CN108647559A - A kind of danger recognition methods based on deep learning - Google Patents
A kind of danger recognition methods based on deep learning Download PDFInfo
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- CN108647559A CN108647559A CN201810235887.0A CN201810235887A CN108647559A CN 108647559 A CN108647559 A CN 108647559A CN 201810235887 A CN201810235887 A CN 201810235887A CN 108647559 A CN108647559 A CN 108647559A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
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Abstract
The present invention discloses a kind of based on deep learning danger recognition methods, applied to intelligent video technology field, pass through the video front-end equipment real-time image acquisition information of the different location in monitoring area, image information is sent to background server, the deep learning algorithm that server is trained according to cosine analyzes the image collected, obtains the judging result with the presence or absence of danger；When judging dangerous object, then to the corresponding location information of terminal feedback danger and image information；Terminal notifying and early warning；The present invention can maximize the reduction danger identification cost of protection by the way that intelligent danger identifying platform to be arranged in the backstage of video monitoring system without the video monitoring system built up being transformed or being removed reconstruction；And method through the invention can largely save human cost；The image procossing of the present invention can realize the speed of Millisecond, and efficiency is improved for danger identification and early warning processing.
Description
Technical field
It is the invention belongs to intelligent video technology field, more particularly to a kind of that control knife is carried to suspicious people in indoor and outdoor surroundings
The Video Analysis Technology of tool.
Background technology
As what China " safe city " was built gos deep into, the demand in China security protection market will further improve, and become after U.S.
World's second largest security protection market after state.
Advancing by leaps and bounds for technical aspect also provides powerful power for the development of security protection industry, the sixties video image skill
The application of art, the computer digit technology seventies, the biological identification technology eighties and the Internet technology nineties makes peace
Anti- industry is rapidly developed.
In recent years, full-scale digital, the video monitoring system advantage of networking are more apparent, the opening of height, collection
It becomes second nature and flexibility, provides more wide development space for the development of entire security protection industry, and intelligent video monitoring is then
One of the application model of networked video monitoring field forefront.Intelligent video monitoring is to digitize, networked video monitoring is
Basis, but it is different from general networked video monitoring, it is a kind of more high-end video surveillance applications.
Video developing stage：
First stage：Late nineteen seventies are to middle nineteen nineties.This stage is with closed-circuit TV monitoring system (CCTV)
It is main, that is, first generation simulated television video monitoring system.At 2000 or so, have basically been eliminated.
Second stage：Middle nineteen nineties to late nineteen nineties, based on the video monitoring system based on PC machine card insert type,
This stage is also known as the Semi-digital epoch by insider.Or second generation video monitoring system.
Phase III：Late nineteen nineties so far, with embedded technology be rely on, using network, the communication technology as platform, with
It is embedded in camera based on the network video monitor and control system that simple image analysis algorithm is characteristic, since then, Network Video Surveillance
Development also enter digital Age.Haikang at present, big China, what the majority security protection manufacturer such as Kodak provided is all the third generation ---
Networking Video Monitor system, also known as IP monitoring systems.
Fourth stage：Earlier 2000s are so far, simple as the requirement that user analyzes intelligent image is higher and higher
Embedded simple algorithm is analyzed the picture of candid photograph and far can not be met the needs of users in camera.User
Evidence obtaining turns to after requirement for monitoring system has been engaged on prevents in advance.Monitor mode is also by duty in monitoring center with people
It is on duty to be changed into Computerized intelligent.Forth generation intelligent recognition monitoring system is exactly answered such demand and is generated.
The main early warning of conventional third generation video monitoring system is to be needed big by personnel by seeing that monitoring shows that large-size screen monitors are realized
The human input of amount, while to the sense of responsibility of personnel, focus requirement is very high, can only in most cases play post-mordem forensics
Effect, and post-mordem forensics is extremely inefficient.
Other most of manufacturers by algorithm integration in camera, it is simple due to that by camera hardware limitation, can only build
Algorithm does basic handling, cannot achieve intelligent recognition truly.
