CN113205037B - Event detection method, event detection device, electronic equipment and readable storage medium - Google Patents
Event detection method, event detection device, electronic equipment and readable storage medium Download PDFInfo
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- G06V20/40—Scenes; Scene-specific elements in video content
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Abstract
The disclosure discloses an event detection method, 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 event detection method comprises the following steps: acquiring image data and identifying urban scenes of the image data; determining a target to be detected and an event to be detected which correspond 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 event detection method and device can improve the accuracy and efficiency of event detection.
Description
Technical Field
The disclosure 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. Provided are a method, apparatus, electronic device, and readable storage medium for event detection.
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, urban grid intellectualization plays a positive role in urban management standardization. However, in the prior art, when event detection is performed by using monitoring video stream data, the technical problems of poor stability and lower accuracy of detection results exist.
Disclosure of Invention
The disclosure provides a method, a device, an electronic device and a readable storage medium for event detection, which are used for improving the 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 urban scenes of the image data; determining a target to be detected and an event to be detected which correspond 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: an acquisition unit configured to acquire image data, and identify a city 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 a 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 storing computer instructions for causing the computer to perform the method as described above.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method as described above.
According to the technical scheme, the urban scene of the acquired image data is firstly identified, then the object to be detected and the event to be detected corresponding to the urban scene are determined, and finally the object to be detected in the image data is detected, so that the event detection result of the image data is obtained according to the detection state of the obtained event to be detected.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for 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 for implementing a method of event detection of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one 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 urban scenes 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;
s104, obtaining an event detection result of the image data according to the detection state of the event to be detected.
According to the event detection method, firstly, urban scenes of the acquired image data are identified, then, targets to be detected and events to be detected corresponding to the urban scenes are determined, finally, the targets to be detected in the image data are detected, so that event detection results of the image data are obtained according to the detection states of the obtained events to be detected, and because different urban scenes and different targets to be detected are corresponding to the events to be detected, the accuracy and the efficiency of event detection can be improved.
The image data acquired in S101 may be extracted from video stream data captured in real time by an image capturing device in a certain monitoring area of a city; the acquired image data contains at least one city image, and the contained city image can 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, an optional implementation manner may be: 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 present embodiment recognizes that the obtained city scene is only one of a road, a square, a school, and the like, but there are cases where a plurality of city scenes are obtained by recognizing image data.
In executing S101 to identify the urban scene of the image data, this embodiment may adopt the following alternative implementation manners: the acquired image data is input into a first recognition model, and the urban scene of the image data is obtained according to the output result of the first recognition model, wherein the first recognition model is obtained through pre-training and can recognize the urban scene corresponding to the image data.
In addition, in the present embodiment, when S101 is executed to identify the urban scene of the image data, the urban scene corresponding to the standard image most similar to the image data may be used as the urban scene of the acquired image data by calculating the similarity between the image data and the different standard images.
After executing S101 to identify the urban scene of the acquired image data, the present embodiment executes S102 to determine the object to be detected and the event to be detected corresponding to the identified urban scene. The target to be detected determined in the step S102 may be one or more. The number of the determined events to be detected may be one or more.
In this embodiment, different city scenes correspond to different targets to be detected and different events to be detected, and the mode of distinguishing the targets to be detected corresponding to the city scenes is adopted, so that the purpose of detecting only specific targets to be detected in image data is achieved, detection blindness can be effectively avoided, and event detection efficiency is improved.
For example, if the urban scene is a market, the targets to be detected in the corresponding market may include people, booths, tables and chairs, billboards, garbage, water accumulation, etc., and the events to be detected in the corresponding market may include illegal operation events, water accumulation events, etc.; if the urban scene is a road, the target to be detected of the corresponding road can comprise vehicles, motorcycles, rickshaw, guideboards, greenbelts, ponding and the like, and the event to be detected of the corresponding road can comprise illegal parking events, road occupation events, ponding events and the like.
In the embodiment, when the step S102 is executed to determine the to-be-detected target and the to-be-detected event corresponding to the city scenes, the to-be-detected target and the to-be-detected event corresponding to the city scenes may be determined according to a preset corresponding relationship, where the preset corresponding relationship includes each city scene and the to-be-detected target and the to-be-detected event corresponding to each city scene.
In this embodiment, after determining the to-be-detected target and the to-be-detected event corresponding to the urban scene in S102, S103 is executed to detect the to-be-detected target in the image data, so as to obtain the detection state of the to-be-detected event. The detection state of the event to be detected obtained in S103 is one of a normal state and an abnormal state.
It can be understood that, if the embodiment executes S102 to determine a plurality of events to be detected, the embodiment executes S103 to obtain the detection status corresponding to each event to be detected.
Specifically, in the embodiment, when executing S103 to detect a target to be detected in the image data to obtain a detection state of an event to be detected, optional implementation manners may be adopted 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, in the embodiment, when the step 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 water accumulation in the image data corresponding to the road, then it is determined that the water accumulation event is in an abnormal state.
