CN112291520A - Abnormal event identification method and device, storage medium and electronic device - Google Patents
Abnormal event identification method and device, storage medium and electronic device Download PDFInfo
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- CN112291520A CN112291520A CN202011158988.6A CN202011158988A CN112291520A CN 112291520 A CN112291520 A CN 112291520A CN 202011158988 A CN202011158988 A CN 202011158988A CN 112291520 A CN112291520 A CN 112291520A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
Abstract
The embodiment of the invention provides an abnormal event identification method, an abnormal event identification device, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring a detection signal sent by target detection equipment, wherein the detection signal is used for indicating the target detection equipment to detect that a target object appears in a target area; acquiring a target video stream acquired by target camera equipment, wherein the target camera equipment is used for shooting images in a target area to obtain the target video stream; the target video stream is analyzed to identify anomalous events in the target area. By the method and the device, the problem that the abnormal event identification resources are unreasonably utilized in the related technology is solved, and the effect of fully utilizing the abnormal event identification resources is achieved.
Description
Technical Field
The embodiment of the invention relates to the field of communication, in particular to an abnormal event identification method, an abnormal event identification device, a storage medium and an electronic device.
Background
In the related technology, the real-time video abnormal event intelligent warning system detects and analyzes a plurality of independent real-time video channels respectively. And identifying whether the image picture of each real-time video channel has abnormal events violating established rules or not by utilizing the tracking relation of the targets in the front frame and the rear frame in the video channel. To ensure that tracking starts as soon as a target appears, existing detection methods keep analyzing the on state for a certain video channel. But for some video channels where the frequency of occurrence of objects is low, resources are wasted. The flow chart of the abnormal event identification in the related art can be seen in fig. 1.
Therefore, the problem that the abnormal event identification resource is unreasonably utilized exists in the related art.
In view of the above problems in the related art, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides an abnormal event identification method, an abnormal event identification device, a storage medium and an electronic device, and at least solves the problem that the abnormal event identification resource is unreasonably utilized in the related technology.
According to an embodiment of the present invention, there is provided an abnormal event recognition method including: acquiring a detection signal sent by target detection equipment, wherein the detection signal is used for indicating the target detection equipment to detect that a target object appears in a target area; acquiring a target video stream acquired by target camera equipment, wherein the target camera equipment is used for shooting images in the target area to obtain the target video stream; analyzing the target video stream to identify an abnormal event in the target area.
According to another embodiment of the present invention, there is provided an abnormal event recognizing apparatus including: the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a detection signal sent by target detection equipment, and the detection signal is used for indicating the target detection equipment to detect a target object in a target area; the second acquisition module is used for acquiring a target video stream acquired by target camera equipment, wherein the target camera equipment is used for shooting images in the target area to obtain the target video stream; an identification module to analyze the target video stream to identify abnormal events in the target area.
According to a further embodiment of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of the above-mentioned method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in the above method embodiments.
According to the method and the device, after the detection signal which is sent by the target detection equipment and used for indicating the target detection equipment to detect the target object in the target area is obtained, the target video stream collected by the target camera equipment is obtained and analyzed to identify the abnormal event in the target area.
Drawings
FIG. 1 is a flow chart of abnormal event recognition in the related art of the present invention;
fig. 2 is a block diagram of a hardware structure of a mobile terminal of an abnormal event identification method according to an embodiment of the present invention;
FIG. 3 is a flow chart of an abnormal event identification method according to an embodiment of the present invention;
fig. 4 is a flowchart of determining an association relationship of a target detection apparatus and a target image capturing apparatus according to an exemplary embodiment of the present invention;
FIG. 5 is a flow diagram of a method for abnormal event identification in accordance with an embodiment of the present invention;
FIG. 6 is a block diagram of an abnormal event recognition method according to an embodiment of the present invention;
fig. 7 is a block diagram of an abnormal event recognition apparatus according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking the operation on the mobile terminal as an example, fig. 2 is a hardware structure block diagram of the mobile terminal of an abnormal event identification method according to an embodiment of the present invention. As shown in fig. 2, the mobile terminal may comprise one or more processors 202 (only one is shown in fig. 2) (the processor 202 may comprise, but is not limited to, a processing means such as a microprocessor MCU or a programmable logic device FPGA), and a memory 204 for storing data, wherein the mobile terminal may further comprise a transmission device 206 for communication functions and an input-output device 208. It will be understood by those skilled in the art that the structure shown in fig. 2 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 2, or have a different configuration than shown in FIG. 2.
