal behaviors of the strangers are found in time, and the safety of communities can be improved. .
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FIG. 1 is a flow chart of a behavior analysis method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for identifying face data of strangers according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a portion of a graph database according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a behavior analysis device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a behavior analysis device according to an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings by means of specific embodiments. Wherein like elements in different embodiments are numbered alike in association. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted, or replaced by other elements, materials, or methods in different situations. In some instances, some operations associated with the present application have not been shown or described in the specification to avoid obscuring the core portions of the present application, and may not be necessary for a person skilled in the art to describe in detail the relevant operations based on the description herein and the general knowledge of one skilled in the art.
Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning. The terms "coupled" and "connected," as used herein, are intended to encompass both direct and indirect coupling (coupling), unless otherwise indicated.
The behavior analysis method provided by the embodiment of the invention can be used for intelligent communities, intelligent parks, intelligent factories, intelligent campuses and other scenes provided with face data acquisition equipment. In these scenarios, face data may be collected by the collecting device, and in the following, specific embodiments will be used to describe how to identify abnormal behaviors of strangers based on the collected face data, and perform timely alarm to improve security.
Fig. 1 is a flowchart of a behavior analysis method according to an embodiment of the invention. As shown in fig. 1, the behavior analysis method provided in this embodiment may include:
s101, recognizing strange face images of face data in a preset time period acquired by at least one image acquisition device, wherein the face data comprises face images, acquisition time information and acquisition position information.
The face data collected in this embodiment may come from different collection devices, taking an intelligent community as an example, a plurality of cameras are generally installed at a plurality of positions to collect face data at different positions, and the face data collected in this embodiment may include face data collected by all collection devices. The face data may include face image information, acquisition time information, and acquisition position information. The face image information may be color picture or gray level picture, or may be video information, which is not limited in this embodiment; the acquisition time information is used for recording the acquisition time of the face image information; the acquisition position information is used to identify the acquisition position of the face image information, and may be represented by, for example, the number of the acquisition device.
In order to enable timely early warning, the face data can be identified in real time in the embodiment. The strangers are relative to the resident population. For a smart community, the residents in the community are resident population; for an intelligent campus, students and teachers in the campus are resident population; for intelligent factories, workers in the factory are resident population. For the collected face data, a face recognition method can be adopted to recognize strange face data in the collected face data.
Referring to fig. 2, in an alternative embodiment, identifying strange face data from the collected face data may include:
s1011, judging whether the collected face data is matched with the resident face database. If the match fails, then S1012 is performed; otherwise, S1013 is executed.
In this embodiment, a resident face database needs to be created in advance, where the resident face database includes face images of resident population. The resident face database can be created according to a specific application scene, and for the intelligent community, face images of residents in the community are collected to create the resident face database; for an intelligent campus, face images of students and teachers in the campus are collected to create a resident face database; for an intelligent factory, face images of workers in the factory are collected to create a resident face database. And the resident face database needs to be updated in time according to the variation of the resident population so as not to identify the resident population as a stranger. In this embodiment, for example, methods such as template matching and machine learning may be used to match the collected face data with the face data in the resident face database.
S1012, determining the face data as the face data of strangers.
S1013, determining the face data as the face data of the resident population.
It can be understood that the resident face data in the collected face data is far more than the strange face data, the collected face data is divided into the resident face data and the strange face data by identifying the collected face data, and further behavior analysis is carried out on the strange face data only to identify abnormal behaviors of the strange person, so that the data processing capacity can be greatly reduced, the data processing efficiency is improved, and the behavior analysis is more targeted.
S102, clustering the face data of the identified strangers to acquire the face data of the same stranger.
The strange face data identified from the collected face data may come from different strangers, and is directly meaningless to analyze, so after the strange face data is identified from the collected face data, the identified strange face data is clustered, and the face data belonging to the same stranger is obtained. The strange face data may be clustered by using an existing clustering method, which is not limited in this embodiment.
