CN113205037A - Event detection method and device, electronic equipment and readable storage medium - Google Patents
Event detection method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The utility model discloses an event detection method, which relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning, and can be applied to smart city scenes. The method for detecting the event comprises the following steps: acquiring image data, and identifying a city scene of the image data; determining a target to be detected and an event to be detected corresponding to the urban scene; detecting a target to be detected in the image data to obtain a detection state of the event to be detected; and obtaining an event detection result of the image data according to the detection state of the event to be detected. The method and the device can improve the accuracy and efficiency of event detection.
Description
Technical Field
The utility model relates to an artificial intelligence technical field, concretely relates to computer vision and deep learning technical field can be applied to under the wisdom city scene. A method, an apparatus, an electronic device and a readable storage medium for event detection are provided.
Background
According to the planning layout of the current city management, a large number of video devices are installed at each position of the city for monitoring. By using the massive data, the urban grid intelligent management system has a positive effect on urban management standardization and urban grid intellectualization. However, when the event detection is performed by using the monitoring video stream data in the prior art, the technical problems of poor stability and low accuracy of the detection result exist.
Disclosure of Invention
The present disclosure provides a method, an apparatus, an electronic device and a readable storage medium for event detection, which are used to improve accuracy and efficiency of event detection.
According to a first aspect of the present disclosure, there is provided a method of event detection, comprising: acquiring image data, and identifying a city scene of the image data; determining a target to be detected and an event to be detected corresponding to the urban scene; detecting a target to be detected in the image data to obtain a detection state of the event to be detected; and obtaining an event detection result of the image data according to the detection state of the event to be detected.
According to a second aspect of the present disclosure, there is provided an apparatus for event detection, comprising: the system comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring image data and identifying the urban scene of the image data; the determining unit is used for determining a target to be detected and an event to be detected, which correspond to the urban scene; the detection unit is used for detecting a target to be detected in the image data to obtain the detection state of the event to be detected; and the processing unit is used for obtaining an event detection result of the image data according to the detection state of the event to be detected.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.
According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described above.
According to the technical scheme, the urban scene of the acquired image data is firstly identified, the target to be detected and the event to be detected corresponding to the urban scene are then determined, the target to be detected in the image data is finally detected, the event detection result of the image data is obtained according to the detection state of the event to be detected, and the accuracy and the efficiency of event detection can be improved due to the fact that different urban scenes and different targets to be detected and events to be detected correspond.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;
FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure;
FIG. 3 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;
FIG. 5 is a block diagram of an electronic device used to implement the method of event detection of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure. As shown in fig. 1, the method for detecting an event in this embodiment may specifically include the following steps:
s101, acquiring image data and identifying a city scene of the image data;
s102, determining a target to be detected and an event to be detected, which correspond to the urban scene;
s103, detecting a target to be detected in the image data to obtain a detection state of the event to be detected;
and S104, obtaining an event detection result of the image data according to the detection state of the event to be detected.
The event detection method of the embodiment includes the steps of firstly identifying the city scene of the acquired image data, then determining the target to be detected and the event to be detected corresponding to the city scene, and finally detecting the target to be detected in the image data, so that the event detection result of the image data is obtained according to the detection state of the obtained event to be detected.
The image data obtained by executing S101 in the present embodiment may be extracted from video stream data captured in real time by an image capturing device in a certain monitored area of a city; the acquired image data includes at least one city image, and the included city image may correspond to at least one of city scenes such as roads, squares, schools, and the like.
In the embodiment, when S101 is executed to acquire image data, the optional implementation manner that can be adopted is as follows: acquiring video stream data of a city, wherein different video stream data correspond to different city scenes; at least one key frame image is extracted from the acquired video stream data as image data.
The present embodiment recognizes a city scene of the acquired image data after executing S101 to acquire the image data. In general, the city scene identified in the present embodiment is only one of a road, a square, a school, and the like, but a plurality of city scenes may be obtained by identifying image data.
In this embodiment, when performing S101 to identify a city scene of image data, the optional implementation manners that may be adopted are: the acquired image data is input into the first recognition model, and the city scene of the image data is obtained according to the output result of the first recognition model.
