CN111476685A - Behavior analysis method, device and equipment - Google Patents
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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: performing stranger 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 of the embodiment of the invention realizes the analysis of the behavior of strangers based on the collected face data, and discovers the abnormal behavior of the strangers in time, thereby improving the safety of communities.
Description
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
The city is the product of human civilization development, the community is the most basic component of the city, the community is used as a carrier for the survival and development of urban residents, and the safety index of the community is the core of the attention of the residents. However, as the social development level is continuously improved, the third party service industry such as express delivery, takeout and the like is started, so that a large number of strangers are introduced every day to the originally relatively closed community. Lawbreakers may be mixed in the system, and certain threats are generated to the personal safety and property safety of community residents.
As a new concept of community management, the intelligent community effectively improves the safety index of the community. The method is characterized in that various sensing devices such as a face snapshot camera and a face access control are mounted, face data are collected, and an artificial intelligence algorithm is combined, so that the resident population and the stranger population are effectively screened, but no effective solution is provided for behavior analysis of screened strangers at present. How to analyze the behavior of strangers based on the collected face data and find abnormal behavior in time is a problem which needs to be solved urgently.
Disclosure of Invention
The embodiment of the invention provides a behavior analysis method, a behavior analysis device and behavior analysis equipment, which are used for analyzing the behavior of a stranger based on collected face data and discovering the abnormal behavior of the stranger.
In a first aspect, an embodiment of the present invention provides a behavior analysis method, including:
performing stranger 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, stranger face image recognition is performed on face data acquired by at least one image acquisition device within a preset time period, and comprises the following steps:
matching the collected face data with a resident face database;
and if the matching fails, the face data is the face data of a stranger.
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 acquisition devices located at each acquisition location as second vertices, the second vertices including acquisition location information, the acquisition locations including at least an entrance of a region and an exit of the region; an 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 a stranger from a graph database includes:
if the number of edges between the top point of a certain stranger and the top point of the acquisition equipment at the entrance of the area in the graph database is larger than a preset frequency threshold, the behavior of the stranger is identified as a first-class abnormal behavior, and the first-class abnormal behavior is used for indicating that the stranger frequently enters the area.
In one embodiment, identifying abnormal behavior of a stranger from a graph database includes:
β 2- β 1> α, wherein β 1 is the collection time represented by the edge between the top of a stranger and the top of a collection device at the entrance of an area in a graph database, β 2 is the collection time represented by the edge between the top of the stranger and the top of the collection device at the exit of the area in the graph database, α is a preset retention time threshold, the behavior of the stranger is recognized as a second type of abnormal behavior, and the second type of abnormal behavior is used for indicating that the stranger is retained in the area for a long time.
In one embodiment, identifying abnormal behavior of a stranger from a graph database includes:
if an edge exists between a top point and a second top point of a stranger in the graph database within a 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 come in or go out of a corresponding area within the abnormal time period.
In one embodiment, the method further comprises:
and if the abnormal behavior is identified, sending abnormal information to a related worker, 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 carrying out stranger face image identification 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;
the clustering module is used for clustering the face images of the identified strangers to acquire the 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 device, including:
at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the behavior analysis method according to any one of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer-executable instructions are stored, and when executed by a processor, the computer-executable instructions are configured to implement the behavior analysis method according to any one of the first aspect.
According to the behavior analysis method, the behavior analysis device and the behavior analysis 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 the face data belonging to the same stranger, constructing a graph database according to the face data of each stranger, and vividly reflecting the association relationship between the strangers and different acquisition equipment at different moments by using the relationship between the top points and edges of the graph database; and finally, recognizing the abnormal behavior of the stranger according to the graph database, and quickly and effectively analyzing the behavior of the stranger based on the graph database, so that the behavior of the stranger is analyzed based on the collected face data, the abnormal behavior of the stranger is discovered in time, and the safety of the community can be improved. .
Drawings
Fig. 1 is a flowchart of a behavior analysis method according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for recognizing face data of a stranger according to an embodiment of the present invention;
FIG. 3 is a partial schematic view of a graph database according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a behavior analysis apparatus 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 present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments are numbered with like associated elements. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning. The term "connected" and "coupled" when used in this application, unless otherwise indicated, includes both direct and indirect connections (couplings).
The behavior analysis method provided by the embodiment of the invention can be used for not only the smart community, but also the smart campus, the smart factory, the smart campus and other scenes with the face data acquisition equipment. In these scenes, the face data may be collected by the collecting device, and how to identify abnormal behavior of strangers based on the collected face data will be described below by using a specific embodiment to give an alarm in time, so as to improve security.
Fig. 1 is a flowchart of a behavior analysis method according to an embodiment of the present invention. As shown in fig. 1, the behavior analysis method provided in this embodiment may include:
s101, stranger face image recognition is carried out on face data in a preset time period collected by at least one image collecting device, wherein the face data comprises face images, collecting time information and collecting position information.
