CN110379125A - Cross the border recognition methods, system and relevant apparatus for a kind of danger zone - Google Patents
Cross the border recognition methods, system and relevant apparatus for a kind of danger zone Download PDFInfo
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- CN110379125A CN110379125A CN201910672330.8A CN201910672330A CN110379125A CN 110379125 A CN110379125 A CN 110379125A CN 201910672330 A CN201910672330 A CN 201910672330A CN 110379125 A CN110379125 A CN 110379125A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/0202—Child monitoring systems using a transmitter-receiver system carried by the parent and the child
- G08B21/0205—Specific application combined with child monitoring using a transmitter-receiver system
- G08B21/0208—Combination with audio or video communication, e.g. combination with "baby phone" function
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/0202—Child monitoring systems using a transmitter-receiver system carried by the parent and the child
- G08B21/0261—System arrangements wherein the object is to detect trespassing over a fixed physical boundary, e.g. the end of a garden
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Abstract
A kind of danger zone provided herein is crossed the border recognition methods, and GPU calculation server is applied to, comprising: obtains target object behavior video；Wherein, target object behavior video is behavior video of the target object in the region comprising presetting danger zone；Visual identity is carried out to target object behavior video using preset deep learning model, obtain recognition result and executes corresponding warning operation according to recognition result.This method carries out visual identity to target object behavior video using preset deep learning model, obtains recognition result and executes corresponding warning operation according to recognition result.Wherein, target object behavior video is behavior video of the target object in the region comprising presetting danger zone.This method can carry out danger zone in time and cross the border identification, and then avoid that safety accident occurs.Cross the border identifying system, GPU calculation server and computer readable storage medium for a kind of danger zone provided herein, all has above-mentioned beneficial effect.
Description
Technical field
Cross the border identification field this application involves danger zone, in particular to a kind of danger zone cross the border recognition methods, system,
GPU calculation server and computer readable storage medium.
Background technique
Before the monitoring and safety accident generation of the personnel's unsafe acts occurred in infrastructure project process of construction
Early warning and precautionary measures are particularly important in infrastructure project process of construction.
In operation, substation is carried out in infrastructure project work progress, if someone is in danger zone (as electrification is spaced and is set
It is standby) activity, then it is likely to result in great human casualty accident, traditional fence form and common infrared emission technology can
To carry out a degree of prompting, but if personnel will enter or come into danger zone, at this moment infrared emission has been
Effect is lost, but it is at this moment exactly most dangerous.Secondly, traditional fence form and infrared emission can only judge be
No someone, but can not know is whom, and examination and safety education can not be also carried out to it.
Therefore, danger zone how is carried out in time to cross the border identification, and then avoids that safety accident occurs to be art technology
The technical issues of personnel's urgent need to resolve.
Summary of the invention
The purpose of the application is to provide a kind of danger zone and crosses the border recognition methods, system, GPU calculation server and computer
Readable storage medium storing program for executing can carry out danger zone in time and cross the border identification, and then avoid that safety accident occurs.
It crosses the border recognition methods in order to solve the above technical problems, the application provides a kind of danger zone, is applied to GPU and calculates clothes
Business device, comprising:
Obtain target object behavior video；Wherein, the target object behavior video is target object comprising default danger
Behavior video in the region in danger zone domain；
Visual identity is carried out to the target object behavior video using preset deep learning model, obtains recognition result
And corresponding warning operation is executed according to the recognition result.
Preferably, the acquisition target object behavior video, comprising:
Receive the target object behavior video of camera shooting overhead pass.
Preferably, which crosses the border recognition methods further include:
Receive the alarm command for preliminary recognition result that the camera is sent；Wherein, the preliminary recognition result
For the camera based on edge calculations to the recognition result of the target object behavior video, the alarm command is according to institute
State the instruction that preliminary recognition result determines that the target object has behavior of crossing the border and generates；
Alarm is triggered according to the alarm command to alarm.
Preferably, described to obtain recognition result and executed after corresponding warning operates according to the recognition result, also wrap
It includes:
Obtain screenshot and/or video clip from the target object behavior video, and by the screenshot and/or the view
Frequency segment is sent to operation system.