The memory 204 may be used for storing computer programs, for example, software programs and modules of application software, such as a computer program corresponding to the abnormal event identification method in the embodiment of the present invention, and the processor 202 executes various functional applications and data processing by running the computer programs stored in the memory 204, so as to implement the method described above. Memory 204 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 204 may further include memory located remotely from the processor 202, which may be connected to the mobile terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 206 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 206 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 206 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In the present embodiment, an abnormal event identification method is provided, and fig. 3 is a flowchart of the abnormal event identification method according to the embodiment of the present invention, as shown in fig. 3, the flowchart includes the following steps:
step S302, a detection signal sent by target detection equipment is obtained, wherein the detection signal is used for indicating that the target detection equipment detects a target object in a target area;
step S304, acquiring a target video stream acquired by target camera equipment, wherein the target camera equipment is used for shooting images in the target area to obtain the target video stream;
step S306, analyzing the target video stream to identify an abnormal event in the target area.
In the above-described embodiments, the target detection apparatus may be a sensor or the like, for example, a sensor for detecting the open/close state of a door, a radar sensor, an infrared sensor, a sound sensor, a photosensor, a smoke sensor, or the like, and the target imaging apparatus may be an imaging apparatus that images a target area, for example, a monitoring camera, or the like. The target object may be a person, an animal, an item, a vehicle, etc.
In the above embodiment, the detection signal of the target detection device may indicate whether the target object is present in the target area, that is, whether the target object is present in the target area may be determined according to the size of the detection signal, and in order to improve the accuracy of the determination, the target detection device may be subjected to a jitter filtering process. When the target detection device is a sensor, different jitter filtering strategies, such as filtering jitter, threshold filtering, etc., may be performed for different sensors. When the target detection device is a sound sensor, a decibel threshold may be set, and when the detected sound signal exceeds the decibel threshold, it is determined that the target object exists in the target area. When the target detection device is a photosensitive sensor, a device illumination intensity threshold value can be used, and when the illumination intensity exceeds the illumination intensity threshold value, the target object in the target area is determined to exist. Similarly, a smoke concentration threshold value and the like can also be set. When the target detection device is a radar sensor, an infrared sensor, or the like, the signal may be subjected to jitter filtering to improve the accuracy of determining the presence of the target object.
For example, the main body of the above steps may be an abnormal behavior detection system, a processor, or other devices with similar processing capabilities, and may also be a machine integrated with at least an audio acquisition device and a data processing device, where the audio acquisition device may include an audio acquisition module such as a microphone, and the data processing device may include a terminal such as a computer, a mobile phone, and the like, but is not limited thereto.
In the above embodiment, assuming that the current abnormal behavior detection system has M paths of real-time video detection capabilities, and only Y (Y is much smaller than N, and Y is equal to or smaller than M) paths of video streams to be analyzed have target objects appearing at the same time, when the sensor senses sound and moving objects, the abnormal behavior detection related to frame tracking before and after the video stream in the visual field in which the sensor is located is started by using an external low-cost sensor (corresponding to the above target detection device, for example, a sensor for detecting the opening and closing states of a door, a radar sensor, an infrared sensor, a sound sensor, a photosensitive sensor, a smoke sensor, etc.). Therefore, under the condition of only M paths of capabilities, N (N is far larger than M) paths of videos are detected in real time.
According to the method and the device, after the detection signal which is sent by the target detection equipment and used for indicating the target detection equipment to detect the target object in the target area is obtained, the target video stream collected by the target camera equipment is obtained and analyzed to identify the abnormal event in the target area.
In one exemplary embodiment, acquiring a target video stream captured by a target imaging device includes: determining the target image pickup device associated with the target detection device based on a predetermined association relationship, wherein the association relationship is used for recording the association relationship between the detection device and one or more image pickup devices; and acquiring the target video stream collected by the target camera equipment associated with the target detection equipment. In this embodiment, the target image capturing device associated with the target detection device may be determined according to a predetermined association relationship, and then the target video stream acquired by the target image capturing device may be acquired. The association relationship may be a correspondence relationship between the target detection apparatus and the target imaging apparatus, and may be one-to-one, one-to-many, or many-to-many. When the target detection device is a sensor and the target image pickup device is a camera, the association relationship may be a correspondence relationship between the sensor and the camera, where one sensor corresponds to one camera, one sensor corresponds to multiple cameras, and multiple sensors correspond to multiple cameras.