The face data of collection in this embodiment may come from different collection devices to the example of wisdom community can be used for gathering the face data of different positions department at a plurality of position installation cameras usually, and the face data of collection in this embodiment can include the face data that all collection devices gathered. The face data may include face image information, acquisition time information, and acquisition location information. The face image information may be a color picture or a grayscale 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 the number of the acquisition device, for example.
In order to be able to perform early warning in time, the face data can be identified in real time in the embodiment. So-called strangers are relative to the resident population. For an intelligent community, residents in the community are resident population; for a smart campus, students and teachers in the campus are resident population; for smart factory floor, workers in the factory floor are a resident population. For the collected face data, a face recognition method can be adopted to recognize strange face data.
Referring to fig. 2, in an alternative embodiment, the recognizing strange face data from the collected face data may include:
and S1011, judging whether the acquired face data is matched with the resident face database. If the matching fails, S1012 is executed; otherwise, S1013 is performed.
In this embodiment, a resident face database is created in advance, and the resident face database includes a face image of a 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 a smart campus, face images of students and teachers in the campus are collected to create a resident face database; for an intelligent factory floor, face images of workers in the factory floor are collected to create a resident face database. And the resident face database needs to be updated in time according to the change of the resident population so as to prevent the resident population from being identified as strangers. In this embodiment, for example, the collected face data may be matched with the face data resident in the face database by using methods such as template matching and machine learning.
And S1012, determining the face data as the face data of a stranger.
And 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 identified, the data is divided into the resident face data and the strange face data, and then only the strange face data is subjected to further behavior analysis to identify the abnormal behavior of the stranger, so that the data processing amount 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 obtain the face data of the same stranger.
The strange face data recognized from the collected face data may come from different strangers and are analyzed meaningless directly, so that after the strange face data are recognized from the collected face data, the recognized strange face data are clustered to obtain the face data belonging to the same stranger. The strange face data may be clustered by using an existing clustering method, which is not limited in this embodiment.
Clustering the recognized strange face data to obtain a plurality of cluster clusters, for example, it can be determined that the face data belonging to one cluster belongs to the same stranger. To further improve accuracy, face data far from the cluster center may be filtered out. The human face data which belong to the same stranger and are acquired by different acquisition equipment at different moments reflect the action track of the stranger. Optionally, for a plurality of face data of the same stranger, the unique virtual ID may be used to identify the identity information.
S103, constructing a graph database according to the face data of each stranger.
In an alternative embodiment, constructing a graph database from face data of strangers may include:
identifying each stranger as a first vertex, wherein the first vertex comprises identity information; identifying acquisition devices located at each acquisition location as second vertices, the second vertices including acquisition location information, the acquisition locations including at least an entrance of a region and an exit of the region; an 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, critical path intersections, and the like.
Referring to fig. 3, there is shown a graph database constructed from face data of a stranger with an ID of 320321. As shown in fig. 3, a thick solid circle at the center represents a first vertex corresponding to a stranger with an ID of 320321; thin solid line circles with peripheral labels of 0-4 respectively represent a second vertex corresponding to a collecting device (label 0) at a community entrance, a collecting device (label 1) at a community exit, a collecting device (label 2) at a building unit door entrance, a collecting device (label 3) at a building unit door exit and a collecting device (label 4) at a key road entrance, each edge between the first vertex and the second vertex corresponds to face data of a stranger with an ID of 320321, and the time of collecting the face data is recorded. Taking the graph database neo4j as an example, the statements are stored 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 dotted line will be generated in fig. 3 to indicate that a stranger with an ID of 320321 traveled 00 minutes and 59 seconds through the community portal at 12 months and 17 days 08 in 2019.
The relationship between the graph database vertex and the edge can visually record the association relationship between a stranger and different acquisition devices at different moments.
And S104, identifying abnormal behaviors of strangers according to the graph database.
Because the relationship between the graph database vertex and the edge records the incidence relationship between a stranger and different acquisition devices at different time, the abnormal behavior of the stranger can be identified by searching the graph database vertex and the edge relationship.
Specifically, if the number of edges between a certain stranger vertex and a collection device vertex at an area entrance in the graph database is greater than a preset frequency threshold, the behavior of the stranger is identified as a first-class abnormal behavior, and the first-class abnormal behavior is used for indicating that the stranger frequently enters the area. For example, the preset frequency threshold may be set to be N, where N is a positive integer, and a specific value of N may be determined according to statistics, and a larger N represents a higher entering frequency. The graph database can be searched in batch, the number n of edges between the top points of strangers and the top points of the acquisition equipment at the entrance of the community in a preset time period in the graph database is obtained, and the number n represents the number of times that the strangers enter the community in the preset time period; if N > N, the stranger frequently enters the community, and the suspicious behavior index is higher when the exceeding value is higher.
