CN114550076A - Method, device and equipment for monitoring area abnormal behaviors and storage medium - Google Patents
Method, device and equipment for monitoring area abnormal behaviors and storage medium Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a method for monitoring abnormal behaviors in a region, which comprises the following steps: classifying the monitoring images according to a preset scene and deleting an invalid scene to obtain a target scene monitoring image; when judging that an abnormal event exists in a target scene monitoring image by using a pre-trained abnormal event recognition model, marking the corresponding target scene monitoring image as an abnormal evidence obtaining picture, and extracting identification information and marking frame position information from the abnormal evidence obtaining picture; and deleting repeated abnormal events in the cache by using a preset rule according to the identification information and the position information of the marking frame to obtain reserved abnormal events and sending the reserved abnormal events to a preset handler. The invention also provides a device for monitoring the regional abnormal behavior, electronic equipment and a storage medium. The invention can improve the accuracy and efficiency of abnormal behavior analysis.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for monitoring regional abnormal behaviors, electronic equipment and a computer readable storage medium.
Background
With the development of projects such as smart cities, monitoring equipment in areas is more and more common at present, and people more and more commonly identify abnormal behaviors of videos captured by the monitoring equipment.
However, at present, when the video captured by the monitoring device is used for identifying abnormal behaviors, a manual identification mode is generally adopted. The manual mode has the following three main disadvantages: firstly, the video amount is huge, and a large amount of manpower is needed to carry out daily manual checking work; secondly, whether the abnormal behavior is effective can be confirmed by often needing playback, so that the working efficiency is low; thirdly, depending on the personal ability and experience of the related staff, the identification is inaccurate due to human factors.
In summary, the accuracy and efficiency of the current area monitoring for the abnormal behavior analysis are low.
Disclosure of Invention
The invention provides a method and a device for monitoring regional abnormal behaviors and a computer readable storage medium, and mainly aims to solve the problems of low accuracy and low efficiency of regional monitoring on abnormal behavior analysis.
In order to achieve the above object, the present invention provides a method for monitoring abnormal regional behaviors, which includes:
acquiring a real-time monitoring video stream, and deframing the video stream to obtain a monitoring image;
classifying the monitoring images according to a preset scene to obtain scene monitoring images;
identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image;
when judging that an abnormal event exists in the target scene monitoring image by utilizing a pre-trained abnormal event recognition model, marking the corresponding target scene monitoring image as an abnormal evidence obtaining picture, extracting identification information and marking frame position information of the abnormal event from the abnormal evidence obtaining picture, and storing the abnormal evidence obtaining picture, the identification information and the marking frame position information corresponding to the abnormal event into a preset cache;
according to the identification information and the position information of the marking frame, deleting the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the repeated abnormal event in the cache by using a preset rule to obtain the reserved abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event;
and storing the abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the reserved abnormal event into a preset abnormal library, and sending the abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event to a preset handler.
Optionally, the monitoring images are classified according to a preset scene to obtain a scene monitoring image;
graying and normalizing the monitoring image to obtain a standardized monitoring image;
performing convolution operation, maximum pooling and full-connection operation on the standardized monitoring image on a plurality of scales, and extracting the characteristics of the standardized monitoring image;
and classifying the features according to a preset scene through a pre-constructed classification model to obtain a scene monitoring image.
Optionally, the identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image includes:
extracting image characteristics of the scene monitoring image;
matching with the image characteristics by using preset invalid scene rules corresponding to different scene types to obtain matching similarity;
and according to the matching similarity, screening an invalid scene image from the scene monitoring images, and deleting the invalid scene image to obtain a target scene monitoring image.
