JP2023527100A - Event detection method, device, electronic device, readable storage medium, and computer program - Google Patents

Abstract

The present disclosure relates to the field of artificial intelligence technology, specifically to the fields of computer vision and deep learning technology, and discloses an event detection method applicable to smart city scenes. The event detection method includes steps of acquiring image data, recognizing an urban scene in the image data, determining a detection target and a detection target event corresponding to the urban scene, and detecting a detection target in the image data. detecting and obtaining a detection state of the detection target event; and obtaining an event detection result of the image data based on the detection state of the detection target event. The present disclosure can improve the accuracy and efficiency of event detection.

Description

The present disclosure is a Chinese patent whose filing date is April 28, 2021, application number is 202110466781.3, and title is "Event detection method, device, electronic device, and readable storage medium" Claim priority of the application.
The present disclosure relates to the field of artificial intelligence technology, specifically to the field of computer vision and deep learning technology, and is applicable to smart city scenes. Event detection methods, apparatus, electronics, and readable storage media are provided.

Currently, according to the planning structure of the city management, each location in the city is equipped with a large number of video devices to monitor. By using these large amounts of data, they all play a positive role in the standardization of urban management and the intelligentization of urban grids. However, the prior art has technical problems such as poor stability and low accuracy of detection results when detecting events using surveillance video stream data.

The present disclosure provides event detection methods, devices, electronic devices, and readable storage media that are used to improve the accuracy and efficiency of event detection.

According to a first aspect of the present disclosure, there is provided an event detection method comprising the steps of obtaining image data, recognizing an urban scene in said image data, detecting targets to be detected and events to be detected corresponding to said urban scene, a detection target in the image data to obtain a detection state of the detection target event; and an event detection result of the image data based on the detection state of the detection target event. and obtaining.

According to a second aspect of the present disclosure, an event detection device is provided, an acquisition unit for acquiring image data, recognizing an urban scene in the image data, a target to be detected corresponding to the urban scene, and detecting a determination unit for determining a target event; a detection unit for detecting a detection target in the image data to obtain a detection state of the detection target event; and based on the detection state of the detection target event, a processing unit for obtaining an event detection result of the image data.

According to a third aspect of the present disclosure, there is provided an electronic apparatus comprising at least one processor and memory communicatively coupled to said at least one processor, wherein said memory is executable by said at least one processor are stored such that when the instructions are executed by the at least one processor, the at least one processor causes the above method to be performed.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium having computer instructions stored thereon, said computer instructions causing said computer to perform the above method.

According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, implementing the above method when said computer program is executed by a processor.

As can be seen from the above technical solution, the present disclosure firstly recognizes the urban scene in the acquired image data, then determines the target to be detected and the event to be detected corresponding to the urban scene, and finally: To detect a detection target in the image data, obtain an event detection result of the image data based on the obtained detection state of the detection target event, and correspond different city scenes with different detection target and detection target event, The present disclosure can improve the accuracy and efficiency of event detection.

It should be understood that nothing described herein is intended to identify key or critical features of embodiments of the disclosure, nor is it used to limit the scope of the disclosure. Other features of the present disclosure can be readily understood through the following specification.

The drawings are for a better understanding of the disclosure and do not limit the disclosure.
1 is a schematic diagram according to a first embodiment of the present disclosure; FIG. FIG. 4 is a schematic diagram according to a second embodiment of the present disclosure; FIG. 4 is a schematic diagram according to a third embodiment of the present disclosure; FIG. 4 is a schematic diagram according to a fourth embodiment of the present disclosure; 1 is a block diagram of an electronic device for implementing an event detection method according to an embodiment of the present disclosure; FIG.

Exemplary embodiments of the present disclosure will now be described with reference to the drawings. Various details of the embodiments of the disclosure are included for ease of understanding and should be considered as exemplary only. Accordingly, those skilled in the art should appreciate that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the disclosure. Similarly, for the sake of clarity, descriptions of well-known functions and constructions are omitted in the following description.

FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure. As shown in FIG. 1, the event detection method of this embodiment can specifically include the following steps:
S101, obtaining image data, recognizing a city scene in the image data;
S102, determining a target to be detected and an event to be detected corresponding to the city scene;
S103, detecting a target to be detected in the image data to obtain a detection state of the event to be detected;
S104, obtaining an event detection result of the image data based on the detection state of the detection target event.

The event detection method of this embodiment first recognizes an urban scene in acquired image data, then determines a target to be detected and an event to be detected corresponding to the urban scene, and finally detects in the image data. In order to detect a target and obtain an event detection result of image data based on the obtained detection state of the detection target event, and to correspond different city scenes to different detection target and detection target event, the present embodiment is: , the accuracy and efficiency of event detection can be improved.

The image data acquired by executing S101 in this embodiment may be extracted and acquired from video stream data captured in real time by a camera in a surveillance area of a city. The data includes at least one city image, and the included city image can correspond to at least one within an urban scene such as a street, a square, a school, or the like.

When performing S101 to acquire image data in this embodiment, an optional implementation that can be adopted is to acquire city video stream data, and different video stream data correspond to different city scenes and are acquired. At least one keyframe image is extracted as image data from the video stream data.

After acquiring the image data by executing S101 in this embodiment, the city scene of the acquired image data is recognized. Under normal circumstances, the city scene recognized in this embodiment is only one of streets, squares, schools, etc., but there are cases where multiple city scenes are obtained by recognizing the image data.

When executing S101 in this embodiment to recognize the city scene of the image data, the selectable implementation method that can be adopted is to input the acquired image data into the first recognition model, and the output of the first recognition model Based on the result, the city scene of the image data is acquired, and the first recognition model of the present embodiment is pre-trained and acquired, and can recognize the city scene corresponding to the image data.

In this embodiment, when recognizing an urban scene in image data by executing S101, a similarity between the image data and a different reference image is calculated to determine the reference image that is most similar to the image data. The city scene can also be the city scene of the acquired image data.

In this embodiment, after recognizing the city scene of the acquired image data by executing S101, the detection object target and the detection object event corresponding to the recognized city scene are determined by executing S102. Among them, the detection target determined by executing S102 in this embodiment may be one or more. The determined detection target event may be one or plural.

Different city scenes in this embodiment correspond to different detection targets and different detection events, and the method of distinguishing the detection objects corresponding to the city scenes achieves the purpose of detecting only specific detection objects in the image data. can effectively avoid detection blindness and improve the efficiency of event detection.

For example, if the city scene is a market, the detectable targets corresponding to the market can include people, booths, tables and chairs, billboards, garbage, and puddles, etc., and the detectable events corresponding to the market are the rules It can include violation management events, puddle events, etc. If the urban scene is a road, the detection targets corresponding to the road can include vehicles, motorcycles, rickshaws, road signs, green areas, puddles, etc. , the detected events corresponding to roads can include illegal parking events, road occupation events, puddle events, and so on.

In this embodiment, when determining the detection target and the detection target event corresponding to the city scene by executing S102, the determination can be made based on a preset correspondence relationship, and the preset correspondence relationship is as follows: It includes each city scene, and the target to be detected and the event to be detected corresponding to each city scene.

In this embodiment, after S102 is executed to determine the detection target and the detection target event corresponding to the city scene, S103 is executed to detect the detection target in the image data and determine the detection state of the detection target event. get. Among them, the detection state of the detection target event acquired by executing S103 of the present embodiment is one of the normal state and the abnormal state.

It can be understood that when S102 is executed in this embodiment to determine a plurality of detection target events, S103 in this embodiment can be executed to acquire the detection state corresponding to each detection target event.

Specifically, in this embodiment, when executing S103 to detect the detection target in the image data and acquire the detection state of the detection target event, the selectable realization method that can be adopted is: is input to the second recognition model, and the detection state of the detection target event is acquired based on the output result of the second recognition model. It is obtained through training, and based on the image data and the detection target, it is possible to obtain the detection state of each detection target event corresponding to the image data.