If β 2- β 1> β 1, where β 01 is the acquisition time represented by the edge between a certain stranger vertex and an acquisition device vertex at an area entrance in the graph database, β 22 is the acquisition time represented by the edge between the stranger vertex and the acquisition device vertex at the area exit in the graph database, α is a preset retention time threshold, the behavior of the stranger is recognized as a second type of abnormal behavior, which is used to indicate that the stranger stays in the area for a long time, where the unit of the preset retention time threshold α set may be seconds, for example, the edge between the stranger vertex and the acquisition device vertex at the community entrance within a specified time is retrieved, which indicates the time β 31 when the stranger enters the community, the edge between the stranger vertex and the acquisition device vertex at the community exit within a specified time is retrieved, which indicates the time β 2 when the stranger leaves the community, if β 2- β 1> α, where β 2- β 1 indicates that the retention time of the stranger in the community is higher, and the suspicious retention time is determined to be less.
If an edge exists between a top point and a second top point of a stranger in the graph database within a 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 come in or go out of a corresponding area within the abnormal time period. For example, a preset abnormal time period may be set to be 23:00-5:00, in the time period, the number S of edges between each stranger vertex and all the vertices of the collection devices in the batch search graph database is set, if S >0, it indicates that the stranger moves in the community in the abnormal time period, and the larger the value S is, the higher the suspicious index is.
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 the face data belonging to the same stranger, constructing a graph database according to the face data of each stranger, and vividly reflecting the association relationship between the strangers and different acquisition equipment at different moments by using the relationship between the top points and edges of the graph database; and finally, recognizing the abnormal behavior of the stranger according to the graph database, and quickly and effectively analyzing the behavior of the stranger based on the graph database, so that the behavior of the stranger is analyzed based on the collected face data, the abnormal behavior of the stranger is discovered in time, and the safety of the community can be improved.
On the basis of the previous embodiment, in order to timely handle the abnormal condition, if the abnormal behavior is recognized, the abnormal information is sent to related workers to perform early warning. For example, the abnormal information may be sent to the community security manager through a short message, a WeChat message, a platform message, or the like. The abnormal information may include picture information of strangers, abnormal behavior occurrence time information, and abnormal behavior reasons.
Fig. 4 is a schematic structural diagram of a behavior analysis apparatus according to an embodiment of the present invention. As shown in fig. 4, the behavior analysis device 40 provided in the present embodiment may include: an identification module 401, a clustering module 402, a construction module 403, and an analysis module 404.
The identification module 401 is configured to perform stranger face image identification on face data within 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;
a clustering module 402, configured to cluster the face images of the identified strangers to obtain face data of the same stranger;
a construction module 403, configured to construct a graph database according to the face data of each stranger;
an analysis module 404 for identifying abnormal behavior of strangers from the graph database.
The behavior analysis apparatus provided in this embodiment may be used to execute the technical solution of the method embodiment corresponding to fig. 1, and the implementation principle and the technical effect are similar, which are not described herein again.
In an embodiment, the recognition module 401 is configured to perform stranger face image recognition on face data acquired by at least one image acquisition device within a preset time period, and specifically may include:
matching the collected face data with a resident face database;
and if the matching fails, the face data is the face data of a stranger.
In one embodiment, the building module 403 is configured to build a graph database according to face data of each stranger, and specifically may include:
identifying each stranger as a first vertex, wherein the first vertex comprises identity information; identifying acquisition devices located at each acquisition location as second vertices, the second vertices including acquisition location information, the acquisition locations including at least an entrance of a region and an exit of the region; an 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 behavior of a stranger according to the graph database, and specifically may include:
if the number of edges between the top point of a certain stranger and the top point of the acquisition equipment at the entrance of the area in the graph database is larger than a preset frequency threshold, the behavior of the stranger is identified as a first-class abnormal behavior, and the first-class abnormal behavior is used for indicating that the stranger frequently enters the area.
In one embodiment, the analysis module 404 is configured to identify abnormal behavior of a stranger according to the graph database, and specifically may include:
β 2- β 1> α, wherein β 1 is the collection time represented by the edge between the top of a stranger and the top of a collection device at the entrance of an area in a graph database, β 2 is the collection time represented by the edge between the top of the stranger and the top of the collection device at the exit of the area in the graph database, α is a preset retention time threshold, the behavior of the stranger is recognized as a second type of abnormal behavior, and the second type of abnormal behavior is used for indicating that the stranger is retained in the area for a long time.