Invention content
It is preposition in order to solve existing danger recognizer, by camera hardware limitation, simple algorithm can only be built, is done
Basic handling, the problem of cannot achieve intelligent recognition truly, the present invention proposes a kind of danger based on deep learning
Dangerous object recognition methods, realization are detected the controlled knife carrier that whether occurs in environment, to find the above situation
When send out alarm signal in time, notice monitoring personnel acquisition measure processing.
The technical solution adopted by the present invention is：Danger recognition methods based on deep learning, including：
S1, pass through the Video stream information of the corresponding monitoring area of video front-end equipment acquisition mounted on each monitoring area；
The Video stream information includes video image and the corresponding monitoring area of each video image；
Further include carrying out implementing video image information acquisition to monitoring blind area by mobile video acquisition equipment；
S2, the collected Video stream informations of step S1 are stored；
S3, the collected video images of step S1 are analyzed based on deep learning, judgement, which obtains in video image, is
No dangerous object；If thening follow the steps S4；Otherwise continue to analyze；
S4, danger video image and corresponding monitoring area are obtained, sends out alarm.
Further, it pair collects video image described in step S3 to analyze, specifically include：
First, training deep-neural-network, and generate network weight；
Then, collected video image is inputted in neural network；
Finally, the video image of input is identified in neural network, and judgement obtains in the video image it is dangerous
Object；If then positioning specific monitoring area, red block is used in combination to carry out frame choosing to target danger；Otherwise continue to judge next regard
Frequency image.
Further, the danger specifically includes：Flame, controlled knife will include fruit knife, chopper, kitchen knife etc.
Further, the video image of input is identified in the neural network, specifically includes：Using default control
Cutter carrier detction algorithm analyzes video image, judges whether controlled knife carrier occur；Using preset flame
Detection algorithm analyzes video image, judges whether fire behavior occur.
Beneficial effects of the present invention：The present invention based on deep learning danger recognition methods, by being mounted on monitored space
The video front-end equipment real-time image acquisition information of different location, background server, server are sent to by image information in domain
The deep learning algorithm trained according to cosine analyzes the image collected, obtains the judgement knot with the presence or absence of danger
Fruit；When judging dangerous object, then to the corresponding location information of terminal feedback danger and image information；Terminal notifying is simultaneously
Early warning；Method using the present invention can eliminate danger in time, and guarantor gives birth to property safety.
Description of the drawings
Fig. 1 is protocol procedures figure provided in an embodiment of the present invention；
Fig. 2 is system block diagram provided in an embodiment of the present invention.
Specific implementation mode
For ease of those skilled in the art understand that the present invention technology contents, below in conjunction with the accompanying drawings to the content of present invention into one
Step is illustrated.
It is the solution of the present invention flow chart as shown in Figure 1, the technical scheme is that：Known based on deep learning danger
Other method, by the video front-end equipment real-time image acquisition information of the different location in monitoring area, by image information
It is sent to background server, the deep learning algorithm that server is trained according to cosine is analyzed the image collected, obtained
With the presence or absence of the judging result of danger；When judging dangerous object, then to the terminal corresponding location information of feedback danger with
And image information；Terminal notifying and early warning.Specifically include following steps：
S1, pass through the video image of the corresponding monitoring area of video front-end equipment acquisition mounted on each monitoring area；Pass through
It is previously installed at the video front-end equipment real-time image acquisition information of each monitoring area；And the image collected information is passed
It send to background server；
Preferably, acquired image information also needs to be pre-processed, then will be through to promote the preparation rate of image recognition
The image information of processing is sent to background server.
Preferably, the video front-end equipment in the present embodiment is set in addition to realizing the video front mounted on each monitoring area
Standby further includes outside mobile collection equipment, is acquired mainly for the video image of monitoring blind area, and by going on patrol, personnel are hand-held to move
It moves collecting device or the mobile robot comprising video acquisition function in real time adopts the video image for monitoring blind area
Collection, and it is sent to background server after pre-processing；To form the video monitoring covered without dead angle.