In addition, when the identification of the city scene of the image data is executed in S101, the city scene corresponding to the standard image most similar to the image data may be used as the city scene of the acquired image data by calculating the similarity between the image data and the different standard images.
After the city scene of the acquired image data is identified in S101, the present embodiment executes S102 to determine the target to be detected and the event to be detected corresponding to the identified city scene. In this embodiment, the number of the targets to be detected determined by executing S102 may be one or multiple; the determined event to be detected can be one or a plurality of events.
In this embodiment, different city scenes correspond to different targets to be detected and different events to be detected, and only a specific object to be detected in image data is detected by distinguishing the objects to be detected corresponding to the city scenes, so that detection blindness can be effectively avoided, and event detection efficiency is improved.
For example, if the city scene is a market, the target to be detected corresponding to the market may include people, booths, tables and chairs, billboards, garbage, water accumulation and the like, and the event to be detected corresponding to the market may include an illegal operation event, a water accumulation event and the like; if the urban scene is a road, the target to be detected corresponding to the road can comprise a vehicle, a motorcycle, a rickshaw, a guideboard, a green space, water accumulation and the like, and the incident to be detected corresponding to the road can comprise a illegal parking incident, a lane occupying incident, a water accumulation incident and the like.
In this embodiment, when S102 is executed to determine the target to be detected and the event to be detected corresponding to the city scene, the determination may be performed according to a preset corresponding relationship, where the preset corresponding relationship 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 the step S102 is executed to determine the target to be detected and the event to be detected corresponding to the city scene, the step S103 is executed to detect the target to be detected in the image data, so as to obtain the detection state of the event to be detected. The detection state of the event to be detected obtained by executing S103 in the present embodiment is one of a normal state and an abnormal state.
It can be understood that, if the present embodiment performs S102 to determine a plurality of events to be detected, the present embodiment performs S103 to obtain a detection state corresponding to each event to be detected.
Specifically, in this embodiment, when S103 is executed to detect the target to be detected in the image data to obtain the detection state of the event to be detected, the optional implementation manner that can be adopted is as follows: the acquired image data and the determined target to be detected are input into a second recognition model, and the detection state of the event to be detected is obtained according to the output result of the second recognition model.
In addition, when S103 is executed to detect the target to be detected in the image data to obtain the detection state of the event to be detected, the detection state of the event to be detected may also be obtained according to whether the detection result of the target to be detected exists in the image data, for example, if there is accumulated water in the image data of the corresponding road, it is determined that the accumulated water event is in an abnormal state.
After the detection state of the event to be detected is obtained in S103, the present embodiment performs S104 to obtain an event detection result of the image data according to the obtained detection state of the event to be detected.
In executing S104, the present embodiment may directly use the obtained detection state of the event to be detected as the event detection result of the image data.
Since there may be an error in the detection state of the event to be detected acquired when S103 is executed in the present embodiment, in order to improve the accuracy of the obtained event detection result, when S104 is executed to obtain the event detection result of the image data according to the detection state of the event to be detected, an optional implementation manner that may be adopted is: sending the acquired image data and the acquired event state of the event to be detected to a user for the user to confirm the event state of the event to be detected; and obtaining an event detection result of the image data according to the confirmation result returned by the user.
That is to say, the event state of the obtained event to be detected can be checked by the user in this embodiment, so that the erroneous event state of the event to be detected is removed as much as possible, and the accuracy of the obtained event detection result is further improved. The second recognition model can be optimized by using the confirmation result returned by the user, that is, the image data, the target to be detected of the image data and the confirmation result are used as training data, and the second recognition model is continuously updated, so that the second recognition model can output a more accurate detection state of the event to be detected.
In addition, after executing S104 to obtain the event detection result of the acquired image data, the present embodiment may further include the following: determining whether an event to be detected in an abnormal state exists in the obtained event detection result; and sending alarm information under the condition that the event to be detected in the abnormal state exists in the event detection result, wherein the sent alarm information comprises the abnormal state of the event to be detected and the position information of the event to be detected in the abnormal state, so that the alarm accuracy and the abnormal event handling efficiency are improved.