Preferably, which crosses the border recognition methods further include:
Filter out target screenshot or target video segment respectively from the screenshot or the video clip；
Model training is carried out using the target screenshot or the target video segment, and using the model after training as institute
State deep learning model.
Preferably, which crosses the border recognition methods further include:
According to the demand of input, the default danger zone is reset.
Preferably, danger zone recognition methods of crossing the border includes:
The GPU calculation server, the camera and the alarm are in same local area network.
The application also provides a kind of danger zone and crosses the border identifying system, is applied to GPU calculation server, comprising:
Target object behavior video acquiring module, for obtaining target object behavior video；Wherein, the target object row
It is behavior video of the target object in the region comprising presetting danger zone for video；
Visual identity module, for carrying out vision to the target object behavior video using preset deep learning model
Identification obtains recognition result and executes corresponding warning operation according to the recognition result.
The application also provides a kind of GPU calculation server, comprising:
Memory and processor；Wherein, the memory is for storing computer program, and the processor is for executing institute
Realized when stating computer program danger zone described above cross the border recognition methods the step of.
The application also provides a kind of computer readable storage medium, and the computer-readable recording medium storage has computer
Program, the computer program realized when being executed by processor danger zone described above cross the border recognition methods the step of.
A kind of danger zone provided herein is crossed the border recognition methods, and GPU calculation server is applied to, comprising: is obtained
Target object behavior video；Wherein, the target object behavior video is target object in the region comprising presetting danger zone
Interior behavior video；Visual identity is carried out to the target object behavior video using preset deep learning model, is known
Other result simultaneously executes corresponding warning operation according to the recognition result.
This method carries out visual identity to target object behavior video using preset deep learning model, obtains identification knot
Fruit simultaneously executes corresponding warning operation according to recognition result.Wherein, target object behavior video is target object comprising default
Behavior video in the region of danger zone.This method can carry out danger zone in time and cross the border identification, and then avoid occurring
Safety accident.A kind of danger zone provided herein is crossed the border identifying system, GPU calculation server and computer-readable storage
Medium all has above-mentioned beneficial effect, and details are not described herein.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of application for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is that a kind of danger zone provided by the embodiment of the present application is crossed the border the flow chart of recognition methods；
Fig. 2 is a kind of optimum embodiment flow diagram provided by the embodiment of the present application；
Fig. 3 is that a kind of danger zone provided by the embodiment of the present application is crossed the border the structural block diagram of identifying system.
Specific embodiment
The core of the application is to provide a kind of danger zone and crosses the border recognition methods, can carry out in time danger zone and cross the border
Identification, and then avoid that safety accident occurs.Another core of the application is to provide a kind of danger zone and crosses the border identifying system, GPU
Calculation server and computer readable storage medium.
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
In attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is
Some embodiments of the present application, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall in the protection scope of this application.
Before the monitoring and safety accident generation of the personnel's unsafe acts occurred in infrastructure project process of construction
Early warning and precautionary measures are particularly important in infrastructure project process of construction.
In operation, substation is carried out in infrastructure project work progress, if someone is in danger zone (as electrification is spaced and is set
It is standby) activity, then it is likely to result in great human casualty accident, traditional fence form and common infrared emission technology can
To carry out a degree of prompting, but if personnel will enter or come into danger zone, at this moment infrared emission has been
Effect is lost, but it is at this moment exactly most dangerous.Secondly, traditional fence form and infrared emission can only judge be
No someone, but can not know is whom, and examination and safety education can not be also carried out to it.A kind of danger zone provided by the present application
It crosses the border recognition methods, danger zone can be carried out in time and crossed the border identification, and then avoid that safety accident occurs.Specifically please refer to figure
1, Fig. 1 crosses the border the flow chart of recognition methods for a kind of danger zone provided by the embodiment of the present application, which crosses the border knowledge
Other method specifically includes:
S101, target object behavior video is obtained；Wherein, target object behavior video is target object comprising default danger
Behavior video in the region in danger zone domain；
The executing subject of the present embodiment is GPU calculation server, and GPU calculation server obtains target object behavior video,
The target object behavior video is behavior video of the target object in the region comprising presetting danger zone.Herein for target
The acquisition equipment of object behavior video is also not especially limited, and in some embodiments, which is specially camera.?
