CN202904792U - Intelligent visualized alarm system - Google Patents
Intelligent visualized alarm systemDownload PDFInfo
- Publication number
- CN202904792U CN202904792UCN 201220640744CN201220640744UCN202904792UCN 202904792 UCN202904792 UCN 202904792UCN 201220640744CN201220640744CN 201220640744CN 201220640744 UCN201220640744 UCN 201220640744UCN 202904792 UCN202904792 UCN 202904792U
- Authority
- CN
- China
- Prior art keywords
- video
- alarm
- confidence
- analysis
- degree
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Lifetime
Links
- 238000001514detection methodMethods0.000claimsabstractdescription50
- 238000000034methodMethods0.000claimsdescription44
- 230000008569processEffects0.000claimsdescription22
- 230000000007visual effectEffects0.000claimsdescription19
- 230000035939shockEffects0.000claimsdescription3
- 238000004458analytical methodMethods0.000abstractdescription53
- 238000005516engineering processMethods0.000abstractdescription9
- 230000007613environmental effectEffects0.000abstractdescription6
- 238000010191image analysisMethods0.000abstractdescription5
- 230000008901benefitEffects0.000abstractdescription4
- 230000003247decreasing effectEffects0.000abstract1
- 230000008878couplingEffects0.000description8
- 238000010168coupling processMethods0.000description8
- 238000005859coupling reactionMethods0.000description8
- 238000012937correctionMethods0.000description7
- 238000012544monitoring processMethods0.000description7
- 230000001960triggered effectEffects0.000description7
- 238000003786synthesis reactionMethods0.000description4
- 238000004454trace mineral analysisMethods0.000description4
- 238000004364calculation methodMethods0.000description2
- 238000004891communicationMethods0.000description2
- 230000005021gaitEffects0.000description2
- 238000012986modificationMethods0.000description2
- 230000004048modificationEffects0.000description2
- 238000004064recyclingMethods0.000description2
- 238000012360testing methodMethods0.000description2
- 239000000654additiveSubstances0.000description1
- 230000000996additive effectEffects0.000description1
- 230000003542behavioural effectEffects0.000description1
- 230000009286beneficial effectEffects0.000description1
- 238000006243chemical reactionMethods0.000description1
- 230000007812deficiencyEffects0.000description1
- 238000011496digital image analysisMethods0.000description1
- 230000000694effectsEffects0.000description1
- 230000008030eliminationEffects0.000description1
- 238000003379elimination reactionMethods0.000description1
- 238000000605extractionMethods0.000description1
- 238000005286illuminationMethods0.000description1
- 238000002715modification methodMethods0.000description1
- 210000000056organAnatomy0.000description1
Images
Landscapes
- Alarm Systems (AREA)
Abstract
Description
Technical field
The utility model relates to the safety monitoring technical field, relates in particular to a kind of intelligent visual warning system and uses the method for this system alarm.
Background technology
When needs are deployed troops on garrison duty to a certain zone, if adopt present existing video monitoring equipment, usually can only directly pass in real time the image of guarded region back monitoring room, constantly stare at screen by the Security Personnel of specialty and check, to reach the purpose of security protection.But the words that adopt this mode will certainly increase Security Personnel's labour intensity.Although also there are some watch-dogs to possess the video analysis ability, but because existing methods of video analyses all is to pass through image analysis technology, by methods such as contrast frame-to-frame differences, background subtractions moving target is detected, so its testing process is easy to be subject to the environmental interference situations such as low contrast in the image, moving target is easy to undetected, and the illumination condition for complexity easily causes flase drop, such as the interference of people's shadow, the interference of ground hot spot and car bulb etc.
The utility model content
The method that the purpose of this utility model is to provide a kind of intelligent visual warning system and uses this system alarm, thus the foregoing problems that exists in the prior art solved.