The identified strange face data is clustered to obtain a plurality of clusters, for example, it can be determined that face data belonging to one cluster belongs to the same stranger. In order to further improve accuracy, face data away from the cluster center may be filtered out. Face data from different acquisition devices, which belong to the same stranger and are acquired at different moments, reflect the action track of the stranger. Alternatively, unique virtual identity IDs may be used to identify identity information for multiple face data of the same stranger.
S103, constructing a graph database according to face data of each stranger.
In an alternative embodiment, constructing a graph database according to face data of strangers may include:
identifying each stranger as a first vertex, wherein the first vertex comprises identity information; identifying the acquisition devices at each acquisition location as a second vertex, the second vertex comprising acquisition location information, the acquisition location comprising at least an entrance to the area and an exit to the area; and one edge between the first vertex and the second vertex corresponds to one piece of face data, and the edge comprises acquisition time information. In practical applications, the collection locations may include, for example, community entrances, community exits, building unit door entrances, building unit door exits, and key intersections.
Referring to fig. 3, a graph database constructed from face data of strangers with an ID 320321 is shown. As shown in fig. 3, the thick solid circle in the center represents the first vertex corresponding to a stranger with ID 320321; thin solid circles with peripheral numbers of 0-4 respectively represent acquisition equipment (with a number of 0) at a community entrance, acquisition equipment (with a number of 1) at a community exit, acquisition equipment (with a number of 2) at a building unit door entrance, acquisition equipment (with a number of 3) at a building unit door exit and second vertexes corresponding to acquisition equipment (with a number of 4) at a key road junction, each edge between the first vertexes and the second vertexes corresponds to one face data of a stranger with an ID of 320321, and the moment of acquiring the face data is recorded. Taking graph database neo4j as an example, store the statement as follows:
Match(p:Persion{id:320321}),(c:Camera{type:0})
Create(p)-[r:Cross{time:20191217080059}]->(c)return p,b
an edge shown by a broken line will be generated in fig. 3 to indicate that a stranger with ID 320321 has traveled through the community portal 00 minutes 59 seconds at 2019, 12, 17, 08.
Through the relationship between the vertex and the edge of the graph database, the association relationship between strangers and different acquisition devices at different moments can be recorded in an image.
S104, identifying abnormal behaviors of strangers according to the graph database.
Because the relationship between the vertex and the edge of the graph database records the association relationship between strangers and different acquisition devices at different times, the abnormal behaviors of the strangers can be identified by searching the relationship between the vertex and the edge of the graph database.
Specifically, if the number of edges between a vertex of a stranger and a vertex of an acquisition device at an entrance of an area in the graph database is greater than a preset frequency threshold, identifying the behavior of the stranger as a first type of abnormal behavior, wherein the first type of abnormal behavior is used for indicating the stranger to frequently enter the area. For example, the preset frequency threshold may be set to N, where N is a positive integer, and the specific value of N may be determined according to statistics, where a larger N represents a higher frequency of entry. The method comprises the steps that batch searching can be conducted on a graph database, and the number n of edges between the top points of strangers and the top points of collecting equipment at the entrance of a community in a preset time period in the graph database is obtained, wherein the number n is the number of times that the strangers enter the community in the preset time period; if N is greater than N, the stranger frequently enters the community, and the suspicious behavior index is higher when the exceeding value is higher.
If β2- β1> α, where β1 is the acquisition time represented by the edge between a stranger vertex in the graph database and the acquisition device vertex at the entrance of the region, β2 is the acquisition time represented by the edge between the stranger vertex in the graph database and the acquisition device vertex at the exit of the region, and α is a preset residence time threshold, identifying the behavior of the stranger as a second type of abnormal behavior, where the second type of abnormal behavior is used to indicate that the stranger is in residence in the region for a long time. Wherein the preset residence time threshold α may be set in seconds. For example, retrieving an edge between a stranger vertex and a collection device vertex at the entrance to the community within a specified time, which indicates the time β1 that the stranger entered the community; retrieving an edge between a stranger vertex and a collection device vertex at the community exit within a specified time, which indicates a time β2 when the stranger leaves the community; if β2- β1> α, wherein β2- β1 represents the time that a stranger is retained in the community, then it is determined that the stranger is not in and out for a long time, and the longer the retention time, the higher the suspicion index.