This is not especially limited target object, which can be people, can also be animal.Herein for presetting danger zone
Also it is not especially limited, which is preset danger zone, such as charging equipment region.And it is somebody's turn to do
Default danger zone is included in the pickup area of the acquisition equipment of target object behavior video, i.e., default danger zone is packet
Contained in region shown by target object behavior video.
S102, visual identity is carried out to target object behavior video using preset deep learning model, obtains identification knot
Fruit simultaneously executes corresponding warning operation according to recognition result.
Visual identity is carried out to target object behavior video using preset deep learning model in the present embodiment, is known
Other result simultaneously executes corresponding warning operation according to recognition result；Wherein, deep learning model is obtained by model training.
Since the present embodiment is not construed as limiting the target object concrete behavior in target object behavior video, therefore for recognition result
It is not especially limited, as long as meeting the actual needs.For example, recognition result can be close default danger zone boundary, it can
Think and cross default danger zone boundary, danger zone can also be preset to have entered.For different recognition result execution pair
The warning operation answered, warning operation can be alarm device and alarm, and can also be that voice broadcast alerts.
This method carries out visual identity to target object behavior video using preset deep learning model, obtains identification knot
Fruit simultaneously executes corresponding warning operation according to recognition result.Wherein, target object behavior video is target object comprising default
Behavior video in the region of danger zone.This method can carry out danger zone in time and cross the border identification, and then avoid occurring
Safety accident.
Based on the above embodiment, above-mentioned acquisition target object behavior video in the present embodiment, comprising: receive camera shooting overhead pass
Target object behavior video.It is camera, and the camera shooting embodiment defines the acquisition equipment of target object behavior video
Overhead pass target object behavior video is to GPU calculation server.The camera usually acquires video in real time, and frequency acquisition is not made to have
Body limits, and corresponding setting should be made according to the actual situation by those skilled in the art.
Based on the above embodiment, in the present embodiment further include: receive the report for preliminary recognition result that camera is sent
Alert instruction；Wherein, preliminary recognition result be camera based on edge calculations to the recognition result of target object behavior video, alarm
Instruction is to determine that target object has behavior of crossing the border and the instruction generated according to preliminary recognition result；It is triggered and is reported according to alarm command
Alert device is alarmed.Specifically, camera acquires video in real time, and identification preliminary in real time is carried out based on edge calculations, if there is
It crosses the border behavior, then sends alarm command and give GPU calculation server, alarmed by GPU calculation server triggering alarm.Camera shooting
After head carries out preliminary analysis to video, video can also return to GPU calculation server in real time, be will do it herein than more complete
Visual identity can equally trigger alarm if there is the behavior of crossing the border and alarm.
Based on the above embodiment, above-mentioned in the present embodiment to obtain recognition result and execute corresponding warning according to recognition result
After operation, further includes: obtain screenshot and/or video clip from target object behavior video, and by screenshot and/or piece of video
Section is sent to operation system.The quantity of screenshot, video clip is not especially limited herein, the duration of each video clip
Also it is not especially limited, corresponding setting should be made according to the actual situation by those skilled in the art.Specifically, it is calculated in GPU
When server triggers are alarmed, while it can send corresponding screenshot or segment video in operation system by network,
Perhaps business personnel can check in small routine or examine etc. to personnel administrative staff.It is understood that
The present embodiment can identify personnel by screenshot, video clip, can identify specific identity information.
Based on the above embodiment, danger zone is crossed the border recognition methods in the present embodiment further include: from screenshot or video clip
It is middle to filter out target screenshot or target video segment respectively；Model training is carried out using target screenshot or target video segment, and
Using the model after training as deep learning model.Herein the screening criteria for target screenshot or target video segment and respectively
The quantity filtered out is not especially limited, and corresponding setting should be made according to the actual situation by those skilled in the art.This reality
It applies example and carries out model training using target screenshot or target video segment, and using the model after training as deep learning model,
Namely the deep learning model being deployed on GPU calculation server is updated.Specifically, problematic in operation system
Screenshot or small video, can be labeled offline, and carry out the training of deep learning model, trained model can be with
It updates in GPU calculation server, identification subsequent in this way can enable new identification model.