In one embodiment, the analysis module 404 is configured to identify abnormal behavior of a stranger according to the graph database, and specifically may include:
if an edge exists between a top point and a second top point of a stranger in the graph database within a 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 come in or go out of a corresponding area within 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 abnormal information to relevant workers if abnormal behavior is identified, where the abnormal information includes picture information of strangers, abnormal behavior occurrence time information, and abnormal behavior reasons.
Fig. 5 is a schematic diagram of a behavior analysis device according to an embodiment of the present invention, which is only illustrated in fig. 5, and the embodiment of 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. The bus 503 is used to realize connection between the elements.
The memory 501 stores a computer program, and when the computer program is executed by the processor 502, the computer program can implement any technical solution of the behavior analysis method provided by the method embodiment.
Wherein, the memory 501 and the processor 502 are electrically connected directly or indirectly to realize the data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines, such as bus 503. The memory 501 stores a computer program for implementing the behavior analysis method, which includes at least one software functional module that can 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, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 501 is used for storing programs, and the processor 502 executes the programs after receiving execution instructions. 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 having signal processing capabilities. The Processor 502 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and so on. The various methods, steps and logic blocks disclosed 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. The network-side device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a cloud consisting of a large number of computers or network servers based on cloud computing, wherein the cloud computing is one of distributed computing and is a super virtual computer consisting of a group of loosely coupled computers.
Reference is made herein to various exemplary embodiments. However, those skilled in the art will recognize that changes and modifications may be made to the exemplary embodiments without departing from the scope hereof. For example, the various operational steps, as well as the components used to perform the operational steps, may be implemented in differing ways depending upon the particular application or consideration of any number of cost functions associated with operation of the system (e.g., one or more steps may be deleted, modified or incorporated into other steps).
Additionally, as will be appreciated by one skilled in the art, the principles herein may be reflected in a computer program product on a computer readable storage medium, which is pre-loaded 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 for implementing 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 illustrated in various embodiments, many modifications of structure, arrangement, proportions, elements, materials, and components particularly adapted to specific environments and operative requirements may be employed 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, one 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 disclosure is to be considered in an illustrative and not a restrictive sense, 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. However, the benefits, advantages, solutions to problems, and any element(s) that may cause any element(s) to occur or become more pronounced are not to be construed as a critical, required, or essential feature or element of any or all the claims. As used herein, 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 "coupled," and any other variation thereof, as used herein, refers to a physical connection, an electrical connection, a magnetic connection, an optical connection, a communicative connection, a functional connection, and/or any other connection.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (10)
1. A method of behavioral analysis, comprising:
performing stranger 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.
2. The method of claim 1, wherein the stranger face image recognition of the face data acquired by at least one image acquisition device within a preset time period comprises:
matching the collected face data with a resident face database;
and if the matching fails, the face data is the face data of a stranger.
3. The method of claim 1, wherein constructing a graph database from face data of strangers comprises:
identifying each stranger as a first vertex, wherein the first vertex comprises identity information; identifying acquisition devices located at each acquisition location as second vertices, the second vertices including acquisition location information, the acquisition locations including at least an entrance of a region and an exit of the region; an edge between the first vertex and the second vertex corresponds to one piece of face data, and the edge comprises acquisition time information.
4. The method of claim 3, wherein identifying abnormal behavior of a stranger from the graph database comprises:
if the number of edges between the top point of a certain stranger and the top point of the acquisition equipment at the entrance of the area in the graph database is larger than a preset frequency threshold, the behavior of the stranger is identified as a first-class abnormal behavior, and the first-class abnormal behavior is used for indicating that the stranger frequently enters the area.
5. The method of claim 3, wherein identifying abnormal behavior of a stranger from the graph database comprises:
β 2- β 1> α, wherein β 1 is the collection time represented by the edge between the top of a stranger and the top of a collection device at the entrance of an area in a graph database, β 2 is the collection time represented by the edge between the top of the stranger and the top of the collection device at the exit of the area in the graph database, α is a preset retention time threshold, the behavior of the stranger is recognized as a second type of abnormal behavior, and the second type of abnormal behavior is used for indicating that the stranger is retained in the area for a long time.
6. The method of claim 3, wherein identifying abnormal behavior of a stranger from the graph database comprises:
if an edge exists between a top point of a stranger and the second top point in the graph database within a 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 come in or go out of a corresponding area within the abnormal time period.
7. The method of any one of claims 1-6, further comprising:
and if the abnormal behavior is identified, sending abnormal information to a related worker, wherein the abnormal information comprises picture information of strangers, abnormal behavior occurrence time information and abnormal behavior reasons.
8. A behavior analysis device, comprising:
the identification module is used for carrying out stranger face image identification 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;
the clustering module is used for clustering the face images of the identified strangers to acquire the 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.
9. A behavior analysis device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the behavior analysis method of any of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, are configured to implement the behavior analysis method of any one of claims 1-7.
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