S2, the collected video images of step S1 are stored；Background server include data storage server and
Data processing server；Data storage server is stored and is backed up to the image information that video front-end equipment is sent；
Described image information includes video image and the corresponding monitoring area position of the image；
S3, the collected video images of step S1 are analyzed based on deep learning, judgement, which obtains in video image, is
No dangerous object；If thening follow the steps S4；Otherwise continue to analyze；Data processing server is according to deep learning trained
To controlled knife carrier detction algorithm model and fire defector algorithm model image is analyzed, and export judgement
As a result；When judging to obtain in certain image there are when the dangers such as controlled knife or flame, then obtained by data storage server
Take the information such as correspondence image and its monitoring position；
S4, danger video image and corresponding monitoring area are obtained, sends out alarm.According to danger correspondence image and
It monitors the information such as position, alarms to terminal, the terminal in the present embodiment includes：Mobile phone, tablet computer wait.
The algorithm of Most current manufacturer uses traditional image recognition algorithm, is affected with environment, accuracy rate is relatively low
The problem of.And this system uses deep learning algorithm, by mass data training algorithm, arithmetic speed is fast, and accuracy rate is higher.
Most current intelligence manufacturer is preposition by the algorithm of intelligent recognition, causes evidence obtaining after an event occurs difficult, and the side of the present invention
Method intelligent recognition platform is disposed on backstage, and the video of storage can be easily analyzed, and rapid positioning needs the data collected evidence,
To have saved a large amount of time cost.And the present invention by intelligent danger identifying platform by being arranged in video monitoring system
Backstage, without the video monitoring system built up being transformed or being removed reconstruction, can maximize protection reduction danger
Dangerous object identifies cost；And method through the invention can largely save human cost；The image procossing of the present invention can be realized
The speed of Millisecond improves efficiency for danger identification and early warning processing.
The algorithm flow of the training of RetinaNet networks and corresponding formula:
1) the propagated forward stage：Training picture is sent into core network and obtains pyramid feature figure layer, then by feature figure layer
Inlet bit sets Recurrent networks and sorter network.Position Recurrent networks are responsible for predicting the corresponding position regression variable of anchor frame, and count
It calculates and recommends frame.Sorter network then predicts the possibility for recommending to include each target in frame.Whole process train picture with
The form of data flow flows through each convolutional layer and pond layer.Specific l layers of convolution kernel is flowed through for data flow, mathematic(al) representation is such as
Shown in formula (1).
Wherein, xl(i, j) is the output of l layers of filter, xl-1(i, j) is the output of last layer, kl(m, n) is l layers of filter
Wave device, blIt is the corresponding amount of bias of l layers of filter, f () is activation primitive.
When data flow through l layers of pond layer, shown in date expression (2).
Xl=f (bl+β·down(xl-1)) (2)
Wherein, xl(i, j) is the output of l layers of pond layer, xl-1(i, j) is the output of last layer, the input of this layer, and β is
The weight of pond layer, f () are activation primitive, down () down-sampling function.The input of down-sampling function is characterized figure n × n
Pixel on region.For maximum pond layer, down () chooses the maximum value in n × n-pixel, and it is input to export dimension
The characteristic pattern of characteristic pattern dimension 1n ' 1n.
2) the back-propagating stage：The weighted value of each layer is corrected using gradient descent method.It calculates first and recommends frame right with its
The total losses with nominal data of classification should be predicted, shown in loss function such as formula (3).
Ltotal=Lcls+λ·Lloc (3)
Wherein, LclsClassification Loss function, i.e. Focal loss functions.LlocIt is that frame returns loss function, with specific reference to text
Offer [R.Girshick.Fast R-CNN [c] //CVPR:The IEEE Conference on Computer Vision and
Pattern Recognition,New York,IEEE,2015:1440-14478.].The effect of lambda parameter is balanced sort and side
Frame returns loss function.
Then gradient of the total losses to each weight is successively calculated, finally optimizes weight with gradient descent method.To convolutional layer
The mathematic(al) representation such as formula (4) of optimization weight optimizes the mathematic(al) representation such as formula (5) of weight for pond layer.