Based on the above embodiment, danger zone is crossed the border recognition methods in the present embodiment further include: according to the demand of input, weight
It is new that default danger zone is set.It is understood that the present embodiment can be based on demand, updates and default danger zone is set.?
This is not especially limited the input mode of demand, should be made according to the actual situation by those skilled in the art and be set accordingly
It is fixed.Specifically, danger zone can be arranged in management backstage in administrator, and operation system can be sent to GPU by network for being arranged
In the edge calculations module of calculation server and camera.
Based on the above embodiment, in the present embodiment danger zone cross the border recognition methods include: GPU calculation server, camera shooting
Head and alarm are in same local area network.Specifically, GPU calculation server, camera and alarm are all in the same office
In the net of domain, network delay is reduced to greatest extent.In addition, linked between GPU calculation server and operation system by public network,
Using coded communication, information leakage is avoided.
It is illustrated below with an optimum embodiment, it is specific referring to FIG. 2, Fig. 2 is provided by the embodiment of the present application
A kind of optimum embodiment flow diagram, as shown in Fig. 2, left side maximum dotted line frame indicates that all the components form a danger in frame
Region is crossed the border identifying system, has a lesser dotted line frame to indicate the alarm in the dotted line frame, take the photograph in the maximum dotted line frame
As head and GPU calculation server are in same local area network.Video or instruction are sent to GPU calculation server, GPU by camera
Calculation server can alarm according to instruction triggers alarm.When GPU calculation server is triggered and is alarmed, meeting simultaneously
It sends corresponding picture or segment video or instruction in operation system by network, leader or business personnel can be in little Cheng
It is checked in sequence, or personnel is examined etc..Danger zone, industry can be arranged in management backstage in administrator or relevant people
Business system can be sent to being arranged in the edge calculations module of GPU calculation server and camera by network.Operation system will
Problematic picture or video export can carry out offline sample mark, and take in offline GPU server and model training
Be engaged in device on carry out deep learning model training, trained model, which can update, to be deployed in GPU calculation server, in this way after
Continuous identification can enable new identification model.
It crosses the border identifying system, GPU calculation server and calculating to a kind of danger zone provided by the embodiments of the present application below
Machine readable storage medium storing program for executing is introduced, and identifying system, GPU calculation server and computer are crossed the border in danger zone described below can
Reference can be corresponded to each other by reading the recognition methods of crossing the border of storage medium and above-described danger zone.
Referring to FIG. 3, Fig. 3 is that a kind of danger zone provided by the embodiment of the present application is crossed the border the structural frames of identifying system
Figure；Danger zone identifying system of crossing the border includes:
Target object behavior video acquiring module 301, for obtaining target object behavior video；Wherein, target object row
It is behavior video of the target object in the region comprising presetting danger zone for video；
Visual identity module 302, for carrying out vision to target object behavior video using preset deep learning model
Identification obtains recognition result and executes corresponding warning operation according to recognition result.
Based on the above embodiment, the video acquiring module of target object behavior described in the present embodiment 301, comprising:
Target object behavior video acquisition unit, for receiving the target object behavior video of camera shooting overhead pass.
Based on the above embodiment, danger zone is crossed the border identifying system in the present embodiment further include:
Alarm command receiving module, the alarm command for preliminary recognition result sent for receiving the camera；
Wherein, the preliminary recognition result be the camera based on edge calculations to the identification knot of the target object behavior video
Fruit, the alarm command are to determine that the target object has behavior of crossing the border and the finger generated according to the preliminary recognition result
It enables；
Alarm triggering module is alarmed for triggering alarm according to the alarm command.
Based on the above embodiment, danger zone is crossed the border identifying system in the present embodiment further include:
Screenshot and/or video clip obtain module, for obtaining screenshot and/or view from the target object behavior video
Frequency segment, and operation system is sent by the screenshot and/or the video clip.
Based on the above embodiment, danger zone is crossed the border identifying system in the present embodiment further include:
Screening module, for filtering out target screenshot or target video piece respectively from the screenshot or the video clip
Section；
Model training module, for carrying out model training using the target screenshot or the target video segment, and will
Model after training is as the deep learning model.
Based on the above embodiment, danger zone is crossed the border identifying system in the present embodiment further include:
Danger zone setup module resets the default danger zone for the demand according to input.