To achieve these goals, the technical solution adopted in the utility model is as follows:
A kind of intelligent visual warning system comprises: video process apparatus, video acquisition device, detection alarm device and warning device, described video acquisition device, described detection alarm device and the warning device of being connected are connected with described video process apparatus respectively.
Preferably, described video process apparatus also is connected with network interface, and described network interface is connected with the remote alarms management devices.
Preferably, described video acquisition device is video camera.
Preferably, described detection alarm device is infrared eye and/or microwave detector and/or shock sensor and/or reveals cable.
Preferably, described warning device is local acoustic-optic alarm and/or remote alarms platform.
A kind of method of using intelligent visual warning system analysis warning, in conjunction with the signal of described detection alarm device feedback and the vision signal of described video acquisition device feedback, whether comprehensive analysis and judgement report to the police.
Preferably, described analysis alarm method specifically may further comprise the steps:
S1, the video data of receiver, video harvester Real-time Collection waits for that simultaneously described detection alarm device is triggered, and then carries out S2 when described detection alarm device is triggered; If not being triggered, described detection alarm device continues to wait for;
S2 analyzes the described video data that receives, and calculates the video analysis degree of confidence, whether judges described video analysis degree of confidence greater than predetermined threshold value, if it is reports to the police; If otherwise carry out S3;
S3 calculates and surveys degree of confidence, and uses the described video analysis degree of confidence of described detection degree of confidence correction, whether judges revised video analysis degree of confidence greater than predetermined threshold value, if it is reports to the police; If otherwise carry out S4;
S4 utilizes the sample classification data that pattern match is carried out in the moving region in the described video data that receives, if coupling reaches alarm threshold, then reports to the police; If coupling does not reach alarm threshold, then S1-S4 is carried out in circulation, until monitoring period finishes.
Preferably, the video analysis degree of confidence described in the S2 calculates by following steps:
S21 carries out trace analysis to the moving target in the described video data and obtains the moving target property value;
S22 utilizes described property value to calculate the video analysis degree of confidence by utility function Y (v, x, t, g);
Wherein, v represents target speed, and x represents the movement locus degree of ripeness, and t represents the time that target travel continues, and g represents target sizes;
Described utility function Y (v, x, t, g) obtains by following formula:
Y(v，x，t，g)＝K1Y1(v)+K2Y2(x)+K3Y3(t)+K4Y4(g)
Wherein, K1, K2, K3, K4The significance level that represents each property value.
Preferably, calculate among the S3 and survey degree of confidence, and use the method for the described video analysis degree of confidence of described detection degree of confidence correction, specifically may further comprise the steps:
S31 by surveying degree of confidence function Z (T), obtains surveying degree of confidence;
Wherein, T represents that described detection alarm device is by the inferior numerical value of continuous trigger;
Described detection degree of confidence function Z (T) calculates by following formula:
Z(T)＝K5Y5(T)；
S32 revises described video analysis degree of confidence by video degree of confidence correction function Y (v, x, t, g, Z), obtains revised video analysis degree of confidence;
Function Y (v, x, t, g, Z) calculates by following formula:
Y(v，x，t，g，Z)＝U(K1Y1(v)，K2Y2(x)，K3Y3(t)，K4Y4(g)，Z)
Wherein, U represents the atomic event that above-mentioned five elements consist of.
Preferably,
Pattern match described in the S4 is for using the sample classification data that pattern match is carried out in the space geometry distribution of the Corner Feature in the video pictures.
The beneficial effects of the utility model are:
Intelligent visual warning system of the present utility model, video acquisition device and detection alarm device are combined common judgement security protection field condition, the advantage of video image analysis technology and object detection technology is incorporated into all over the body, keep an eye in the situation that need not the special messenger, reduced to greatest extent the wrong report of warning device and the possibility of failing to report.Alarm method of the present utility model is applied on the intelligent visual warning system of the present utility model, effectively prevented because of the interference of the environmental factors such as light, shadow to video image, adopt the multiple analysis algorithm, analysis-by-synthesis is carried out at the security protection scene, thereby greatly improved the accuracy of security alarm.