In one exemplary embodiment, before determining the target image capturing apparatus associated with the target detection apparatus based on a predetermined association relationship, the method further includes: determining the association by at least one of: acquiring the input incidence relation; determining the association relation based on an input device distribution diagram, wherein distribution information of the detection device and the image pickup device is recorded in the device distribution diagram; and storing the association relation. In this embodiment, the association between the target detection apparatus and the target image capturing apparatus may be an association input by the user, that is, before the abnormal event is recognized, after the user completes the association between the target detection apparatus and the target image capturing apparatus, the association is input to the detection system. The association relationship between the object detection apparatus and the object imaging apparatus may also be determined from an input apparatus distribution diagram, that is, the position distribution diagrams of the object detection apparatus and the object imaging apparatus may be acquired first, and the association relationship between the object detection apparatus and the object imaging apparatus may be determined from the position distribution diagrams.
In one exemplary embodiment, storing the association comprises: checking whether the association relationship is legal or not; and storing the association relation under the condition that the association relation is verified to be legal. In this embodiment, after the association relationship between the target detection apparatus and the target image capturing apparatus is determined, whether the association relationship is legitimate may be detected, and in the case where the association relationship is determined to be legitimate, the association relationship is stored. The checking the validity of the association relationship includes checking the validity of the camera ID, and the like.
In an exemplary embodiment, after checking whether the association relationship is legal, the method further includes: and feeding back first alarm information under the condition that the association relation is verified to be illegal. In this embodiment, when it is detected that the association between the target detection device and the target imaging device is illegal, the first warning information is fed back to prompt the user that the association is illegal, and the association is adjusted. When the target detection device is a sensor and the target image capturing device is a camera, a flowchart for determining the association relationship between the target detection device and the target image capturing device may refer to fig. 4, as shown in fig. 4, where the flowchart includes:
step S402, configuring the association relationship between the sensor and the camera by a user;
step S404 determines whether the association relationship is legitimate, and if the determination result is yes, step S406 is executed, and if the determination result is no, step S408 is executed.
Step S406, storing the configuration data, that is, storing the association relationship.
Step S408, an error code is returned to the user, that is, the first warning information is returned to the user.
And step S410, ending.
In one exemplary embodiment, acquiring a target video stream captured by a target imaging device includes: determining a video stream corresponding to the target camera equipment from a plurality of pre-cached video streams; and determining a video stream within a target time period included in the video stream corresponding to the target camera device as the target video stream. In this embodiment, after the detection signal is acquired, a video stream corresponding to the target image capturing apparatus is determined from a plurality of pre-buffered videos, and a video stream within a target time period in the video stream is determined as a target video stream. Since there will be a certain delay between the sending of the detection signal by the target detection device and the obtaining of the detection signal sent by the detection device, the video stream collected by the target camera device may be buffered to make up for the delay from the actual occurrence of the target object to the sending of the sensor signal (corresponding to the detection signal) to the switching module, that is, the video stream in the target time period may include the video frames in a period before the detection signal sent by the target detection device is obtained, and may also include the video frames in a period after the detection signal sent by the target detection device is obtained. The method can establish N-path video frame buffer queues for storage, wherein the size of the buffer queue of the buffer depends on the size of delay, when the delay is large, the storage space of the buffer queue is large, when the delay is small, the storage space of the buffer queue is small, wherein the size of the buffer queue can be set by self, the buffer queue can be used for buffering video frames with the same frame number, and different target time periods can be set for different camera devices because the video frames acquired by different camera devices in the same time are different.
In one exemplary embodiment, analyzing the target video stream comprises: judging the state of a target channel corresponding to the target video stream; switching the target channel to a video analysis mode if it is determined that the target channel is in an idle state. In this embodiment, after the detection signal is acquired, it may be determined whether a channel corresponding to the image pickup apparatus associated with the inspection signal is in an analysis state. In the analysis state, the detection signal is discarded. And when the target video stream is not in the analysis state, switching the channel of the target video stream to a video analysis mode, and identifying the event abnormal condition in the target video stream. After the abnormal event is identified, alarm information can be sent out to prompt a user that the abnormal event occurs, and the target video stream and the identification information of the target video stream can be stored to facilitate the retrieval of the abnormal event. The identification information of the target video stream may include the type of the exception, the time and place of capturing the target video stream by the target camera device, and the like. When the target video stream is analyzed, the target video stream can be decoded to obtain a small-interval YUV picture stream, and then the picture stream is detected to determine an abnormal event.