The application also provides a kind of GPU calculation server, comprising: memory and processor；Wherein, memory is for storing
Computer program, processor is for realizing that the danger zone of above-mentioned any embodiment is crossed the border recognition methods when executing computer program
The step of.
The application also provides a kind of computer readable storage medium, and computer-readable recording medium storage has computer journey
Sequence, realized when computer program is executed by processor the danger zone of above-mentioned any embodiment cross the border recognition methods the step of.
The computer readable storage medium may include: USB flash disk, mobile hard disk, read-only memory (Read-Only
Memory, ROM), random access memory (Random Access Memory, RAM), magnetic or disk etc. is various to deposit
Store up the medium of program code.
Each embodiment is described in a progressive manner in specification, the highlights of each of the examples are with other realities
The difference of example is applied, the same or similar parts in each embodiment may refer to each other.For embodiment provide system and
Speech, since it is corresponding with the method that embodiment provides, so being described relatively simple, related place is referring to method part illustration
?.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Profession
Technical staff can use different methods to achieve the described function each specific application, but this realization is not answered
Think beyond the scope of this invention.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
It crosses the border recognition methods, system, GPU calculation server and calculating to a kind of danger zone provided herein above
Machine readable storage medium storing program for executing is described in detail.Specific case used herein carries out the principle and embodiment of the application
It illustrates, the description of the example is only used to help understand the method for the present application and its core ideas.It should be pointed out that for
For those skilled in the art, under the premise of not departing from the application principle, if can also be carried out to the application
Dry improvement and modification, these improvement and modification are also fallen into the protection scope of the claim of this application.
Claims (10)
- The recognition methods 1. a kind of danger zone is crossed the border, which is characterized in that be applied to GPU calculation server, comprising:Obtain target object behavior video；Wherein, the target object behavior video is target object comprising presetting danger area Behavior video in the region in domain；Using preset deep learning model to the target object behavior video carry out visual identity, obtain recognition result and according to Corresponding warning operation is executed according to the recognition result.
- The recognition methods 2. danger zone according to claim 1 is crossed the border, which is characterized in that the acquisition target object behavior Video, comprising:Receive the target object behavior video of camera shooting overhead pass.
- The recognition methods 3. danger zone according to claim 2 is crossed the border, which is characterized in that further include:Receive the alarm command for preliminary recognition result that the camera is sent；Wherein, the preliminary recognition result is institute Recognition result of the camera based on edge calculations to the target object behavior video is stated, the alarm command is according to described first Step recognition result determines the instruction that the target object has behavior of crossing the border and generates；Alarm is triggered according to the alarm command to alarm.
- The recognition methods 4. danger zone according to claim 3 is crossed the border, which is characterized in that it is described obtain recognition result and according to After the corresponding warning operation of recognition result execution, further includes:Obtain screenshot and/or video clip from the target object behavior video, and by the screenshot and/or the piece of video Section is sent to operation system.
- The recognition methods 5. danger zone according to claim 4 is crossed the border, which is characterized in that further include:Filter out target screenshot or target video segment respectively from the screenshot or the video clip；Model training is carried out using the target screenshot or the target video segment, and using the model after training as the depth Spend learning model.
- The recognition methods 6. danger zone according to claim 5 is crossed the border, which is characterized in that further include:According to the demand of input, the default danger zone is reset.
- The recognition methods 7. danger zone according to claim 6 is crossed the border characterized by comprisingThe GPU calculation server, the camera and the alarm are in same local area network.
- The identifying system 8. a kind of danger zone is crossed the border, which is characterized in that be applied to GPU calculation server, comprising:Target object behavior video acquiring module, for obtaining target object behavior video；Wherein, the target object behavior view Frequency is behavior video of the target object in the region comprising presetting danger zone；Visual identity module, for carrying out vision knowledge to the target object behavior video using preset deep learning model Not, it obtains recognition result and executes corresponding warning operation according to the recognition result.
- 9. a kind of GPU calculation server characterized by comprisingMemory and processor；Wherein, the memory is for storing computer program, the processor by execute it is described based on Realized when calculation machine program danger zone as described in any one of claim 1 to 7 cross the border recognition methods the step of.