Description of drawings
Fig. 1 is the structural representation of intelligent visual warning system of the present utility model;
Fig. 2 is the flow chart of steps of the method for application intelligent visual warning system analysis warning of the present utility model.
Embodiment
In order to make the purpose of this utility model, technical scheme and advantage clearer, below in conjunction with accompanying drawing, the utility model is further elaborated.Should be appreciated that embodiment described herein only in order to explaining the utility model, and be not used in restriction the utility model.
As shown in Figure 1, intelligent visual warning system of the present utility model, comprise: video process apparatus, video acquisition device, detection alarm device and warning device, described video acquisition device, described detection alarm device and the warning device of being connected are connected with described video process apparatus respectively.Described video process apparatus also is connected with network interface, is used for access network, and vision signal and/or alerting signal are sent on local acoustic-optic alarm and/or remote alarms management devices and the remote alarms platform in real time.Described video acquisition device is video camera.Described detection alarm device is infrared eye and/or microwave detector and/or shock sensor and/or reveals cable.Described warning device is acoustic-optic alarm and/or remote alarms platform.
In this example, adopt video camera as video acquisition device, use dsp chip as video process apparatus, can certainly use computing machine as video process apparatus.Video camera also can use the IP video camera that possesses network interface, thereby directly use original network interface on the video camera, so just saved the trouble of independent configuring network interface, in addition, when using dsp chip as video process apparatus, in order to save the space dsp chip is arranged in the housing of described I P video camera.Whole intelligent visual warning system is take the IP video camera that is embedded in the video analysis dsp chip as main body, connects simultaneously the detection alarm device harmony photoelectric alarm devices such as infrared, microwave, forms this routine Intelligent visible warning system.Also by the network interface that carries on the IP video camera alerting signal is sent on local acoustic-optic alarm, remote alarms management devices or the remote terminal in real time simultaneously.
When the needs safety precaution, on-the-spot monitor video obtains by video camera, and be divided into two-way output, one the tunnel passes to video process apparatus is used for video analysis, output was used for conventional video monitoring after another road was converted to simulating signal, and vision signal can be divided into simulating signal and two kinds of way of outputs of digital signal.On-the-spot detectable signal is by detection alarm device collections such as infrared, microwaves, and is uploaded to video process apparatus by video camera switching value input port and processes.Video process apparatus is to video analysis result and infrared, the result that the detection alarm devices such as microwave are triggered processes, produce judged result after the analysis-by-synthesis, when the result meets alert if, then alarming result is divided into two-way, the video process apparatus of leading up to exports network interface to and is sent to remote alarms management devices or terminal device and remote alarms platform by network again, the video that will be attached with simultaneously warning message is preserved, another road alarming result is converted to the switching value signal by the switching value output port, be used for guide sound, the local panalarms such as photoelectric alarm equipment are reported to the police, thereby can effectively stop the generation of criminal's behavior.Can certainly the connecting communication device, send alert information by communication device to the platform of receiving a crime report of public security organ.
As shown in Figure 2, the method that application intelligent visual warning system analysis of the present utility model is reported to the police, core are in conjunction with the vision signal of the signal of described detection alarm device feedback and described video acquisition device feedback, comprehensively judge whether to report to the police.
Described analysis alarm method specifically may further comprise the steps:
S1, the video data of receiver, video harvester Real-time Collection waits for that simultaneously described detection alarm device is triggered, and then carries out S2 when described detection alarm device is triggered; If not being triggered, described detection alarm device continues to wait for;
S2 analyzes the described video data that receives, and calculates the video analysis degree of confidence, whether judges described video analysis degree of confidence greater than predetermined threshold value, if it is reports to the police; If otherwise carry out S3;
Described video analysis degree of confidence calculates by following steps:
S21 carries out trace analysis to the moving target in the described video data and obtains the moving target property value;
S22 utilizes described property value to calculate the video analysis degree of confidence by utility function Y (v, x, t, g);
Wherein, v represents target speed, and x represents the movement locus degree of ripeness, and t represents run duration, and g represents target sizes;
Described utility function Y (v, x, t, g) obtains by following formula:
Y(v，x，t，g)＝K1Y1(v)+K2Y2(x)+K3Y3(t)+K4Y4(g)
Wherein, K1, K2, K3, K4The significance level that represents each property value.