Optionally, before the time when the pre-trained abnormal event recognition model is used to determine that the abnormal event exists in the target scene monitoring image, the method further includes:
extracting an initial characteristic diagram of the target scene monitoring image;
performing weight adjustment on the initial characteristic diagram by using a preset attention mechanism module to obtain an attention characteristic diagram;
performing convolution and average pooling operation on the attention feature map to obtain image features of the target scene monitoring image;
calculating the probability value of the image characteristics of the target scene monitoring image belonging to a preset abnormal event by using a preset activation function;
and judging whether the target scene monitoring image has an abnormal event or not according to the probability value.
Optionally, deleting the abnormal evidence-obtaining picture, the identification information and the position information of the mark frame corresponding to the repeated abnormal event in the cache by using a preset rule according to the identification information and the position information of the mark frame to obtain the reserved abnormal evidence-obtaining picture, the identification information and the position information of the mark frame corresponding to the abnormal event, including:
inquiring the cache according to the identification information, and judging whether a target abnormal event identical to the identification information exists in the cache or not;
when the target abnormal event which is the same as the identification information does not exist in the cache, reserving an abnormal evidence obtaining picture, the identification information and the position information of the marking frame which correspond to the abnormal event which corresponds to the identification information;
when a target abnormal event identical to the identification information exists in the cache, judging whether the abnormal event is repeated with the target abnormal event or not based on the frame-annotation position information and the frame-annotation position information corresponding to the target abnormal event;
when the abnormal event is repeated with the target abnormal event, deleting the abnormal evidence obtaining picture, the identification information and the position information of the mark frame corresponding to the repeated abnormal event in the cache;
and when the abnormal event is not repeated with the target abnormal event, reserving the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event.
Optionally, the querying the cache according to the identification information, and determining whether a target abnormal event that is the same as the identification information exists in the cache, where the method further includes:
calculating the similarity between the identification information and the identification information in the cache;
and judging whether a target abnormal event identical to the identification information exists in the cache or not according to the similarity.
Optionally, the determining whether the abnormal event is repeated with the target abnormal event based on the frame-annotation position information and the frame-annotation position information corresponding to the target abnormal event includes:
matching the frame-injection position information with the mark-injection frame position information corresponding to the target abnormal event to obtain the overlapping rate of the frame-injection position information and the mark-injection frame position information corresponding to the target abnormal event;
and judging whether the abnormal event is repeated with a target abnormal event or not according to the overlapping rate.
In order to solve the above problem, the present invention further provides a device for monitoring abnormal regional behaviors, which includes:
the monitoring image acquisition module is used for acquiring a real-time monitoring video stream and unframing the video stream to obtain a monitoring image;
the invalid scene deleting module is used for classifying the monitoring images according to a preset scene to obtain scene monitoring images; identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image;
the abnormal event judging module is used for marking the corresponding target scene monitoring image as an abnormal evidence obtaining picture when judging that the abnormal event exists in the target scene monitoring image by utilizing a pre-trained abnormal event recognition model, extracting identification information and marking frame position information of the abnormal event from the abnormal evidence obtaining picture, and storing the abnormal evidence obtaining picture, the identification information and the marking frame position information corresponding to the abnormal event into a preset cache;
the abnormal event duplicate removal module is used for deleting the abnormal evidence obtaining picture, the identification information and the position information of the mark frame corresponding to the repeated abnormal event in the cache by using a preset rule according to the identification information and the position information of the mark frame to obtain the reserved abnormal evidence obtaining picture, the identification information and the position information of the mark frame corresponding to the abnormal event;
and the abnormal event processing module is used for storing the reserved abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event into a preset abnormal library and sending the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event to a preset handler.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of monitoring regional abnormal behavior as described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the above-mentioned method for monitoring regional abnormal behavior.