If an edge exists between a vertex of a stranger and a second vertex in the graph database in the preset abnormal time period, the behavior of the stranger is identified as a third abnormal behavior, and the third abnormal behavior is used for indicating the stranger to enter and exit the corresponding area in the abnormal time period. For example, a preset anomaly time period may be set to be 23:00-5:00, in which the number of edges S between each stranger vertex and all the collection device vertices in the batch search map database is greater, if S >0, the stranger is active in the community in the anomaly time period, and the greater the S value, the higher the suspicious index.
According to the behavior analysis method provided by the embodiment, the face data of strangers are firstly identified from the collected face data, so that the data processing amount can be greatly reduced, the data processing efficiency is improved, and the behavior analysis is more targeted; then clustering the face data of the identified strangers to obtain face data of the same strangers, constructing a graph database according to the face data of each stranger, and vividly reflecting the association relationship between the strangers and different acquisition devices at different moments by utilizing the relationship between the vertexes and the edges of the graph database; and finally, identifying abnormal behaviors of the strangers according to the graph database, and rapidly and effectively analyzing the behaviors of the strangers based on the graph database, so that the behaviors of the strangers are analyzed based on the acquired face data, the abnormal behaviors of the strangers are found in time, and the safety of communities can be improved.
On the basis of the above embodiment, in order to timely process the abnormal situation, if the abnormal behavior is identified, the abnormal information is sent to the relevant workers for early warning. The anomaly information may be sent to community security manager, for example, by short messages, weChat or platform messages, etc. The abnormal information may include picture information of strangers, abnormal behavior occurrence time information, and abnormal behavior cause.
Fig. 4 is a schematic structural diagram of a behavior analysis device according to an embodiment of the invention. As shown in fig. 4, the behavior analysis device 40 provided in this embodiment may include: an identification module 401, a clustering module 402, a construction module 403 and an analysis module 404.
The recognition module 401 is configured to perform strange face image recognition on face data in a preset time period acquired by at least one image acquisition device, where the face data includes a face image, acquisition time information and acquisition position information;
the clustering module 402 is configured to cluster the face images of the identified strangers, and obtain face data of the same stranger;
a construction module 403, configured to construct a graph database according to face data of each stranger;
an analysis module 404 is used to identify abnormal behavior of strangers from the graph database.
The behavior analysis device provided in this embodiment may be used to execute the technical scheme of the method embodiment corresponding to fig. 1, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the identification module 401 is configured to perform strange face image identification on face data in a preset period of time acquired by at least one image acquisition device, and may specifically include:
matching the collected face data with a resident face database;
if the matching fails, the face data is the face data of strangers.
In one embodiment, the building module 403 is configured to build a graph database according to face data of strangers, which may specifically include:
identifying each stranger as a first vertex, wherein the first vertex comprises identity information; identifying the acquisition devices at each acquisition location as a second vertex, the second vertex comprising acquisition location information, the acquisition location comprising at least an entrance to the area and an exit to the area; and one edge between the first vertex and the second vertex corresponds to one piece of face data, and the edge comprises acquisition time information.
In one embodiment, the analysis module 404 is configured to identify abnormal behaviors of strangers according to a graph database, and may specifically include:
if the number of edges between the vertex of a stranger and the vertex of the acquisition device at the entrance of the region in the graph database is larger than a preset frequency threshold, identifying the behavior of the stranger as a first type of abnormal behavior, wherein the first type of abnormal behavior is used for indicating the stranger to frequently enter the region.
In one embodiment, the analysis module 404 is configured to identify abnormal behaviors of strangers according to a graph database, and may specifically include:
if β2- β1> α, where β1 is the acquisition time represented by the edge between a stranger vertex in the graph database and the acquisition device vertex at the entrance of the region, β2 is the acquisition time represented by the edge between the stranger vertex in the graph database and the acquisition device vertex at the exit of the region, and α is a preset residence time threshold, identifying the behavior of the stranger as a second type of abnormal behavior, where the second type of abnormal behavior is used to indicate that the stranger is in residence in the region for a long time.