In this embodiment, after the detection state of the event to be detected is obtained in step S103, step S104 obtains an event detection result of the image data according to the obtained detection state of the event to be detected.
In the embodiment, when S104 is executed, the obtained detection state of the event to be detected may be directly used as the event detection result of the image data.
Since the detection state of the event to be detected, which is acquired when S103 is executed, may have an error, in order to improve the accuracy of the obtained event detection result, when S104 is executed, according to the detection state of the event to be detected, the event detection result of the image data is obtained, an optional implementation manner may be adopted as follows: the acquired image data and the obtained event state of the event to be detected are sent 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, in this embodiment, the user may check the obtained event state of the event to be detected, so as to remove the error event state of the event to be detected as much as possible, and further improve the accuracy of the obtained event detection result. In this embodiment, the second recognition model may 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 may output a more accurate detection state of the event to be detected.
In addition, the present embodiment may further include the following after the event detection result of the acquired image data is obtained by executing S104: determining whether an event to be detected in an abnormal state exists in the obtained event detection result; under the condition that the event to be detected in the abnormal state exists in the event detection result, alarm information is sent, and 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 treatment efficiency of the abnormal event are improved.
It can be appreciated that the present embodiment may send alarm information to the related personnel to enable the related personnel to quickly process 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 or in a batch manner; the video playing storage module is used for completing real-time playing and storage of the video data stream and supporting retrieval and browsing of the video data stream; the video analysis module is used for completing event detection and abnormal event alarming in the embodiment; the alarm information storage module is used for realizing classified retrieval, inquiry and display of alarm information; the information handling module is used for handling 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 inputting the video data stream into a 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 disposal unit; the information processing unit sends alarm information to related partial personnel for confirmation, and if the abnormal event is detected to be wrong, the confirmation result is returned to the video analysis module; if the abnormal event does not need to be processed, ending the flow; if the abnormal event needs to be processed, issuing treatment, and checking to determine whether the abnormal event is processed or not under the condition that the treatment is determined to be completed, and if not, turning to executing the issuing treatment.
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 in this embodiment includes:
an acquiring unit 401, 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 corresponding to the urban scene;
the detection 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;
and 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 capturing device in a certain monitoring area of a city; the acquired image data contains at least one city image, and the contained city image can correspond to at least one of city scenes such as roads, squares, schools and the like.
The acquiring unit 401 may adopt the following alternative implementation manners when acquiring the image data: 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 acquisition unit 401 recognizes that the obtained city scene is only one of a road, a square, a school, and the like, but there are also cases where a plurality of city scenes are obtained by recognizing image data.
The obtaining unit 401 may adopt the following alternative implementation manners when identifying the urban scene of the image data: and inputting the acquired image data into a 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 a city scene of image data, the acquisition unit 401 may also use a city scene corresponding to a standard image most similar to the image data as a city scene of the acquired image data by calculating the similarity between the image data and a different standard image.
The present embodiment, after the urban scene of the acquired image data is identified by the acquisition unit 401, determines a target to be detected and an event to be detected corresponding to the identified urban scene by the determination unit 402. The target to be detected determined by the determining unit 402 may be one or more; the number of the determined events to be detected may be one or more.
The determining unit 402 may determine, when determining the object to be detected and the event to be detected corresponding to the urban scene, according to a preset corresponding relationship, where the preset corresponding relationship includes each urban scene, and the object to be detected and the event to be detected corresponding to each urban scene.
In this embodiment, after determining, by the determining unit 402, a target to be detected and an event to be detected corresponding to a city scene, the detecting unit 403 detects the target to be detected in the image data, so as to obtain a detection state of the event to be detected. Wherein 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 can be 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 and obtains the detection state of the event to be detected, the following optional implementation manners may be adopted: 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 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 water accumulation in the image data corresponding to the road, then it is determined that the water accumulation event is in an abnormal state.
In this embodiment, after the detection unit 403 obtains the detection state of the event to be detected, the processing unit 404 obtains the 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 take the detection state of the event to be detected obtained by the detection unit 403 as an 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, an optional implementation manner may be: the acquired image data and the obtained event state of the event to be detected are sent 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, the processing unit 404 may further check the obtained event status of the event to be detected by the user, so as to remove the error event status of the event to be detected as much as possible, thereby further improving the accuracy of the obtained event detection result. The processing unit 404 may further use the confirmation result returned by the user to optimize the second recognition model, that is, the image data, the target to be detected of the image data, and the confirmation result as training data, to continuously update the second recognition model, so that the second recognition model may output a more accurate detection state of the event to be detected.