According to the embodiment of the invention, the target scene monitoring image is obtained by classifying the monitoring images according to the preset scene and deleting the invalid scene, the classification aiming at different scenes is beneficial to improving the identification accuracy of abnormal behaviors, the invalid scene is deleted, the images for identifying abnormal events are reduced, and the abnormal event identification efficiency is improved; judging whether an abnormal event exists in the target scene monitoring image by using a pre-trained abnormal event recognition model, and improving the feature extraction capability of the abnormal event recognition model so as to improve the accuracy of abnormal event recognition; and deleting the abnormal evidence-taking picture, the identification information and the position information of the marking frame corresponding to the repeated abnormal event in the cache by using a preset rule, so that the quantity of the abnormal events is reduced, the storage pressure of the abnormal events is reduced, and the analysis efficiency of the abnormal behavior is improved. Therefore, the method, the device, the electronic equipment and the computer readable storage medium for monitoring the abnormal behaviors in the area can solve the problems of low accuracy and low efficiency of analyzing the abnormal behaviors by area monitoring.
Drawings
Fig. 1 is a schematic flow chart of a method for monitoring abnormal regional behaviors according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a detailed implementation of one step in the method for monitoring abnormal behavior of the area shown in FIG. 1;
FIG. 3 is a flowchart illustrating a detailed implementation of one step in the method for monitoring abnormal behavior of the area shown in FIG. 1;
FIG. 4 is a flowchart illustrating a detailed implementation of one step in the method for monitoring abnormal behavior of the area shown in FIG. 1;
fig. 5 is a functional block diagram of a device for monitoring abnormal regional behaviors according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device for implementing the method for monitoring abnormal regional behaviors according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a method for monitoring abnormal behaviors in a region. The execution subject of the regional abnormal behavior monitoring method includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server and a terminal. In other words, the regional abnormal behavior monitoring method may be executed by software or hardware installed in the terminal device or the server device, where the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a method for monitoring abnormal regional behaviors according to an embodiment of the present invention. In this embodiment, the method for monitoring abnormal behaviors in a region includes:
s1, acquiring a real-time monitoring video stream, and deframing the video stream to obtain a monitoring image;
in the embodiment of the invention, the monitoring video stream can be acquired by cameras, video recorders and other devices with monitoring and snapshot functions which are arranged in the region to be monitored, such as lamp posts on both sides of XX, XXXX ports, city X, small X key regions and the like.
In one embodiment of the present invention, currently commonly used deframing software may be utilized, such as: and unframing the video stream by using animation GIF (graphics interchange Format) making software, clipping and mapping and the like.
S2, classifying the monitoring images according to a preset scene to obtain a scene monitoring image;
in one embodiment of the present invention, the preset scenes include scenes such as disappearing XXX, horse X, society XXX, city X, and the like.
In the embodiment of the invention, a convolutional neural network with an image feature extraction function can be adopted to construct a classification model, and the classification model is utilized to classify the monitoring image. The convolutional neural network includes, but is not limited to, AlexNet network, ZFNet network, VGGNet network, ResNet50, google lenet, and the like.
In one embodiment of the invention, the classification model is constructed by a GoogLeNet network, and the GoogLeNet network can be composed of a convolutional layer, a pooling layer and a full-link layer.
In detail, referring to fig. 2, the S2 includes:
s21, graying and normalizing the monitoring image to obtain a standardized monitoring image;
according to the embodiment of the invention, the standardized monitoring images with the same size and the same gray value can be obtained by carrying out graying and normalization processing on the monitoring images. Wherein the preferred value of the gray value is 0-1. The standardized monitoring image can reduce a large amount of calculation in the convolution calculation process and improve the classification efficiency of the monitoring image.
S22, performing convolution operation, maximum pooling and full-connection operation on the standardized monitoring image on multiple scales, and extracting the characteristics of the standardized monitoring image;
in one embodiment of the invention, the features of the standardized monitoring image are extracted from a plurality of scales by utilizing 1 × 1 convolution, 3 × 3 convolution and 5 × 5 convolution, so that the obtained features are richer and more accurate in classification judgment.
And S23, classifying the features according to a preset scene through a pre-constructed classification model to obtain a scene monitoring image.