Further, in this embodiment, when executing S103 to detect the detection target in the image data and acquire the detection state of the detection target event, it is determined whether or not the detection result of the detection target exists in the image data. For example, if a puddle exists in the corresponding road image data, it is determined that the puddle event is in an abnormal state.

In this embodiment, after acquiring the detection state of the detection target event by executing S103, the event detection result of the image data is acquired based on the detection state of the detection target event acquired by executing S104.

When executing S104 in the present embodiment, the directly acquired detection state of the detection target event can be used as the event detection result of the image data.

Since there is a possibility that an error exists in the detection state of the detection target event acquired when executing S103 in this embodiment, in order to improve the accuracy of the acquired event detection result, S104 is executed in this embodiment. When executing to obtain the event detection result of the image data based on the detection state of the detectable event, a selectable implementation that can be adopted is to provide the obtained image data and the event state of the obtained detectable event to the user. , and the event detection result of the image data is acquired based on the confirmation result returned from the user.

In other words, in this embodiment, by checking the event status of the detection target event acquired by the user, erroneous event statuses of the detection target event are eliminated as much as possible, and the accuracy of the acquired event detection result is further improved. can also In this embodiment, confirmation results returned from the user can also be used to optimize the second recognition model, i.e., the image data, the targets to be detected in the image data, and the confirmation results are used as training data, and the second The recognition model is continuously updated so that the second recognition model can output a more accurate detection state of the detectable event.

In addition, after acquiring the event detection result of the acquired image data by executing S104 in the present embodiment, the following content may be further included, and the acquired event detection result may include a detection target event in an abnormal state. If it is determined that there is a detection target event in an abnormal state in the event detection result, alarm information is issued. The location information of the detected event in the state is included to improve the accuracy of the alarm and the processing efficiency of the abnormal event.

It can be understood that in this embodiment, alarm information is issued to relevant department personnel so that they can quickly deal with abnormal events occurring in the city.

FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure. FIG. 2 shows an event detection system 200, which includes a basic configuration module, a video playback storage module, a video analysis module, an alarm information storage module, and an information processing module. Among them, the basic configuration module is used to flexibly configure the processing rules of different video data streams according to the user's needs, and the video data streams can be processed in time-sharing or batch processing. the video playback and storage module is used to complete the real-time playback and storage of the video data stream, and to support calling and browsing of the video data stream; and the video analysis module is the event detection and abnormal event The alarm information storage module is used to implement alarm information classification search and inquiry display, and the information processing module is used to implement abnormal event processing.

FIG. 3 is a schematic diagram according to a third embodiment of the present disclosure; FIG. 3 shows the processing flow chart of the information processing unit, after inputting the video data stream into the video analysis module, the video analysis module performs event detection, and the video analysis module generates alarm information after detecting an abnormal event. , report to the information processing unit, the information processing unit sends the alarm information to the person in the relevant part for confirmation, and if the abnormal event detects an error, the confirmation result is returned to the video analysis module, and the abnormal event needs to be processed. If not, terminate the process, if the abnormal event needs to be processed, transmit the processing, and if it determines that the processing has already completed, check to determine if the abnormal event has already completed processing and if unchecked, move to Execute Propagating Actions.

FIG. 4 is a schematic diagram according to a fourth embodiment of the present disclosure; As shown in FIG. 4, the event detection device 400 of this embodiment includes an acquisition unit 401, a determination unit 402, a detection unit 403 and a processing unit 404,
the acquisition unit 401 is used to acquire image data and recognize an urban scene in said image data;
a determining unit 402 is used to determine a detectable target and a detectable event corresponding to the urban scene;
the detection unit 403 is used to detect a detection target in the image data to obtain a detection state of the detection target event;
A processing unit 404 is used to obtain an event detection result of the image data based on the detection state of the detection target event.

The image data acquired by the acquisition unit 401 may be extracted and acquired from the video stream data captured in real time by the camera in a surveillance area of the city, and the acquired image data may include at least one and the included city images may correspond to at least one in an urban scene, such as roads, squares, schools, and the like.