In one embodiment, the analysis module 404 is configured to identify abnormal behaviors of strangers according to a graph database, and may specifically include:
if an edge exists between a vertex of a stranger and a second vertex in the graph database in the preset abnormal time period, the behavior of the stranger is identified as a third abnormal behavior, and the third abnormal behavior is used for indicating the stranger to enter and exit the corresponding area in the abnormal time period.
In one embodiment, the behavior analysis device 40 may further include an early warning module (not shown in the figure) for sending, if abnormal behavior is identified, abnormal information to the related worker, where the abnormal information includes picture information of strangers, occurrence time information of abnormal behavior, and cause of abnormal behavior.
The embodiment of the present invention further provides a behavior analysis device, please refer to fig. 5, and the embodiment of the present invention is only illustrated by taking fig. 5 as an example, and the present invention is not limited thereto. Fig. 5 is a schematic structural diagram of a behavior analysis device according to an embodiment of the present invention. As shown in fig. 5, the behavior analysis device 50 provided in the present embodiment may include: memory 501, processor 502, and bus 503. Wherein a bus 503 is used to enable the connection between the various components.
The memory 501 stores a computer program, which when executed by the processor 502 can implement the technical solution of the behavior analysis method provided in any of the above method embodiments.
Wherein the memory 501 and the processor 502 are electrically connected, either directly or indirectly, to enable transmission or interaction of data. For example, the elements may be electrically coupled to each other via one or more communication buses or signal lines, such as bus 503. The memory 501 stores therein a computer program for implementing a behavior analysis method, including at least one software functional module that may be stored in the memory 501 in the form of software or firmware, and the processor 502 executes various functional applications and data processing by running the software program and the module stored in the memory 501.
The Memory 501 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The memory 501 is used for storing a program, and the processor 502 executes the program after receiving an execution instruction. Further, the software programs and modules within the memory 501 may also include an operating system, which may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.
The processor 502 may be an integrated circuit chip with signal processing capabilities. The processor 502 may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), and the like. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. It will be appreciated that the configuration of fig. 5 is merely illustrative and may include more or fewer components than shown in fig. 5 or have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware and/or software.
It should be noted that, the behavior analysis device provided in this embodiment includes, but is not limited to, at least one of the following: user side equipment and network side equipment. User-side devices include, but are not limited to, computers, smart phones, tablets, digital broadcast terminals, messaging devices, game consoles, personal digital assistants, and the like. Network-side devices include, but are not limited to, a single network server, a server group of multiple network servers, or a cloud of large numbers of computers or network servers based on cloud computing, where cloud computing is one of distributed computing, and is a super virtual computer consisting of a group of loosely coupled computers.
Reference is made to various exemplary embodiments herein. However, those skilled in the art will recognize that changes and modifications may be made to the exemplary embodiments without departing from the scope herein. For example, the various operational steps and components used to perform the operational steps may be implemented in different ways (e.g., one or more steps may be deleted, modified, or combined into other steps) depending on the particular application or taking into account any number of cost functions associated with the operation of the system.
Additionally, as will be appreciated by one of skill in the art, the principles herein may be reflected in a computer program product on a computer readable storage medium preloaded with computer readable program code. Any tangible, non-transitory computer readable storage medium may be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-ROMs, DVDs, blu-Ray disks, etc.), flash memory, and/or the like. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including means which implement the function specified. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified.
While the principles herein have been shown in various embodiments, many modifications of structure, arrangement, proportions, elements, materials, and components, which are particularly adapted to specific environments and operative requirements, may be used without departing from the principles and scope of the present disclosure. The above modifications and other changes or modifications are intended to be included within the scope of this document.
The foregoing detailed description has been described with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes may be made without departing from the scope of the present disclosure. Accordingly, the present disclosure is to be considered as illustrative and not restrictive in character, and all such modifications are intended to be included within the scope thereof. Also, advantages, other advantages, and solutions to problems have been described above with regard to various embodiments. The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature. 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, system, article, or apparatus. Furthermore, the term "couple" and any other variants thereof are used herein to refer to physical connections, electrical connections, magnetic connections, optical connections, communication connections, functional connections, and/or any other connection.