Because the monitoring images comprise a plurality of scenes, the abnormal behaviors of each scene are different, and the monitoring images are classified according to the preset scenes, so that the identification accuracy rate of the abnormal behaviors is improved.
S3, identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image;
the invalid scene in the embodiment of the invention comprises the following steps: scene monitoring images that do not contain disappearing XXX, do not contain street X, do not contain store X, do not contain a place to city X where XX is not allowed, etc.
In detail, referring to fig. 3, the S3 includes:
s31, extracting image features of the scene monitoring image;
s32, matching the image features by using preset invalid scene rules corresponding to different scene types to obtain matching similarity;
and S33, according to the matching similarity, screening out an invalid scene image from the scene monitoring images, and deleting the invalid scene image to obtain a target scene monitoring image.
In the embodiment of the present invention, the invalid scene rule is a rule corresponding to various invalid scenes, for example, image features such as a XXX elimination operation field and a XXX elimination operation field do not appear in the image features.
Specifically, in the embodiment of the present invention, when the matching similarity is greater than or equal to a preset first similarity threshold, the corresponding scene monitoring image is used as a target scene monitoring image to perform a retention operation; and when the matching similarity is smaller than the preset first similarity threshold, taking the corresponding scene monitoring image as an invalid scene monitoring image to execute deletion operation.
According to the embodiment of the invention, the invalid scene image is deleted to obtain the target scene monitoring image, so that the images for identifying the abnormal event can be reduced, and the efficiency for identifying the abnormal event is improved.
S4, judging whether an abnormal event exists in the target scene monitoring image by using a pre-trained abnormal event recognition model;
the abnormal events in the embodiment of the invention include, but are not limited to, occupation of vanishing XXX operation site, horse XXX, business XXX, city XXXXX.
In one embodiment of the present invention, the abnormal event identification model may be a model of convolution layer (convolution) between blocks of a densely connected convolutional neural network (DenseNet) plus an attention module, the attention module model including a channel attention module and a spatial attention module.
In detail, the determining whether an abnormal event exists in the target scene monitoring image by using a pre-trained abnormal event recognition model includes:
extracting an initial characteristic diagram of the target scene monitoring image;
performing weight adjustment on the initial characteristic diagram by using a preset attention mechanism module to obtain an attention characteristic diagram;
performing convolution and average pooling operation on the attention feature map to obtain image features of the target scene monitoring image;
calculating the probability value of the image characteristics of the target scene monitoring image belonging to a preset abnormal event by using a preset activation function;
and judging whether the target scene monitoring image has an abnormal event or not according to the probability value.
Specifically, when the probability value is greater than or equal to a preset probability threshold value, judging that an abnormal event exists in the target scene monitoring image; and when the probability value is smaller than the preset probability threshold value, judging that no abnormal event exists in the target scene monitoring image.
Preferably, in one embodiment of the present invention, the core size of the convolution layer of the abnormal event identification model is 7 × 7, and the step size is 2; the kernel size of the average pooling layer is 3 × 3 with a step size of 2. The arrangement can not merge the depth information early, and can also reduce the number of parameters of the network structure and enhance the robustness of the network structure.
According to the embodiment of the invention, the attention mechanism module is utilized to adjust and optimize the abnormal event identification model, so that the feature extraction capability of the abnormal event identification model is improved, and the accuracy of abnormal event identification is improved.
When no abnormal event exists in the target scene monitoring image, returning to the step of S1;
when an abnormal event exists in the target scene monitoring image, S5, marking the corresponding target scene monitoring image as an abnormal evidence obtaining picture, extracting identification information and position information of a label frame of the abnormal event from the abnormal evidence obtaining picture, and storing the abnormal evidence obtaining picture, the identification information and the position information of the label frame corresponding to the abnormal event in a preset cache;
in one embodiment of the present invention, the identification information of the abnormal event is used to identify a certain abnormal event at a certain preset point of a certain monitoring point location, and may be: the method comprises the steps of point location numbering, preset points and abnormal event numbering, wherein the point location numbering is used for identifying monitoring point locations; wherein the preset points are used to identify the monitoring area of monitoring point ; the exception number is used to identify an exception.