In an exemplary embodiment, before analyzing the target video stream, the method further comprises: obtaining a first number of channels in the video analysis mode and a second number of the target video streams; and feeding back second alarm information under the condition that the first number is smaller than the second number. In this embodiment, before analyzing the target video stream, a first number of channels currently in the video analysis mode may be further obtained, and a second number of the target video stream, when the first number is greater than or equal to the second number, the target video stream may be identified, and when the first number is less than the second number, an alarm message may be sent to prompt the user that there is no idle video analysis channel currently. That is, before analyzing the target video stream, the channels in the video analysis mode may be checked to ensure that the number of video streams that can be simultaneously subjected to anomaly detection is not greater than M, and when the capacity is exceeded, the anomaly information is thrown out, where M is the maximum number of channels that allow simultaneous video analysis.
The following describes an abnormal event recognition method with reference to a specific embodiment:
fig. 5 is a flowchart of an abnormal event recognition method according to an embodiment of the present invention, as shown in fig. 5, the flowchart includes:
in step S502, an external sensor (corresponding to the target detection device) collects a detection signal.
Step S504, accessing the sensor signal (corresponding to the detection signal), and performing channel association according to the user configuration.
In step S506, it is determined whether there is an abnormality, and if the determination result is yes, step S512 is executed, and if the determination result is no, step S518 is executed.
And step S508, accessing N paths of front-end cameras.
Step S510, perform buffering processing on the N paths of video streams.
Step S512, the Y-path video stream with the target is switched to a video analysis state. It should be noted that, before the previous step S512, the steps S508 to S510 need to be executed, i.e., the steps S508 to S510 and the steps S502 to S506 may be parallel.
In step S514, video decoding is performed.
Step S516, the YUV picture stream obtained after decoding is subjected to abnormity detection.
And step S518, ending.
Fig. 6 is a block diagram corresponding to an abnormal event identification method according to an embodiment of the present invention, and as shown in fig. 6, the block includes:
the user end 602 sends the sensor association relationship to the visualization user interaction client 604, where the sensor association relationship may include association relationship and type of the sensor and the camera, signal threshold of the sensor, and the like. When the detection signal of the sensor exceeds the threshold value, the target object in the area where the sensor is located is determined to be present.
The visual user interaction client 604 is configured to receive the sensor association relationship sent by the user side 602, and send the information to the sensor association module.
The sensor association module 606 is configured to receive the sensor association sent by the interactive client 604, store the configured association, receive the detection information sent by the external sensor 608, and send a target notification (corresponding to the detection signal) to the video buffer switching module 610, that is, after receiving the sensor signal, send an exception notification to the video switching module 610 according to the association.
The external sensor 608 is configured to detect information and send a detection signal to the sensor association module 606 when it is determined that the target object exists in the target area.
The video buffer switching module 610 is configured to receive a real-time video sent by the camera 612, and send a target video stream to the abnormal behavior detection module 614 when receiving a target notification.
And a camera 612 for capturing video in real time.
And the abnormal behavior detection module 614 is configured to receive the target video stream, detect whether the video stream is abnormal, and send an abnormal event to the visual user interaction client 604.
In the embodiment, the sensor is combined with the video stream behavior analysis, a low-cost technology is used for filtering a large amount of analysis requirements, limited resources are given to a high-performance technology, the number of analysis channels far exceeding the current hardware capacity is realized, and the cost is greatly reduced. In addition, the video buffering and switching method according to the notification are used, and the normal abnormal event detection rate is still ensured on the premise of optimizing the performance.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, an abnormal event recognition apparatus is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, and the description already made is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 7 is a block diagram of an abnormal event recognition apparatus according to an embodiment of the present invention, as shown in fig. 7, the apparatus including:
a first obtaining module 72, configured to obtain a detection signal sent by a target detection device, where the detection signal is used to indicate that the target detection device detects that a target object appears in a target area;
a first obtaining module 74, configured to obtain a target video stream collected by a target image capturing device, where the target image capturing device is configured to capture an image in the target area to obtain the target video stream;
an identification module 76 for analyzing the target video stream to identify abnormal events in the target area.
The first obtaining module 72 corresponds to the sensor associating module, the second obtaining module 74 corresponds to the video buffer switching module, and the identifying module 76 corresponds to the abnormal behavior detecting module.
In an exemplary embodiment, the second obtaining module 74 may obtain the target video stream captured by the target camera device by: determining the target image pickup device associated with the target detection device based on a predetermined association relationship, wherein the association relationship is used for recording the association relationship between the detection device and one or more image pickup devices; and acquiring the target video stream collected by the target camera equipment associated with the target detection equipment.