It is understood that the present embodiment may send alarm information to the personnel in the relevant department, so that the personnel in the relevant department can quickly handle the abnormal event 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 that includes a base configuration module, a video playback storage module, a video analysis module, an alarm information storage module, and an information handling module. The basic configuration module is used for flexibly configuring processing rules of different video data streams according to user requirements, so that the video data streams can be processed in a time-sharing manner and can also be processed in batches; the video playing and storing module is used for completing the real-time playing and storing of the video data stream and supporting the calling and browsing of the video data stream; the video analysis module is used for completing event detection and abnormal event alarm of the embodiment; the alarm information storage module is used for realizing classified retrieval and query display of alarm information; the information handling module is used for realizing handling of the abnormal event.
Fig. 3 is a schematic diagram according to a third embodiment of the present disclosure. Fig. 3 shows a process flow diagram of the information handling unit: after the video data stream is input into the video analysis module, the video analysis module carries out event detection; after the video analysis module detects an abnormal event, generating alarm information and reporting the alarm information to an information processing unit; the information processing unit sends the alarm information to related personnel for confirmation, and if the abnormal event is detected wrongly, the confirmation result is returned to the video analysis module; if the abnormal event does not need to be processed, ending the process; and if the abnormal event needs to be processed, issuing the handling, checking to determine whether the abnormal event is processed or not under the condition that the handling is determined to be finished, and if the abnormal event is not checked, switching to executing the issuing handling.
Fig. 4 is a schematic diagram according to a fourth embodiment of the present disclosure. As shown in fig. 4, the apparatus 400 for event detection of the present embodiment includes:
the acquiring unit 401 is configured to acquire image data and identify a city scene of the image data;
a determining unit 402, configured to determine a target to be detected and an event to be detected, where the target to be detected and the event to be detected correspond to the city scene;
the detecting unit 403 is configured to detect a target to be detected in the image data, so as to obtain a detection state of the event to be detected;
the processing unit 404 is configured to obtain an event detection result of the image data according to the detection state of the event to be detected.
The image data acquired by the acquisition unit 401 may be extracted from video stream data captured in real time by an image capture device in a certain monitored area of a city; the acquired image data includes at least one city image, and the included city image may correspond to at least one of city scenes such as roads, squares, schools, and the like.
When the obtaining unit 401 obtains the image data, the optional implementation manners that can be adopted are: acquiring video stream data of a city; at least one key frame image is extracted from the acquired video stream data as image data.
The acquisition unit 401 identifies a city scene of the acquired image data after acquiring the image data. In a normal case, the city scene identified by the acquisition unit 401 is only one of a road, a square, a school, and the like, but a plurality of city scenes may be obtained by identifying image data.
When the obtaining unit 401 identifies the city scene of the image data, the optional implementation manners that may be adopted are: and inputting the acquired image data into the first recognition model, and obtaining the urban scene of the image data according to the output result of the first recognition model.
In addition, when identifying the city scene of the image data, acquisition section 401 may set, as the city scene of the acquired image data, the city scene corresponding to the standard image most similar to the image data by calculating the similarity between the image data and the different standard images.
After the city scene of the acquired image data is recognized by the acquisition unit 401, the determination unit 402 determines the target to be detected and the event to be detected corresponding to the recognized city scene. The number of the targets to be detected determined by the determining unit 402 may be one or multiple; the determined event to be detected can be one or a plurality of events.
When determining the target to be detected and the event to be detected corresponding to the city scene, the determining unit 402 may determine according to a preset corresponding relationship, where the preset corresponding relationship 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 the determining unit 402 determines the target to be detected and the event to be detected corresponding to the city scene, the detecting unit 403 detects the target to be detected in the image data to obtain the detection state of the event to be detected. The detection state of the event to be detected obtained by the detection unit 403 is one of a normal state and an abnormal state.
It is understood that, if the determining unit 402 determines a plurality of events to be detected, the detecting unit 403 may obtain a detection state corresponding to each event to be detected.
Specifically, when the detection unit 403 detects the target to be detected in the image data to obtain the detection state of the event to be detected, the optional implementation manner that can be adopted is as follows: and inputting the acquired image data and the determined target to be detected into a second recognition model, and obtaining the detection state of the event to be detected according to the output result of the second recognition model.