In one embodiment of the present invention, the position information of the mark frame identifies position information of the mark frame corresponding to a specific abnormal behavior of the abnormal forensics picture.
S6, according to the identification information and the position information of the marking frame, deleting the abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the repeated abnormal event in the cache by using a preset rule to obtain the reserved abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event;
in detail, referring to fig. 4, the S6 includes:
s61, inquiring the cache according to the identification information, and judging whether a target abnormal event identical to the identification information exists in the cache;
when the target abnormal event which is the same as the identification information does not exist in the cache, executing S62, and reserving an abnormal evidence-taking picture, the identification information and the position information of the marking frame which correspond to the abnormal event which corresponds to the identification information;
when the target abnormal event which is the same as the identification information exists in the cache, executing S63, and judging whether the abnormal event is repeated with the target abnormal event or not based on the frame-annotation position information and the frame-annotation position information corresponding to the target abnormal event;
when the abnormal event is repeated with the target abnormal event, executing S64, and deleting the abnormal evidence-taking picture, the identification information and the position information of the mark frame corresponding to the repeated abnormal event in the cache;
and when the abnormal event is not repeated with the target abnormal event, executing S65, and reserving the abnormal evidence-taking picture, the identification information and the position information of the marking frame corresponding to the abnormal event.
In detail, the querying the cache according to the identification information and determining whether a target abnormal event identical to the identification information exists in the cache includes:
calculating the similarity between the identification information and the identification information in the cache;
and judging whether a target abnormal event identical to the identification information exists in the cache or not according to the similarity.
Specifically, when the similarity of the identification information is greater than or equal to the preset second similarity threshold, it is determined that a target abnormal event identical to the identification information exists in the cache; and when the similarity of the identification information is smaller than the preset second similarity threshold, the target abnormal event which is the same as the identification information does not exist in the cache.
Further, the determining whether the abnormal event is repeated with the target abnormal event based on the frame-annotation position information and the frame-annotation position information corresponding to the target abnormal event includes:
matching the frame-injection position information with the mark-injection frame position information corresponding to the target abnormal event to obtain the overlapping rate of the frame-injection position information and the mark-injection frame position information corresponding to the target abnormal event;
and judging whether the abnormal event is repeated with a target abnormal event or not according to the overlapping rate.
Specifically, when the overlap ratio is greater than or equal to the preset overlap ratio threshold, it is determined that the abnormal event is repeated with a target abnormal event; and when the overlapping rate is smaller than the preset overlapping rate threshold value, judging that the abnormal event is not repeated with the target abnormal event.
The abnormal event is a continuous process, and according to the identification information and the position information of the marking frame, the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event repeatedly in the cache are deleted, so that the storage pressure of the abnormal event is reduced, the quantity of the abnormal event is reduced, and the analysis efficiency of the abnormal behavior is improved.
And S7, storing the reserved abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event into a preset abnormal library, and sending the abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event to a preset handler.
In the embodiment of the present invention, the administrator may be an administrator in the XX system of the intelligent XX or other relevant administrators.
In the embodiment of the invention, the reserved abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event are stored in the preset abnormal library, so that the abnormal event can be inquired, proved and analyzed subsequently.
In one implementation of the present invention, after the abnormal forensic picture, the identification information, and the position information of the label frame corresponding to the abnormal event are sent to a preset handler in S7, when a time difference between a sending time and a current time is greater than a first time threshold and the administrator is not handling the abnormal forensic picture, the identification information, and the position information of the label frame corresponding to the abnormal event are sent to the administrator again, the administrator is prompted to perform processing to prevent the administrator from missing the information, further, when the time difference between the sending time and the current time is greater than a second time threshold and the administrator is not handling the abnormal forensic picture, the identification information, and the position information of the label frame corresponding to the abnormal event are sent to other administrators to ensure that at least one administrator receives and processes the abnormal forensic picture, the identification information, and the position information of the label frame corresponding to the abnormal event, and corresponding measures can be taken for abnormal actions.