In one exemplary embodiment, the apparatus may be configured to, before determining the target image capturing apparatus associated with the target detection apparatus based on a predetermined association relationship, determine the association relationship by at least one of: acquiring the input incidence relation; determining the association relation based on an input device distribution diagram, wherein distribution information of the detection device and the image pickup device is recorded in the device distribution diagram; and storing the association relation.
In an exemplary embodiment, the apparatus may store the association relationship by: checking whether the association relationship is legal or not; and storing the association relation under the condition that the association relation is verified to be legal.
In an exemplary embodiment, the apparatus may be further configured to, after verifying whether the association relationship is legal, feed back first warning information in a case where the association relationship is verified to be illegal.
In an exemplary embodiment, the second obtaining module 74 may obtain the target video stream captured by the target image capturing device by: determining a video stream corresponding to the target camera equipment from a plurality of pre-cached video streams; and determining a video stream within a target time period included in the video stream corresponding to the target camera device as the target video stream.
In an exemplary embodiment, the recognition module 76 may implement the analysis of the target video stream by: judging the state of a target channel corresponding to the target video stream; switching the target channel to a video analysis mode if it is determined that the target channel is in an idle state.
In an exemplary embodiment, the apparatus may be further configured to obtain a first number of channels in the video analysis mode and a second number of channels in the target video stream before analyzing the target video stream; and feeding back second alarm information under the condition that the first number is smaller than the second number.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.
In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.
It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and they may be implemented using program code executable by the computing devices, such that they may be stored in a memory device and executed by the computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into various integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. An abnormal event identification method is characterized by comprising the following steps:
acquiring a detection signal sent by target detection equipment, wherein the detection signal is used for indicating the target detection equipment to detect that a target object appears in a target area;
acquiring a target video stream acquired by target camera equipment, wherein the target camera equipment is used for shooting images in the target area to obtain the target video stream;
analyzing the target video stream to identify an abnormal event in the target area.
2. The method of claim 1, wherein obtaining the target video stream captured by the target camera device comprises:
determining the target image pickup device associated with the target detection device based on a predetermined association relationship, wherein the association relationship is used for recording the association relationship between the detection device and one or more image pickup devices;
and acquiring the target video stream collected by the target camera equipment associated with the target detection equipment.
3. The method according to claim 2, before determining the target image capturing apparatus associated with the target detection apparatus based on a predetermined association relationship, the method further comprising:
determining the association by at least one of: acquiring the input incidence relation; determining the association relation based on an input device distribution diagram, wherein distribution information of the detection device and the image pickup device is recorded in the device distribution diagram;
and storing the association relation.
4. The method of claim 3, wherein storing the association comprises:
checking whether the association relationship is legal or not;
and storing the association relation under the condition that the association relation is verified to be legal.
5. The method of claim 4, wherein after checking whether the association relationship is legal, the method further comprises:
and feeding back first alarm information under the condition that the association relation is verified to be illegal.
6. The method of claim 1, wherein obtaining the target video stream captured by the target camera device comprises:
determining a video stream corresponding to the target camera equipment from a plurality of pre-cached video streams;
and determining a video stream within a target time period included in the video stream corresponding to the target camera device as the target video stream.
7. The method of claim 1, wherein analyzing the target video stream comprises:
judging the state of a target channel corresponding to the target video stream;
switching the target channel to a video analysis mode if it is determined that the target channel is in an idle state.
8. The method of claim 1, wherein prior to analyzing the target video stream, the method further comprises:
acquiring a first number of channels in a video analysis mode and a second number of the target video streams;
and feeding back second alarm information under the condition that the first number is smaller than the second number.
9. An abnormal event recognition apparatus, comprising:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a detection signal sent by target detection equipment, and the detection signal is used for indicating the target detection equipment to detect a target object in a target area;
the second acquisition module is used for acquiring a target video stream acquired by target camera equipment, wherein the target camera equipment is used for shooting images in the target area to obtain the target video stream;
an identification module to analyze the target video stream to identify abnormal events in the target area.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 8 when executed.
11. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 8.