Wherein, wl(i) it is the weighted value of specific l layers of filter ith, wl(i+1) it is the newer weighted value of i+1 time.α
For learning rate.To variable bl、βlOptimize renewal process and wlEqually.
The test of RetinaNet networks：RetinaNet is almost the same with trained propagated forward in test pictures.Only
It is finally to use non-maximum suppression algorithm [J.Hosang, R.Benenson, and B.Schiele.Learning non-
maximum suppression[EB/OL].[2017-05-09].https://arxiv.org/pdf/
1705.02950.pdf.] merge the recommendation frame being overlapped.
The one kind for being illustrated in figure 2 the offer of the present invention is based on deep learning danger identifying system, including：Video front
Equipment, Distributor, Algorithm Analysis server, Resource Server, storage server, database and application server, visitor
Family end；
Video front-end equipment acquires video image information in real time；During the video image information of acquisition is carried out through Distributor
Turn, is sent to Algorithm Analysis server；
The Algorithm Analysis server includes：Video decoding module, analysis module, sending module and reception mould
Block；
Start analysis module, just send a request for carrying video analysis program ID from trend Resource Server,
Resource Server returns to one according to device numbering and is taken with video front-end equipment number, the corresponding distribution of video front-end equipment
Be engaged in device IP, Distributor port the first information to analysis module.
Analysis module calls Video decoding module after obtaining the first information, and Video decoding module is according to Distributor
IP, Distributor port obtain it on corresponding Distributor and correspond to the Video stream information of video front-end equipment.
The video file that the Video stream information got is transcoded into YVU420 formats by Video decoding module is transmitted to video point
Analysis module is analyzed.If there is analysis result just preserves result, at xml document, (that deposits inside xml document is picture and regards
Frequency is analyzing program institute store path on computers), jpg pictures (3 continuous shootings when 3 events generations), avi video (things
Video file when part occurs), preserving rear video analysis module can call a sending module to be sent out to corresponding receiving module
Send xml files.
Receiving module can parse the xml document sended over from sending module, exist to obtain picture and video
The storage address on Algorithm Analysis server where analysis module, following receiving module can deposit picture and video
On a ftp server (because analysis module where Algorithm Analysis server on will not store for a long time picture and
Video can be deleted periodically, so there are the times on a ftp server, preserved on ftp server picture and video
It comparatively can be longer).The path being stored on ftp server is preserved to oracle database.
The data of last typing will be illustrated in the display equipment of client, the early warning figure in the display equipment of client
Piece and video are that picture on ftp server, video storage address are accessed by the form of http to be shown.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.For ability
For the technical staff in domain, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made
Any modification, equivalent substitution, improvement and etc., should be included within scope of the presently claimed invention.
Claims (5)
1. the danger recognition methods based on deep learning, which is characterized in that including：
S1, pass through the Video stream information of the corresponding monitoring area of video front-end equipment acquisition mounted on each monitoring area；
The Video stream information includes video image and the corresponding monitoring area of each video image；
S2, the collected Video stream informations of step S1 are stored；
S3, the collected video images of step S1 are analyzed based on deep learning, judgement obtains in video image whether deposit
In danger；If thening follow the steps S4；Otherwise continue to analyze；
S4, danger video image and corresponding monitoring area are obtained, sends out alarm.
2. the danger recognition methods according to claim 1 based on deep learning, which is characterized in that further include passing through shifting
Dynamic video capture device carries out implementing video image information acquisition to monitoring blind area.
3. the danger recognition methods according to claim 1 based on deep learning, which is characterized in that right described in step S3
It collects video image to be analyzed, specifically include：
First, training deep-neural-network, and generate network weight；
Then, collected video image is inputted in neural network；
Finally, the video image of input is identified in neural network, and judgement obtains in the video image it is dangerous object；If
It is the video image for exporting dangerous object；Otherwise continue to judge next video image.
4. the danger recognition methods according to claim 1 based on deep learning, which is characterized in that the danger is extremely
Include controlled knife, fire behavior less.