Fig. 5 is a functional block diagram of a device for monitoring abnormal regional behaviors according to an embodiment of the present invention.
The device 100 for monitoring abnormal behavior of an area according to the present invention may be installed in an electronic device. According to the implemented functions, the area abnormal behavior monitoring apparatus 100 may include a monitoring image obtaining module 101, an invalid scene deleting module 102, an abnormal event determining module 103, an abnormal event deduplication module 104, and an abnormal event processing module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the monitoring image obtaining module 101 is configured to obtain a real-time monitoring video stream, and perform deframing on the video stream to obtain a monitoring image;
the invalid scene deleting module 102 is configured to classify the monitoring images according to a preset scene to obtain scene monitoring images; identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image;
the abnormal event judgment module 103 is configured to, when judging that an abnormal event exists in the target scene monitoring image by using a pre-trained abnormal event recognition model, mark the corresponding target scene monitoring image as an abnormal evidence obtaining picture, extract identification information and mark frame position information of the abnormal event from the abnormal evidence obtaining picture, and store the abnormal evidence obtaining picture, the identification information and the mark frame position information corresponding to the abnormal event in a preset cache;
the abnormal event deduplication module 104 is configured to delete the abnormal forensics picture, the identification information, and the position information of the mark frame corresponding to the repeated abnormal event in the cache according to the identification information and the position information of the mark frame by using a preset rule, so as to obtain the reserved abnormal forensics picture, the identification information, and the position information of the mark frame corresponding to the abnormal event;
the abnormal event processing module 105 is configured to store the abnormal forensic picture, the identification information, and the position information of the mark frame corresponding to the reserved abnormal event into a preset abnormal library, and send the abnormal forensic picture, the identification information, and the position information of the mark frame corresponding to the abnormal event to a preset handler.
In detail, when the modules in the device 100 for monitoring area abnormal behavior according to the embodiment of the present invention are used, the same technical means as the method for monitoring area abnormal behavior described in fig. 1 to fig. 4 are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 6 is a schematic structural diagram of an electronic device for implementing a method for monitoring an abnormal behavior of a region according to an embodiment of the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a regional abnormal behavior monitoring program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a regional abnormal behavior monitoring program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various types of data, such as codes of a regional abnormal behavior monitoring program, but also temporarily store data that has been output or will be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit, such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 6 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 6 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The area abnormal behavior monitoring program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can realize that:
acquiring a real-time monitoring video stream, and deframing the video stream to obtain a monitoring image;
classifying the monitoring images according to a preset scene to obtain scene monitoring images;
identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image;
when judging that an abnormal event exists in the target scene monitoring image by utilizing a pre-trained abnormal event recognition model, marking the corresponding target scene monitoring image as an abnormal evidence obtaining picture, extracting identification information and marking frame position information of the abnormal event from the abnormal evidence obtaining picture, and storing the abnormal evidence obtaining picture, the identification information and the marking frame position information corresponding to the abnormal event into a preset cache;
according to the identification information and the position information of the marking frame, deleting the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the repeated abnormal event in the cache by using a preset rule to obtain the reserved abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event;
and storing the abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the reserved abnormal event into a preset abnormal library, and sending the abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event to a preset handler.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring a real-time monitoring video stream, and deframing the video stream to obtain a monitoring image;
classifying the monitoring images according to a preset scene to obtain scene monitoring images;
identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image;
when judging that an abnormal event exists in the target scene monitoring image by utilizing a pre-trained abnormal event recognition model, marking the corresponding target scene monitoring image as an abnormal evidence obtaining picture, extracting identification information and marking frame position information of the abnormal event from the abnormal evidence obtaining picture, and storing the abnormal evidence obtaining picture, the identification information and the marking frame position information corresponding to the abnormal event into a preset cache;