The foregoing description of the invention has been presented for purposes of illustration and description, and is not intended to be limiting. Several simple deductions, modifications or substitutions may also be made by a person skilled in the art to which the invention pertains, based on the idea of the invention.

Claims (9)

1. A method of behavioral analysis comprising:
the method comprises the steps of carrying out strange face image recognition on face data in a preset time period acquired by at least one image acquisition device, wherein the face data comprises face images, acquisition time information and acquisition position information;
clustering the face images of the identified strangers to acquire face data of the same stranger;
constructing a graph database according to the face data of each stranger;
the construction of the graph database according to the face data of each stranger comprises the following steps:
identifying each stranger as a first vertex, wherein the first vertex comprises identity information; identifying the acquisition devices at each acquisition location as a second vertex, the second vertex comprising acquisition location information, the acquisition location comprising at least an entrance to the area and an exit to the area; one edge between the first vertex and the second vertex corresponds to one face data, and the edge comprises acquisition time information;
and identifying abnormal behaviors of strangers according to the graph database.
2. The method of claim 1, wherein the step of identifying strange face images from face data acquired by at least one image acquisition device for a predetermined period of time comprises:
matching the collected face data with a resident face database;
if the matching fails, the face data is the face data of strangers.
3. The method of claim 1, wherein the identifying abnormal behavior of strangers from the graph database comprises:
if the number of edges between the vertex of a stranger and the vertex of the collecting device at the entrance of the region in the graph database is larger than a preset frequency threshold, identifying the behavior of the stranger as a first type of abnormal behavior, wherein the first type of abnormal behavior is used for indicating the stranger to frequently enter the region.
4. The method of claim 1, wherein the identifying abnormal behavior of strangers from the graph database comprises:
if β2- β1> α, where β1 is the acquisition time represented by the edge between a stranger vertex in the graph database and the acquisition device vertex at the entrance of the region, β2 is the acquisition time represented by the edge between the stranger vertex in the graph database and the acquisition device vertex at the exit of the region, and α is a preset residence time threshold, identifying the behavior of the stranger as a second type of abnormal behavior, where the second type of abnormal behavior is used to indicate that the stranger is in residence in the region for a long time.
5. The method of claim 1, wherein the identifying abnormal behavior of strangers from the graph database comprises:
if an edge exists between a vertex of a stranger and the second vertex in the graph database in the preset abnormal time period, identifying the behavior of the stranger as a third type of abnormal behavior, wherein the third type of abnormal behavior is used for indicating the stranger to enter and exit a corresponding area in the abnormal time period.
6. The method of any one of claims 1-5, wherein the method further comprises:
if the abnormal behavior is identified, the abnormal information is sent to related workers, wherein the abnormal information comprises picture information of strangers, abnormal behavior occurrence time information and abnormal behavior reasons.
7. A behavior analysis device, comprising:
the identification module is used for identifying strange face images of face data in a preset time period acquired by at least one image acquisition device, wherein the face data comprises face images, acquisition time information and acquisition position information;
the clustering module is used for clustering the face images of the identified strangers to acquire face data of the same stranger;
the construction module is used for constructing a graph database according to the face data of each stranger;
the construction of the graph database according to the face data of each stranger comprises the following steps:
identifying each stranger as a first vertex, wherein the first vertex comprises identity information; identifying the acquisition devices at each acquisition location as a second vertex, the second vertex comprising acquisition location information, the acquisition location comprising at least an entrance to the area and an exit to the area; one edge between the first vertex and the second vertex corresponds to one face data, and the edge comprises acquisition time information;
and the analysis module is used for identifying abnormal behaviors of strangers according to the graph database.
8. A behavior analysis apparatus, characterized by comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the behavior analysis method of any one of claims 1-6.
9. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to implement the behavior analysis method of any one of claims 1-6.
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