5. the danger recognition methods according to claim 4 based on deep learning, which is characterized in that the neural network
The video image of input is identified, is specifically included：Using default controlled knife carrier detction algorithm to video image into
Row analysis, judges whether controlled knife carrier occur；Video image is analyzed using preset flame detection algorithm, is judged
Whether fire behavior is occurred.
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| CN110472635A (en) * | 2019-07-19 | 2019-11-19 | 西北工业大学 | Tool characteristics recognition methods based on deep learning |
| CN110516529A (en) * | 2019-07-09 | 2019-11-29 | 杭州电子科技大学 | It is a kind of that detection method and system are fed based on deep learning image procossing |
| CN110996067A (en) * | 2019-12-19 | 2020-04-10 | 哈尔滨融智爱科智能科技有限公司 | Personnel safety real-time intelligent video monitoring system under high-risk operation environment based on deep learning |
| CN111027491A (en) * | 2019-12-12 | 2020-04-17 | 江西洪都航空工业集团有限责任公司 | Flame detection method and device based on anchor-free mechanism |
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| CN111414829A (en) * | 2020-03-13 | 2020-07-14 | 珠海格力电器股份有限公司 | Method and device for sending alarm information |
| CN111593891A (en) * | 2020-04-26 | 2020-08-28 | 中联重科股份有限公司 | Safety early warning method and device for engineering mechanical arm frame equipment and engineering machine |
| CN112017384A (en) * | 2020-08-05 | 2020-12-01 | 山东大学 | Automatic alarm method and system for real-time area monitoring |
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| CN110516529A (en) * | 2019-07-09 | 2019-11-29 | 杭州电子科技大学 | It is a kind of that detection method and system are fed based on deep learning image procossing |
| CN110472635A (en) * | 2019-07-19 | 2019-11-19 | 西北工业大学 | Tool characteristics recognition methods based on deep learning |
| WO2021114221A1 (en) * | 2019-12-12 | 2021-06-17 | 江西洪都航空工业集团有限责任公司 | Non-anchor mechanism-based flame detection method and apparatus |
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| CN110996067A (en) * | 2019-12-19 | 2020-04-10 | 哈尔滨融智爱科智能科技有限公司 | Personnel safety real-time intelligent video monitoring system under high-risk operation environment based on deep learning |
| CN111341068A (en) * | 2020-03-02 | 2020-06-26 | 北京四利通控制技术股份有限公司 | Drilling site dangerous area early warning system and method based on deep learning |
| CN111341068B (en) * | 2020-03-02 | 2022-04-08 | 北京四利通控制技术股份有限公司 | Construction method of drilling site dangerous area early warning system based on deep learning |
| CN111414829A (en) * | 2020-03-13 | 2020-07-14 | 珠海格力电器股份有限公司 | Method and device for sending alarm information |
| CN111414829B (en) * | 2020-03-13 | 2024-03-15 | 珠海格力电器股份有限公司 | Method and device for sending alarm information |
| CN111593891A (en) * | 2020-04-26 | 2020-08-28 | 中联重科股份有限公司 | Safety early warning method and device for engineering mechanical arm frame equipment and engineering machine |
| CN113206977A (en) * | 2020-04-28 | 2021-08-03 | 中国石油天然气股份有限公司 | Inspection monitoring method and device for gas transmission station and storage medium |
| CN113206977B (en) * | 2020-04-28 | 2023-07-04 | 中国石油天然气股份有限公司 | Inspection monitoring method and device for gas station and storage medium |
| CN112017384A (en) * | 2020-08-05 | 2020-12-01 | 山东大学 | Automatic alarm method and system for real-time area monitoring |
| CN113139738A (en) * | 2021-05-06 | 2021-07-20 | 马鞍山钢铁股份有限公司 | Method for carrying out environment-friendly unorganized emission supervision by using machine learning |
| CN113596398A (en) * | 2021-07-27 | 2021-11-02 | 金鹏电子信息机器有限公司 | Artificial intelligent security monitoring system and monitoring method |
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Application publication date: 20181012 |