according to the identification information and the position information of the marking frame, deleting the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the repeated abnormal event in the cache by using a preset rule to obtain the reserved abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event;
and storing the abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the reserved abnormal event into a preset abnormal library, and sending the abnormal evidence-obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event to a preset handler.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for monitoring regional abnormal behaviors, which is characterized by comprising the following steps:
acquiring a real-time monitoring video stream, and deframing the video stream to obtain a monitoring image;
classifying the monitoring images according to a preset scene to obtain scene monitoring images;
identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image;
when judging that an abnormal event exists in the target scene monitoring image by utilizing a pre-trained abnormal event recognition model, marking the corresponding target scene monitoring image as an abnormal evidence obtaining picture, extracting identification information and marking frame position information of the abnormal event from the abnormal evidence obtaining picture, and storing the abnormal evidence obtaining picture, the identification information and the marking frame position information corresponding to the abnormal event into a preset cache;
according to the identification information and the position information of the marking frame, deleting the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the repeated abnormal event in the cache by using a preset rule to obtain the reserved abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event;
and storing the abnormal evidence obtaining picture, the identification information and the position information of the mark frame corresponding to the reserved abnormal event into a preset abnormal library, and sending the abnormal evidence obtaining picture, the identification information and the position information of the mark frame corresponding to the abnormal event to a preset processor.
2. The method for monitoring the regional abnormal behavior according to claim 1, wherein the monitoring images are classified according to preset scenes to obtain scene monitoring images;
graying and normalizing the monitoring image to obtain a standardized monitoring image;
performing convolution operation, maximum pooling and full-connection operation on the standardized monitoring image on a plurality of scales, and extracting the characteristics of the standardized monitoring image;
and classifying the features according to a preset scene through a pre-constructed classification model to obtain a scene monitoring image.
3. The method for monitoring the abnormal behavior of the area according to claim 1, wherein the identifying an invalid scene image from the scene monitoring images and deleting the invalid scene image to obtain a target scene monitoring image comprises:
extracting image characteristics of the scene monitoring image;
matching with the image characteristics by using preset invalid scene rules corresponding to different scene types to obtain matching similarity;
and screening an invalid scene image from the scene monitoring images according to the matching similarity, and deleting the invalid scene image to obtain a target scene monitoring image.
4. The method for monitoring the abnormal behavior of the area according to claim 1, wherein the determining the abnormal event in the target scene monitoring image by using the pre-trained abnormal event recognition model comprises:
extracting an initial characteristic diagram of the target scene monitoring image;
performing weight adjustment on the initial characteristic diagram by using a preset attention mechanism module to obtain an attention characteristic diagram;
performing convolution and average pooling operation on the attention feature map to obtain image features of the target scene monitoring image;
calculating the probability value of the image characteristics of the target scene monitoring image belonging to a preset abnormal event by using a preset activation function;
and judging whether the target scene monitoring image has an abnormal event or not according to the probability value.
5. The method for monitoring the abnormal behavior of the area according to claim 1, wherein the deleting, according to the identification information and the position information of the mark frame, the abnormal forensics picture, the identification information and the position information of the mark frame corresponding to the repeated abnormal event in the cache by using a preset rule to obtain the reserved abnormal forensics picture, the identification information and the position information of the mark frame corresponding to the abnormal event comprises:
inquiring the cache according to the identification information, and judging whether a target abnormal event identical to the identification information exists in the cache or not;
when the target abnormal event which is the same as the identification information does not exist in the cache, reserving an abnormal evidence obtaining picture, the identification information and the position information of the marking frame which correspond to the abnormal event which corresponds to the identification information;
when a target abnormal event identical to the identification information exists in the cache, judging whether the abnormal event is repeated with the target abnormal event or not based on the frame-annotation position information and the frame-annotation position information corresponding to the target abnormal event;
when the abnormal event is repeated with the target abnormal event, deleting the abnormal evidence obtaining picture, the identification information and the position information of the mark frame corresponding to the repeated abnormal event in the cache;
and when the abnormal event is not repeated with the target abnormal event, reserving the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event.