The acquisition unit 401 acquires the image data, an optional implementation that can be adopted is to acquire the video stream data of the city, and extract at least one key frame image from the acquired video stream data as image data.

After acquiring the image data, the acquisition unit 401 recognizes the urban scene in the acquired image data. Under normal circumstances, the city scene recognized by the acquisition unit 401 is only one of roads, squares, schools, etc., but multiple city scenes may be acquired by recognizing the image data.

The acquisition unit 401, when recognizing the urban scene in the image data, an optional implementation that can be adopted is to input the acquired image data into the first recognition model, and based on the output result of the first recognition model, Get the city scene of the image data.

In addition, when recognizing the city scene of the image data, the acquisition unit 401 can calculate the similarity between the image data and different reference images to determine the city scene corresponding to the reference image that is most similar to the image data, It can also be a city scene of the acquired image data.

In this embodiment, after recognizing the city scene of the image data acquired by the acquisition unit 401 , the detection target and the detection target event corresponding to the recognized city scene are determined by the determination unit 402 . Wherein, the detection target determined by the determination unit 402 may be one or more. The determined detection target event may be one or plural.

The determination unit 402 can determine the detection target and the detection target event corresponding to the city scene based on a preset correspondence relationship, and the preset correspondence relationship is determined for each city scene. , and detectable targets and detectable events corresponding to each city scene.

In this embodiment, after the determination unit 402 determines the detection target and the detection target event corresponding to the city scene, the detection unit 403 detects the detection target in the image data to obtain the detection state of the detection target event. . Wherein, the detection status of the detection target event obtained by the detection unit 403 is one of normal status and abnormal status.

It can be appreciated that if the determining unit 402 determines multiple detectable events, the detecting unit 403 can respectively obtain the detection status corresponding to each detectable event.

Specifically, when the detection unit 403 detects the detection target in the image data and obtains the detection status of the detection target event, the selectable implementation that can be adopted is determined as the obtained image data. The target to be detected is input to the second recognition model, and the detection state of the event to be detected is obtained based on the output result of the second recognition model.

Further, when the detection unit 403 detects the detection target in the image data and acquires the detection state of the detection target event, the detection unit 403 detects the detection target based on whether or not the detection result of the detection target exists in the image data. The detection status of the event can also be obtained, for example, if there is a puddle in the corresponding road image data, determine that the puddle event is in an abnormal state.

In this embodiment, after acquiring the detection state of the detection target event from the detection unit 403 , the event detection result of the image data is acquired based on the detection state of the detection target event acquired from the processing unit 404 .

The processing unit 404 can use the detection state of the detection target event directly acquired by the detection unit 403 as the event detection result of the image data.

Since there may be an error in the detection state of the detection target event acquired by the detection unit 403, the processing unit 404 corrects the detection state of the detection target event in order to improve the accuracy of the acquired event detection result. , when obtaining the event detection result of the image data, an optional implementation method that can be adopted is to send the event status of the obtained image data and the obtained detection target event to the user, so that the user can detect the detection target event is used to confirm the event state of the image data, and based on the confirmation result returned from the user, the event detection result of the image data is acquired.

That is, by checking the event status of the detection target event acquired by the user by the processing unit 404, the error event status of the detection target event is eliminated as much as possible, and the accuracy of the acquired event detection result is further improved. can also The verification results returned from the user by the processing unit 404 can also be used to optimize the second recognition model, i.e., the image data, the targets to be detected in the image data, and the verification results as training data, and the second The recognition model is continuously updated so that the second recognition model can output a more accurate detection state of the detectable event.

In addition, the event detection device 400 of the present embodiment may further include an alarm unit 405, and after obtaining the event detection result of the obtained image data, the alarm unit 405 detects an abnormality in the obtained event detection result. It is used to determine whether or not a detection target event in a state exists, and if it is determined that a detection target event in an abnormal state exists in the event detection result, and is used to issue alarm information. includes the abnormal state of the detection target event and the location information of the detection target event in the abnormal state.

According to embodiments of the disclosure, the disclosure further provides an electronic device, a readable storage medium, and a computer program product.