S3 calculates and surveys degree of confidence, and uses the described video analysis degree of confidence of described detection degree of confidence correction, whether judges revised video analysis degree of confidence greater than predetermined threshold value, if it is reports to the police; If otherwise carry out S4;
Survey the method for the described video analysis degree of confidence of degree of confidence correction, specifically may further comprise the steps:
S31 by surveying degree of confidence function Z (T), obtains surveying degree of confidence;
Wherein, T represents that described detection alarm device is by the inferior numerical value of continuous trigger;
Described detection degree of confidence function Z (T) calculates by following formula:
Z(T)＝K5Y5(T)；
S32 revises described video analysis degree of confidence by video degree of confidence correction function Y (v, x, t, g, Z), obtains revised video analysis degree of confidence;
Function Y (v, x, t, g, Z) calculates by following formula:
Y(v，x，t，g，Z)＝U(K1Y1(v)，K2Y2(x)，K3Y3(t)，K4Y4(g)，Z)
Wherein, U represents the atomic event that above-mentioned five elements consist of, so-called atomic event refer to indispensable above-mentioned in the needed element of any one alert if, otherwise can not report to the police.
S4 utilizes the sample classification data that pattern match is carried out in the moving region in the described video data that receives, if coupling reaches alarm threshold, then reports to the police; If coupling does not reach alarm threshold, then S1-S4 is carried out in circulation, until monitoring period finishes.
Pattern match described in the S4 is for using the sample classification data that pattern match is carried out in the space geometry distribution of the Corner Feature in the video pictures.
Utilize the detection alarm device such as infrared, microwave first guarded region to be surveyed, judge that guarded region has driftlessness, eliminate light to the interference of guarded region.In order to eliminate temperature variation or little moving target to the interference of detection alarm device, utilize in the image analytical method digital serial images that obtains is analyzed, obtain the movement properties information of target, comprise position, speed, size, direction, track, and target is followed the tracks of.Recycling is set up warning degree of confidence model to the target property information of Target Acquisition.The high low reaction of warning degree of confidence the degree of risk of guarded region.Degree of risk refers to that mainly there is unmanned behavioral activity in the analysis monitoring zone.For the situation of low contrast, or the impact of environmental factor, the tracking of target is probably lost, or the track of target is described inaccurate, the accuracy that impact is reported to the police.For fear of failing to report, the utility model is after the degree of confidence analysis-by-synthesis, also be provided with the video analysis algorithm: first human body and various types of vehicles are carried out modeling, when degree of confidence Comprehensive analysis results in front does not meet alert if, again video pictures is analyzed, during analysis pattern match is carried out in the moving region, if the degree of coupling is high, then report to the police.In addition, the detection means that this method relates to is not limited to infrared, microwave, comprises all similar detection methods.This method utilizes detection means get rid of to disturb in a word, utilizes the video analysis means to remedy the deficiency of detection means, reaches the purpose of accurate warning.
The Digital image analysis technique of based on motion target, refer to utilize statistics that image background is carried out modeling, then according to background subtraction, frame-to-frame differences method moving target is detected, and extraction moving target association attributes, determine testing result and the object that detects is followed the tracks of, and the moving region carried out pattern match, last output detections result.
The utility model utilize that the Detection Techniques such as image analysis technology and infrared, microwave merge mutually zone of protection carried out the object intrusion detection report to the police, can effectively get rid of light, shadow disturbs.