6. The method for monitoring regional abnormal behavior as claimed in claim 5, wherein said querying the cache according to the identification information and determining whether the cache has a target abnormal event identical to the identification information comprises:
calculating the similarity between the identification information and the identification information in the cache;
and judging whether a target abnormal event identical to the identification information exists in the cache or not according to the similarity.
7. The method for monitoring abnormal regional behaviors as claimed in claim 5, wherein said determining whether the abnormal event is repeated with the target abnormal event based on the frame-annotation position information and the frame-annotation position information corresponding to the target abnormal event comprises:
matching the frame-injection position information with the mark-injection frame position information corresponding to the target abnormal event to obtain the overlapping rate of the frame-injection position information and the mark-injection frame position information corresponding to the target abnormal event;
and judging whether the abnormal event is repeated with a target abnormal event or not according to the overlapping rate.
8. An apparatus for monitoring regional abnormal behavior, the apparatus comprising:
the monitoring image acquisition module is used for acquiring a real-time monitoring video stream and unframing the video stream to obtain a monitoring image;
the invalid scene deleting module is used for classifying the monitoring images according to a preset scene to obtain scene monitoring images; identifying an invalid scene image from the scene monitoring image, and deleting the invalid scene image to obtain a target scene monitoring image;
the abnormal event judging module is used for marking the corresponding target scene monitoring image as an abnormal evidence obtaining picture when judging that the abnormal event exists in the target scene monitoring image by utilizing a pre-trained abnormal event recognition model, extracting the identification information and the position information of the marking frame of the abnormal event from the abnormal evidence obtaining picture, and storing the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event into a preset cache;
the abnormal event duplicate removal module is used for deleting the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the repeated abnormal event in the cache by using a preset rule according to the identification information and the position information of the marking frame to obtain the reserved abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event;
and the abnormal event processing module is used for storing the reserved abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event into a preset abnormal library and sending the abnormal evidence obtaining picture, the identification information and the position information of the marking frame corresponding to the abnormal event to a preset handler.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of regional abnormal behavior monitoring of any of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the regional abnormal behavior monitoring method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116797429A (en) * | 2023-05-31 | 2023-09-22 | 北京瑞泰兴成工程技术有限公司 | Comprehensive security management method, platform, equipment and computer readable storage medium |
| CN116958707A (en) * | 2023-08-18 | 2023-10-27 | 武汉市万睿数字运营有限公司 | Image classification method, device and related medium based on spherical machine monitoring equipment |
| CN117575543A (en) * | 2024-01-15 | 2024-02-20 | 西安卓越软件开发有限公司 | Intelligent property management method and device |
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2021
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116797429A (en) * | 2023-05-31 | 2023-09-22 | 北京瑞泰兴成工程技术有限公司 | Comprehensive security management method, platform, equipment and computer readable storage medium |
| CN116958707A (en) * | 2023-08-18 | 2023-10-27 | 武汉市万睿数字运营有限公司 | Image classification method, device and related medium based on spherical machine monitoring equipment |
| CN116958707B (en) * | 2023-08-18 | 2024-04-23 | 武汉市万睿数字运营有限公司 | Image classification method, device and related medium based on spherical machine monitoring equipment |
| CN117575543A (en) * | 2024-01-15 | 2024-02-20 | 西安卓越软件开发有限公司 | Intelligent property management method and device |
| CN117575543B (en) * | 2024-01-15 | 2024-04-30 | 西安卓越软件开发有限公司 | Intelligent property management method and device |
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