In addition, the apparatus 400 for event detection in this embodiment may further include an alarm unit 405 for performing: after an 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 under the condition that the event to be detected in the abnormal state exists in the event detection result, sending alarm information, 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.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
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 telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 includes a computing unit 501 that can perform various suitable actions and processes according to 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 computing unit 501, ROM502, and RAM503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 505 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an 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 via a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various 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 on 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 a computer program is loaded into RAM503 and executed by 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 to perform the method of event detection in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here can be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On 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, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code 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 code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. 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. The 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 pointing device (e.g., a mouse or 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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 a client and a server. The client and server are typically 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 that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A method of event detection, comprising:
acquiring image data, and identifying a city scene of the image data, wherein the city scene is one of a square scene, a market scene and a school scene;
determining a target to be detected corresponding to the urban scene and an event to be detected corresponding to the urban scene according to a preset corresponding relation, wherein the preset corresponding relation comprises the target to be detected corresponding to different urban scenes and the event to be detected corresponding to different urban scenes;
detecting a target to be detected in the image data to obtain a detection state of an event to be detected corresponding to the urban scene of the image data;
obtaining an event detection result of the image data according to the detection state of the event to be detected;
wherein the identifying the city scene of the image data comprises:
calculating the similarity between the image data and different standard images;
taking the urban scene corresponding to the standard image with the highest similarity calculation result as the urban scene of the image data;
the detecting the object to be detected in the image data to obtain the detection state of the event to be detected corresponding to the urban scene of the image data comprises the following steps:
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 corresponding to the urban scene of the image data according to the output result of the second recognition model;
the method further comprises the steps of:
acquiring a confirmation result of a user on the detection state of an event to be detected corresponding to the urban scene of the image data;
and updating the second recognition model by taking the image data, the target to be detected corresponding to the urban scene of the image data and the confirmation result as training data.
2. The method of claim 1, wherein the acquiring image data comprises:
acquiring video stream data of a city;
and extracting at least one key frame image from the video stream data as the image data.
3. The method according to claim 1, wherein the obtaining the event detection result of the image data according to the detection state of the event to be detected includes:
transmitting the image data and the detection state of the event to be detected to a user;
and obtaining an event detection result of the image data according to the confirmation result returned by the user.
4. The method of claim 1, further comprising:
after an 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 out alarm information under the condition that the event to be detected in an abnormal state exists in the event detection result.
5. An apparatus for event detection, comprising:
the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring image data and identifying a city scene of the image data, wherein the city scene is one of a square scene, a market scene and a school scene;
the determining unit is used for determining a target to be detected corresponding to the urban scene and an event to be detected corresponding to the urban scene according to a preset corresponding relation, wherein the preset corresponding relation comprises the target to be detected corresponding to different urban scenes and the event to be detected corresponding to different urban scenes;
the detection unit is used for detecting a target to be detected in the image data to obtain a detection state of an event to be detected corresponding to the urban scene of the image data;
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;
wherein the acquiring unit, when identifying the urban scene of the image data, specifically performs:
calculating the similarity between the image data and different standard images;
taking the urban scene corresponding to the standard image with the highest similarity calculation result as the urban scene of the image data;
the detection unit specifically performs the following steps when detecting a target to be detected in the image data to obtain a detection state of an event to be detected corresponding to a city scene of the image data:
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 corresponding to the urban scene of the image data according to the output result of the second recognition model;
the processing unit is further configured to perform:
acquiring a confirmation result of a user on the detection state of an event to be detected corresponding to the urban scene of the image data;
and updating the second recognition model by taking the image data, the target to be detected corresponding to the urban scene of the image data and the confirmation result as training data.
6. The apparatus according to claim 5, wherein the acquisition unit, when acquiring the image data, specifically performs:
acquiring video stream data of a city;
and extracting at least one key frame image from the video stream data as the image data.
7. The apparatus according to claim 5, 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:
transmitting the image data and the detection state of the event to be detected to a user;
and obtaining an event detection result of the image data according to the confirmation result returned by the user.
8. The apparatus of claim 5, further comprising an alarm unit for performing:
after the processing unit obtains an 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 out alarm information under the condition that the event to be detected in an abnormal state exists in the event detection result.
9. 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 4.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1 to 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 |
| PCT/CN2022/075019 WO2022227764A1 (en) | 2021-04-28 | 2022-01-29 | Event detection method and apparatus, electronic device, and readable storage medium |
| KR1020227024754A KR20220149508A (en) | 2021-04-28 | 2022-01-29 | Event detection method, apparatus, electronic device and readable recording medium |
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| 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 |
| 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 |
| CN114445711B (en) * | 2022-01-29 | 2023-04-07 | 北京百度网讯科技有限公司 | Image detection method, image detection device, electronic equipment and storage medium |
| 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 |
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| KR20220149508A (en) | 2022-11-08 |
| CN113205037A (en) | 2021-08-03 |
| WO2022227764A1 (en) | 2022-11-03 |
| JP2023527100A (en) | 2023-06-27 |
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