In addition, when the detection unit 403 detects the target to be detected in the image data to obtain the detection state of the event to be detected, the detection unit may also obtain the detection state of the event to be detected according to whether the detection result of the target to be detected exists in the image data, for example, if there is water accumulated in the image data corresponding to the road, it is determined that the water accumulation event is in an abnormal state.
In the present embodiment, after the detection state of the event to be detected is obtained by the detection unit 403, the processing unit 404 obtains an event detection result of the image data according to the obtained detection state of the event to be detected.
The processing unit 404 may directly use the detection state of the event to be detected obtained by the detection unit 403 as the event detection result of the image data.
Since the detection state of the event to be detected acquired by the detection unit 403 may have an error, in order to improve the accuracy of the obtained event detection result, when the processing unit 404 obtains the event detection result of the image data according to the detection state of the event to be detected, the optional implementation manner that may be adopted is: sending the acquired image data and the acquired event state of the event to be detected to a user for the user to confirm the event state of the event to be detected; and obtaining an event detection result of the image data according to the confirmation result returned by the user.
That is to say, the processing unit 404 may also check the obtained event state of the event to be detected by the user, so as to remove an erroneous event state of the event to be detected as much as possible, and further improve the accuracy of the obtained event detection result. The processing unit 404 can further optimize the second recognition model by using the confirmation result returned by the user, that is, the image data, the target to be detected of the image data, and the confirmation result are used as training data, and the second recognition model is continuously updated, so that the second recognition model can output a more accurate detection state of the event to be detected.
In addition, the event detection apparatus 400 in this embodiment may further include an alarm unit 405, configured to perform: after the event detection result of the acquired image data is obtained, determining whether an event to be detected in an abnormal state exists in the obtained event detection result; and sending alarm information under the condition that the event to be detected in the abnormal state exists in the event detection result, wherein the sent alarm information comprises the abnormal state of the event to be detected and the position information of the event to be detected in the abnormal state.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
As shown in fig. 5, is a block diagram of an electronic device of a method of event detection according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 comprises a computing unit 501 which may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM502, and the RAM503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 505 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 executes the respective methods and processes described above, such as the method of event detection. For example, in some embodiments, the method of event detection may 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 may be loaded and/or installed onto the device 500 via the ROM502 and/or the communication unit 509. When the computer program is loaded into the RAM503 and executed by the computing unit 501, one or more steps of the method of event detection described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured by any other suitable means (e.g., by means of firmware) to perform the method of event detection.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (15)
1. A method of event detection, comprising:
acquiring image data, and identifying a city scene of the image data;
determining a target to be detected and an event to be detected corresponding to the urban scene;
detecting a target to be detected in the image data to obtain a detection state of the event to be detected;
and obtaining an event detection result of the image data according to the detection state of the event to be detected.
2. The method of claim 1, wherein the acquiring image data comprises:
acquiring video stream data of a city;
at least one key frame image is extracted from the video stream data as the image data.
3. The method of claim 1, wherein the determining the target to be detected and the event to be detected corresponding to the urban scene comprises:
and determining the target to be detected and the event to be detected corresponding to the city scene according to a preset corresponding relation.
4. The method according to claim 1, wherein the detecting the target to be detected in the image data to obtain the detection state of the event to be detected comprises:
and inputting the image data and the target to be detected into a second recognition model, and obtaining the detection state of the event to be detected according to the output result of the second recognition model.
5. The method according to claim 1, wherein the obtaining an event detection result of the image data according to the detection state of the event to be detected comprises:
sending the image data and the event state of the event to be detected to a user;
and obtaining an event detection result of the image data according to a confirmation result returned by the user.
6. The method of claim 1, further comprising:
after the event detection result of the image data is obtained, determining whether an event to be detected in an abnormal state exists in the event detection result;
and sending alarm information under the condition that the event to be detected in the abnormal state exists in the event detection result.