- 10. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has computer journey Sequence, the computer program realize that danger zone as described in any one of claim 1 to 7 is crossed the border identification when being executed by processor The step of method.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110930657A (en) * | 2019-11-29 | 2020-03-27 | 万翼科技有限公司 | Danger warning method and related equipment |
| CN111092491A (en) * | 2019-12-27 | 2020-05-01 | 国网河南省电力公司检修公司 | Transformer substation supervision method and device and computer readable storage medium |
| CN112418004A (en) * | 2020-11-05 | 2021-02-26 | 烟台艾睿光电科技有限公司 | Fire safety detection method and related components |
| CN112907867A (en) * | 2021-01-22 | 2021-06-04 | 深圳英飞拓科技股份有限公司 | Early warning method and device based on image recognition and server |
| CN113420725A (en) * | 2021-08-20 | 2021-09-21 | 天津所托瑞安汽车科技有限公司 | Method, device, system and storage medium for identifying false alarm scenes of BSD (backup service discovery) product |
| CN116580530A (en) * | 2023-05-26 | 2023-08-11 | 江西省宏旺科技有限公司 | Dangerous behavior early warning method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011036414A1 (en) * | 2009-09-28 | 2011-03-31 | Université Du Sud Toulon-Var | System and method for detecting the presence of a subject in a sensitive area |
| CN107818651A (en) * | 2017-10-27 | 2018-03-20 | 华润电力技术研究院有限公司 | A kind of illegal cross-border warning method and device based on video monitoring |
| CN109002744A (en) * | 2017-06-06 | 2018-12-14 | 中兴通讯股份有限公司 | Image-recognizing method, device and video monitoring equipment |
| CN109410496A (en) * | 2018-10-25 | 2019-03-01 | 北京交通大学 | Attack early warning method, apparatus and electronic equipment |
| CN109712360A (en) * | 2018-12-24 | 2019-05-03 | 衢州市林森消防设备有限公司 | A kind of positioning and tracking type alarming device and method |
-
2019
- 2019-07-24 CN CN201910672330.8A patent/CN110379125A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011036414A1 (en) * | 2009-09-28 | 2011-03-31 | Université Du Sud Toulon-Var | System and method for detecting the presence of a subject in a sensitive area |
| CN109002744A (en) * | 2017-06-06 | 2018-12-14 | 中兴通讯股份有限公司 | Image-recognizing method, device and video monitoring equipment |
| CN107818651A (en) * | 2017-10-27 | 2018-03-20 | 华润电力技术研究院有限公司 | A kind of illegal cross-border warning method and device based on video monitoring |
| CN109410496A (en) * | 2018-10-25 | 2019-03-01 | 北京交通大学 | Attack early warning method, apparatus and electronic equipment |
| CN109712360A (en) * | 2018-12-24 | 2019-05-03 | 衢州市林森消防设备有限公司 | A kind of positioning and tracking type alarming device and method |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110930657A (en) * | 2019-11-29 | 2020-03-27 | 万翼科技有限公司 | Danger warning method and related equipment |
| CN111092491A (en) * | 2019-12-27 | 2020-05-01 | 国网河南省电力公司检修公司 | Transformer substation supervision method and device and computer readable storage medium |
| CN112418004A (en) * | 2020-11-05 | 2021-02-26 | 烟台艾睿光电科技有限公司 | Fire safety detection method and related components |
| CN112907867A (en) * | 2021-01-22 | 2021-06-04 | 深圳英飞拓科技股份有限公司 | Early warning method and device based on image recognition and server |
| CN112907867B (en) * | 2021-01-22 | 2022-10-25 | 深圳英飞拓科技股份有限公司 | Early warning method and device based on image recognition and server |
| CN113420725A (en) * | 2021-08-20 | 2021-09-21 | 天津所托瑞安汽车科技有限公司 | Method, device, system and storage medium for identifying false alarm scenes of BSD (backup service discovery) product |
| CN113420725B (en) * | 2021-08-20 | 2021-12-31 | 天津所托瑞安汽车科技有限公司 | Method, device, system and storage medium for identifying false alarm scenes of BSD (backup service discovery) product |
| CN116580530A (en) * | 2023-05-26 | 2023-08-11 | 江西省宏旺科技有限公司 | Dangerous behavior early warning method and device |
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Application publication date: 20191025 |