FIG. 5 is a block diagram of electronics for an event detection method according to an embodiment of the present disclosure. Electronic equipment is intended to represent various forms of digital computers such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, large scale computers, and other suitable computers. Electronics can also represent various forms of mobile devices such as personal digital assistants, cell phones, smart phones, wearable devices, and other similar computing devices. The components, their connections and relationships, and their functionality illustrated herein are merely examples and are not intended to limit the description and/or required implementation of the disclosure herein.

As shown in FIG. 5, the device 500 includes a computing unit 501 which is loaded into random access memory (RAM) 503 from a computer program stored in read only memory (ROM) 502 or from storage unit 508 . Various suitable operations and processes can be performed based on a computer program. RAM 503 can also store various programs and data necessary for device 500 to operate. Computing unit 501 , ROM 502 and RAM 503 are connected to each other via bus 504 . An input/output (I/O) interface 505 is also connected to bus 504 .

A number of components within the device 500 are connected to an I/O interface 505, including input units 506 such as keyboards, mice, etc., output units 507 such as various types of displays, speakers, etc., and storage units such as discs, optical discs, etc. It includes a unit 508 and a communication unit 509 such as a network card, modem, wireless communication transceiver. Communication unit 509 enables device 500 to exchange information/data with other devices via computer networks, such as the Internet, and/or various telecommunications networks.

Computing unit 501 is a general-purpose and/or special-purpose processing component with various processing and computing capabilities. Some examples of computing unit 501 include central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, computing units that run various machine learning model algorithms, digital Including, but not limited to, signal processors (DSPs), and any suitable processors, controllers, microcontrollers, and the like. Computing unit 501 performs the various methods and processes described above, such as event detection methods. For example, in some implementations, an event detection method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508 .

In some embodiments, part or all of the computer program is loaded and/or installed on device 500 via ROM 502 and/or communication unit 509 . When the computer program is loaded into RAM 503 and executed by computing unit 501, it can perform one or more steps of the event detection method described above. Alternatively, in other embodiments, computing unit 501 may be configured to perform event detection methods via any other suitable manner (eg, by firmware).

Various implementations of the systems and techniques described herein include digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs). ), system-on-chip system (SOC), loading programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include being embodied in one or more computer programs, which are executed and executed in a programmable system including at least one programmable processor. /or may be interpreted, the programmable processor may be an application-specific or general-purpose programmable processor, receives data and instructions from a storage system, at least one input device, and at least one output device; Data and instructions can be transmitted to the storage system, the at least one input device, and the at least one output device.

Program code to implement the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes may be stored in a general purpose computer, special purpose computer, or other programmable computer such that when the program code is executed by a processor or controller, the functions/acts specified in the flowcharts and/or block diagrams are performed. It can be provided in a processor or controller of a data processing device. Program code may be executed entirely on a machine, partially on a machine, partially on a machine as a separate software package, partially on a remote machine, or entirely on a machine. It can also be run on a remote machine or server.

In the context of this disclosure, a machine-readable medium is a tangible medium capable of containing or storing a program for use with, or in combination with, an instruction execution system, device, or apparatus. It may be a medium. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or instruments, or any suitable combination of the above. More specific examples of machine-readable storage media are electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only Including memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the above.

To provide interaction with a user, the systems and techniques described herein can be implemented on a computer, which includes a display device (e.g., CRT) for displaying information to the user. ) or LCD (liquid crystal display) monitor), and a keyboard and pointing device (e.g., mouse or trackball) through which a user can provide input to the computer. Other types of devices can also be used to provide interaction with a user, for example, the feedback provided to the user can be any form of sensing feedback (e.g., visual, auditory, or tactile feedback). ) and can receive input from the user in any form (including acoustic, speech, and tactile input).