7. An apparatus for event detection, comprising:
the system comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring image data and identifying the urban scene of the image data;
the determining unit is used for determining a target to be detected and an event to be detected, which correspond to the urban scene;
the detection unit is used for detecting a target to be detected in the image data to obtain the detection state of the event to be detected;
and the processing unit is used for obtaining an event detection result of the image data according to the detection state of the event to be detected.
8. The apparatus according to claim 7, wherein the acquisition unit, when acquiring the image data, specifically performs:
acquiring video stream data of a city;
at least one key frame image is extracted from the video stream data as the image data.
9. The device according to claim 7, wherein the determining unit, when determining the target to be detected and the event to be detected corresponding to the city scene, specifically performs:
and determining the target to be detected and the event to be detected corresponding to the city scene according to a preset corresponding relation.
10. The apparatus according to claim 7, wherein the detecting unit, when detecting the object to be detected in the image data and obtaining the detection state of the event to be detected, specifically performs:
and inputting the image data and the target to be detected into a second recognition model, and obtaining the detection state of the event to be detected according to the output result of the second recognition model.
11. The apparatus according to claim 7, wherein the processing unit, when obtaining the event detection result of the image data according to the detection state of the event to be detected, specifically performs:
sending the image data and the event state of the event to be detected to a user;
and obtaining an event detection result of the image data according to a confirmation result returned by the user.
12. The apparatus of claim 7, further comprising an alarm unit to perform:
after the processing unit obtains the event detection result of the image data, determining whether an event to be detected in an abnormal state exists in the event detection result;
and sending alarm information under the condition that the event to be detected in the abnormal state exists in the event detection result.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1 to 6.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.
Priority Applications (4)
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|---|---|---|---|
| CN202110466781.3A CN113205037B (en) | 2021-04-28 | 2021-04-28 | Event detection method, event detection device, electronic equipment and readable storage medium |
| JP2022543087A JP2023527100A (en) | 2021-04-28 | 2022-01-29 | Event detection method, device, electronic device, readable storage medium, and computer program |
| KR1020227024754A KR20220149508A (en) | 2021-04-28 | 2022-01-29 | Event detection method, apparatus, electronic device and readable recording medium |
| PCT/CN2022/075019 WO2022227764A1 (en) | 2021-04-28 | 2022-01-29 | Event detection method and apparatus, electronic device, and readable storage medium |
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| CN202110466781.3A CN113205037B (en) | 2021-04-28 | 2021-04-28 | Event detection method, event detection device, electronic equipment and readable storage medium |
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| JP (1) | JP2023527100A (en) |
| KR (1) | KR20220149508A (en) |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114118933A (en) * | 2021-08-24 | 2022-03-01 | 泰华智慧产业集团股份有限公司 | Recording method and system for city management event |
| CN114332731A (en) * | 2021-12-24 | 2022-04-12 | 阿波罗智联(北京)科技有限公司 | City event identification method and device, automatic driving vehicle and cloud server |
| CN114445711A (en) * | 2022-01-29 | 2022-05-06 | 北京百度网讯科技有限公司 | Image detection method, image detection device, electronic equipment and storage medium |
| CN114943510A (en) * | 2022-05-09 | 2022-08-26 | 上海商汤科技开发有限公司 | City management case processing method, system, device, equipment and storage medium |
| WO2022227764A1 (en) * | 2021-04-28 | 2022-11-03 | 北京百度网讯科技有限公司 | Event detection method and apparatus, electronic device, and readable storage medium |