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| CN113992893A (en) * | 2021-10-26 | 2022-01-28 | 福建云知声智能科技有限公司 | Park inspection method and device, storage medium and electronic device |
| CN114708542A (en) * | 2022-06-06 | 2022-07-05 | 阿里巴巴达摩院(杭州)科技有限公司 | Image processing method, image processing apparatus, and computer-readable storage medium |
| CN114913663A (en) * | 2021-02-08 | 2022-08-16 | 腾讯科技（深圳）有限公司 | Anomaly detection method and device, computer equipment and storage medium |
| CN115086621A (en) * | 2022-08-22 | 2022-09-20 | 上海意静信息科技有限公司 | Fire fighting abnormal information determination method, device, equipment and medium |
| CN117313019A (en) * | 2023-11-29 | 2023-12-29 | 广州汇通国信科技有限公司 | Data anomaly detection method based on deep reinforcement learning |
| CN117649642A (en) * | 2024-01-29 | 2024-03-05 | 深圳市瀚晖威视科技有限公司 | Abnormal behavior analysis method and system based on multiple video cameras |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102340625A (en) * | 2010-07-16 | 2012-02-01 | 安讯士有限公司 | Method for event initiated video capturing and a video camera for capture event initiated video |
| WO2015164389A1 (en) * | 2014-04-21 | 2015-10-29 | Blast Motion Inc. | Motion event recognition and video synchronization system and method |
| CN110062198A (en) * | 2018-01-18 | 2019-07-26 | 杭州海康威视数字技术股份有限公司 | A kind of monitoring evidence collecting method, device, system, electronic equipment and storage medium |
| CN110118973A (en) * | 2019-05-27 | 2019-08-13 | 杭州亚美利嘉科技有限公司 | Warehouse Intellisense recognition methods, device and electronic equipment |
| CN111080963A (en) * | 2019-12-18 | 2020-04-28 | 广州穗能通能源科技有限责任公司 | Construction site warning method and device, computer equipment and storage medium |
| CN111260895A (en) * | 2020-02-24 | 2020-06-09 | 上海明略人工智能(集团)有限公司 | Alarm information sending method and device, storage medium and electronic device |
-
2020
- 2020-10-26 CN CN202011158988.6A patent/CN112291520B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102340625A (en) * | 2010-07-16 | 2012-02-01 | 安讯士有限公司 | Method for event initiated video capturing and a video camera for capture event initiated video |
| WO2015164389A1 (en) * | 2014-04-21 | 2015-10-29 | Blast Motion Inc. | Motion event recognition and video synchronization system and method |
| CN110062198A (en) * | 2018-01-18 | 2019-07-26 | 杭州海康威视数字技术股份有限公司 | A kind of monitoring evidence collecting method, device, system, electronic equipment and storage medium |
| CN110118973A (en) * | 2019-05-27 | 2019-08-13 | 杭州亚美利嘉科技有限公司 | Warehouse Intellisense recognition methods, device and electronic equipment |
| CN111080963A (en) * | 2019-12-18 | 2020-04-28 | 广州穗能通能源科技有限责任公司 | Construction site warning method and device, computer equipment and storage medium |
| CN111260895A (en) * | 2020-02-24 | 2020-06-09 | 上海明略人工智能(集团)有限公司 | Alarm information sending method and device, storage medium and electronic device |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114913663A (en) * | 2021-02-08 | 2022-08-16 | 腾讯科技（深圳）有限公司 | Anomaly detection method and device, computer equipment and storage medium |
| CN113450125A (en) * | 2021-07-06 | 2021-09-28 | 北京市商汤科技开发有限公司 | Method and device for generating traceable production data, electronic equipment and storage medium |
| CN113992893A (en) * | 2021-10-26 | 2022-01-28 | 福建云知声智能科技有限公司 | Park inspection method and device, storage medium and electronic device |
| CN114708542A (en) * | 2022-06-06 | 2022-07-05 | 阿里巴巴达摩院(杭州)科技有限公司 | Image processing method, image processing apparatus, and computer-readable storage medium |
| CN115086621A (en) * | 2022-08-22 | 2022-09-20 | 上海意静信息科技有限公司 | Fire fighting abnormal information determination method, device, equipment and medium |
| CN117313019A (en) * | 2023-11-29 | 2023-12-29 | 广州汇通国信科技有限公司 | Data anomaly detection method based on deep reinforcement learning |
| CN117313019B (en) * | 2023-11-29 | 2024-03-22 | 广州汇通国信科技有限公司 | Data anomaly detection method based on deep reinforcement learning |
| CN117649642A (en) * | 2024-01-29 | 2024-03-05 | 深圳市瀚晖威视科技有限公司 | Abnormal behavior analysis method and system based on multiple video cameras |
| CN117649642B (en) * | 2024-01-29 | 2024-04-05 | 深圳市瀚晖威视科技有限公司 | Abnormal behavior analysis method and system based on multiple video cameras |
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