The utility model carries out the method for confidence calculations based on the graphical analysis result, guarantee that the alarming result of exporting has higher reliability.
The utility model has also used the recognition methods to the moving region target, and the method is that human body and a various vehicle are trained, and the angle point of the various gaits of first human body and various types of vehicles utilizes the space distribution of angle point that target is identified.The sample data that the recycling classification is good is carried out pattern match to the moving region.
Method of the present utility model is carried out modeling analysis to result of detections such as video analysis result and infrared, microwaves and is calculated the warning degree of confidence.Warning degree of confidence model utilizes the multi-attribute-utility decision model to carry out modeling, and data modeling specific implementation process is as follows:
Obtain the objective attribute target attribute value--utility function of＞objective definition property value--＞calculating video analysis degree of confidence--＞calculate and survey degree of confidence--＞calculating alarming result.
The detection means such as that this example is at first utilized is infrared, microwave are surveyed the zone, if there is signal to trigger, then further utilize the video analysis means that the moving region is analyzed, to get rid of variation of ambient temperature or the interference of little moving target.Specific practice is to utilize the detection tracking results of moving target in conjunction with result of detection analysis warning degree of confidence such as infrared, microwaves, if reach alarm threshold value then report to the police.When degree of confidence Comprehensive analysis results in front does not meet alert if, again video pictures is carried out trace analysis, in to the motion target tracking analytic process, utilize simultaneously angle point spatial model coupling, pattern match is carried out in the moving region, if coupling reaches the thresholding upper limit, then report to the police.Pattern match can fine elimination because environmental interference can not the Continuous Tracking target causes the problem of track rejection, can judge whether well so real moving target existence.
Before implementing to analyze warning, the various gait samples of human body and various types of vehicles sample are carried out Corner Detection, utilize angle point to distribute it is classified.Obtain the sample classification data.The sample classification data storing is for subsequent use
Concrete alarm analysis implementation Process is as follows:
A. at first utilize the detection means such as infrared, microwave that the zone is surveyed, if there is signal to trigger, then carry out next step;
B. to the video image target trace analysis, obtain the wherein property value of moving target,
The video analysis degree of confidence is comprised of following property value, target speed (v), the track degree of ripeness (x) of motion, the time of motion (t), the size of target (g).
The utility function Y (v, x, t, g) of definition video analysis degree of confidence,
Several variablees of supposing this function are separate, so the multi-attribute-utility decision function can be expressed as additive function:
Y(v，x，t，g)＝K1Y1(v)+K2Y2(x)+K3Y3(t)+K4Y4(g)；
Wherein, K1, K2, K3, K4The significance level that represents each factor.
C. calculate the video analysis degree of confidence,
Utilize decision function to calculate the video analysis degree of confidence, when degree of confidence surpasses default threshold value, then need not to revise, direct output alarm signal, otherwise continue next step.
D. comprehensive confidence calculations,
As video analysis degree of confidence result during less than predetermined threshold value, calculate and survey degree of confidence:
Z(T)＝K5Y5(T)
Wherein, T represents the number of times utility value of infrared continuous trigger.
Utilize Z (T) that video analysis degree of confidence Y (v, x, t, g) is revised, determine whether according to correction result and carry out alerting signal output.Modification method is as follows:
Y(v，x，t，g，Z)＝U(K1Y1(v)，K2Y2(x)，K3Y3(t)，K4Y4(g)，Z)；
Wherein, U represents that above-mentioned several element consists of an atomic event.
If e. Y value does not reach the threshold value of warning, utilize simultaneously the sample classification data that pattern match is carried out in the moving region, the space geometry that mainly mates Corner Feature distributes, if coupling reaches alarm threshold, reports to the police equally.
In the above-mentioned steps, no matter when which step judgement need to be reported to the police, then the local panalarms such as guide sound, light, electric alarm equipment were reported to the police, and simultaneously warning message are transferred to target platform or terminal by network interface.