| CN115334250A (en) * | 2022-08-09 | 2022-11-11 | 阿波罗智能技术(北京)有限公司 | Image processing method and device and electronic equipment |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116035564B (en) * | 2022-12-06 | 2024-08-02 | 北京顺源辰辰科技发展有限公司 | Dysphagia and aspiration intelligent detection method and device and electronic equipment |
| CN117407507A (en) * | 2023-10-27 | 2024-01-16 | 北京百度网讯科技有限公司 | Event processing method, device, equipment and medium based on large language model |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678267A (en) * | 2016-01-08 | 2016-06-15 | 浙江宇视科技有限公司 | Scene recognition method and device |
| CN109815852A (en) * | 2019-01-03 | 2019-05-28 | 深圳壹账通智能科技有限公司 | Smart city event management method, device, computer equipment and storage medium |
| CN110443969A (en) * | 2018-05-03 | 2019-11-12 | 中移(苏州)软件技术有限公司 | A kind of fire point detecting method, device, electronic equipment and storage medium |
| CN111753634A (en) * | 2020-03-30 | 2020-10-09 | 上海高德威智能交通系统有限公司 | Traffic incident detection method and device |
| CN112507813A (en) * | 2020-11-23 | 2021-03-16 | 北京旷视科技有限公司 | Event detection method and device, electronic equipment and storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679189B (en) * | 2012-09-14 | 2017-02-01 | 华为技术有限公司 | Method and device for recognizing scene |
| US20220001858A1 (en) * | 2018-11-13 | 2022-01-06 | Nec Corporation | Dangerous scene prediction device, dangerous scene prediction method, and dangerous scene prediction program |
| US10885775B2 (en) * | 2019-06-06 | 2021-01-05 | Verizon Patent And Licensing Inc. | Monitoring a scene to analyze an event using a plurality of image streams |
| CN113205037B (en) * | 2021-04-28 | 2024-01-26 | 北京百度网讯科技有限公司 | Event detection method, event detection device, electronic equipment and readable storage medium |
-
2021
- 2021-04-28 CN CN202110466781.3A patent/CN113205037B/en active Active
-
2022
- 2022-01-29 JP JP2022543087A patent/JP2023527100A/en active Pending
- 2022-01-29 WO PCT/CN2022/075019 patent/WO2022227764A1/en active Application Filing
- 2022-01-29 KR KR1020227024754A patent/KR20220149508A/en not_active Application Discontinuation
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678267A (en) * | 2016-01-08 | 2016-06-15 | 浙江宇视科技有限公司 | Scene recognition method and device |
| CN110443969A (en) * | 2018-05-03 | 2019-11-12 | 中移(苏州)软件技术有限公司 | A kind of fire point detecting method, device, electronic equipment and storage medium |
| CN109815852A (en) * | 2019-01-03 | 2019-05-28 | 深圳壹账通智能科技有限公司 | Smart city event management method, device, computer equipment and storage medium |
| CN111753634A (en) * | 2020-03-30 | 2020-10-09 | 上海高德威智能交通系统有限公司 | Traffic incident detection method and device |
| CN112507813A (en) * | 2020-11-23 | 2021-03-16 | 北京旷视科技有限公司 | Event detection method and device, electronic equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| LIMIN WANG ET AL.: "Object-Scene Convolutional Neural Networks for Event Recognition in Images", PROCEEDINGS OF IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION(CVPR) WORKSHOPS, pages 30 - 35 * |
| 汪勤 等: "基于视频图像处理的交通事件检测系统", 计算机应用, vol. 28, no. 7, pages 1886 - 1889 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022227764A1 (en) * | 2021-04-28 | 2022-11-03 | 北京百度网讯科技有限公司 | Event detection method and apparatus, electronic device, and readable storage medium |
| CN114118933A (en) * | 2021-08-24 | 2022-03-01 | 泰华智慧产业集团股份有限公司 | Recording method and system for city management event |
| CN114332731A (en) * | 2021-12-24 | 2022-04-12 | 阿波罗智联(北京)科技有限公司 | City event identification method and device, automatic driving vehicle and cloud server |
| CN114445711A (en) * | 2022-01-29 | 2022-05-06 | 北京百度网讯科技有限公司 | Image detection method, image detection device, electronic equipment and storage medium |
| CN114943510A (en) * | 2022-05-09 | 2022-08-26 | 上海商汤科技开发有限公司 | City management case processing method, system, device, equipment and storage medium |
| CN115334250A (en) * | 2022-08-09 | 2022-11-11 | 阿波罗智能技术(北京)有限公司 | Image processing method and device and electronic equipment |
| CN115334250B (en) * | 2022-08-09 | 2024-03-08 | 阿波罗智能技术(北京)有限公司 | Image processing method and device and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| JP2023527100A (en) | 2023-06-27 |
| CN113205037B (en) | 2024-01-26 |
| KR20220149508A (en) | 2022-11-08 |
| WO2022227764A1 (en) | 2022-11-03 |
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