The systems and techniques described herein may be computing systems that include back-end components (e.g., data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include front-end components. A system (e.g., a user computer having a graphical user interface or web browser, through which the user interacts with implementations of the systems and techniques described herein), or such a back-end component , middleware components, and front-end components in any combination. The components of the system can be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. A client-server relationship is created by computer programs running on corresponding computers and having a client-server relationship to each other. The server may be a cloud server, also called cloud computing or cloud host, which is one of the host products in the cloud computing service system, and is similar to the traditional physical host and VPS service (“Virtual Private Server” or “ abbreviated as "VPS") to solve the deficiencies of high management difficulty and weak business expandability. The server may be a server of a distributed system or a server combining blockchains.

It should be appreciated that steps may be reordered, added, or deleted using the various forms of flow shown above. For example, each step described in the present disclosure may be performed in parallel, sequentially, or in a different order, but the techniques disclosed in the present disclosure The scheme is not limited herein so long as it can achieve the desired result.

The above specific implementation manners do not constitute a limitation of the protection scope of this disclosure. Those skilled in the art can make various modifications, combinations, subcombinations, and substitutions based on design requirements and other factors. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this disclosure shall all fall within the protection scope of this disclosure.

Claims (15)

An event detection method comprising:
obtaining image data and recognizing an urban scene in said image data;
determining a detectable target and a detectable event corresponding to the urban scene;
detecting a detection target in the image data to obtain a detection state of the detection target event;
obtaining an event detection result of the image data based on the detection state of the detection target event;
Event detection method. The step of obtaining the image data includes:
obtaining city video stream data;
extracting at least one keyframe image from said video stream data as said image data;
The event detection method according to claim 1. determining the detectable target and the detectable event corresponding to the city scene,
determining the detection target and the detection target event corresponding to the city scene based on a preset correspondence relationship;
The event detection method according to claim 1. The step of detecting the detection target in the image data and obtaining the detection state of the detection target event includes:
inputting the image data and the detection target to a second recognition model, and acquiring the detection state of the detection target event based on the output result of the second recognition model;
The event detection method according to claim 1. The step of acquiring the event detection result of the image data based on the detection state of the detection target event,
transmitting the image data and the event state of the detection target event to a user;
obtaining the event detection result for the image data based on confirmation results returned from the user;
The event detection method according to claim 1. determining, after obtaining the event detection result of the image data, whether or not the event detection result includes a detection target event in an abnormal state;
and issuing alarm information when it is determined that the detection target event in the abnormal state exists in the event detection result.
The event detection method according to claim 1. An event detection device,
an acquisition unit for acquiring image data and recognizing an urban scene in said image data;
a determining unit for determining detectable targets and detectable events corresponding to the urban scene;
a detection unit that detects a detection target in the image data and acquires a detection state of the detection target event;
a processing unit that obtains an event detection result of the image data based on the detection state of the detection target event;
Event detector. When the acquisition unit acquires the image data,
Get city video stream data,
extracting at least one keyframe image from the video stream data as the image data;
The event detection device according to claim 7. When the determining unit determines the detectable target and the detectable event corresponding to the urban scene,
determining a target to be detected and an event to be detected corresponding to the city scene based on a preset correspondence relationship;
The event detection device according to claim 7. When the detection unit detects the detection target in the image data and acquires the detection state of the detection target event,
inputting the image data and the detection target to a second recognition model, and acquiring the detection state of the detection target event based on the output result of the second recognition model;
The event detection device according to claim 7. When the processing unit acquires an event detection result of the image data based on the detection state of the detection target event,
transmitting the image data and the event state of the detection target event to a user;
obtaining the event detection result for the image data based on confirmation results returned from the user;
The event detection device according to claim 7. further comprising an alarm unit;
The alarm unit
After the processing unit obtains the event detection result of the image data, it is determined whether the event detection result includes a detection target event in an abnormal state;
issuing alarm information when it is determined that the detection target event in the abnormal state exists in the event detection result;
The event detection device according to claim 7. an electronic device,
at least one processor;
a memory communicatively coupled to the at least one processor;
Instructions executable by the at least one processor are stored in the memory, and when the instructions are executed by the at least one processor, the at least one processor performs the operation of any one of claims 1 to 6. perform the event detection method described in
Electronics. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the event detection method of any one of claims 1-6. realizing the method of any one of claims 1 to 6 when executed by a processor,
computer program.

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