By adopting the disclosed technique scheme of the utility model, obtained following useful effect:
Intelligent visual warning system of the present utility model, video acquisition device and detection alarm device are combined common judgement security protection field condition, the advantage of video image analysis technology and object detection technology is incorporated into all over the body, keep an eye in the situation that need not the special messenger, reduced to greatest extent the wrong report of warning device and the possibility of failing to report.Alarm method of the present utility model is applied on the intelligent visual warning system of the present utility model, effectively prevented because of the interference of the environmental factors such as light, shadow to video image, adopt the multiple analysis algorithm, analysis-by-synthesis is carried out at the security protection scene, thereby greatly improved the accuracy of security alarm.
The above only is preferred implementation of the present utility model; should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the utility model principle; can also make some improvements and modifications, these improvements and modifications also should be looked protection domain of the present utility model.
Claims (5)
1. intelligent visual warning system, it is characterized in that, comprise: video process apparatus, video acquisition device, detection alarm device and warning device, described video acquisition device, described detection alarm device and the warning device of being connected are connected with described video process apparatus respectively.
2. intelligent visual warning system according to claim 1 is characterized in that described video process apparatus also is connected with network interface, and described network interface is connected with the remote alarms management devices.
3. intelligent visual warning system according to claim 1 and 2 is characterized in that, described video acquisition device is video camera.
4. intelligent visual warning system according to claim 1 and 2 is characterized in that, described detection alarm device is infrared eye and/or microwave detector and/or shock sensor and/or reveals cable.
5. intelligent visual warning system according to claim 1 and 2 is characterized in that, described warning device is local acoustic-optic alarm and/or remote alarms platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201220640744CN202904792U (en) | 2012-11-28 | 2012-11-28 | Intelligent visualized alarm system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201220640744CN202904792U (en) | 2012-11-28 | 2012-11-28 | Intelligent visualized alarm system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN202904792Utrue CN202904792U (en) | 2013-04-24 |
Family
ID=48125432
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201220640744Expired - LifetimeCN202904792U (en) | 2012-11-28 | 2012-11-28 | Intelligent visualized alarm system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN202904792U (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102999988A (en)* | 2012-11-28 | 2013-03-27 | 尹兆杰 | Intelligent visualization alarm system and method for alarming by using system |
| CN103366483A (en)* | 2013-06-27 | 2013-10-23 | 深圳市智美达科技有限公司 | Monitoring alarm system |
| CN105225391A (en)* | 2015-08-28 | 2016-01-06 | 国网上海市电力公司 | Based on the electric power facilities anti-theft anti-intrusion method for supervising of infrared thermal release electric human body sensing |
- 2012
- 2012-11-28CNCN 201220640744patent/CN202904792U/ennot_activeExpired - Lifetime
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102999988A (en)* | 2012-11-28 | 2013-03-27 | 尹兆杰 | Intelligent visualization alarm system and method for alarming by using system |
| CN102999988B (en)* | 2012-11-28 | 2014-09-17 | 北京和顺智视科技有限公司 | Intelligent visualization alarm system and method for alarming by using system |
| CN103366483A (en)* | 2013-06-27 | 2013-10-23 | 深圳市智美达科技有限公司 | Monitoring alarm system |
| CN105225391A (en)* | 2015-08-28 | 2016-01-06 | 国网上海市电力公司 | Based on the electric power facilities anti-theft anti-intrusion method for supervising of infrared thermal release electric human body sensing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102164270A (en) | Intelligent video monitoring method and system capable of exploring abnormal events | |
| CN100585656C (en) | A rule-based all-weather intelligent video analysis and monitoring method | |
| KR102195706B1 (en) | Method and Apparatus for Detecting Intruder | |
| US20130265423A1 (en) | Video-based detector and notifier for short-term parking violation enforcement | |
| Lim et al. | iSurveillance: Intelligent framework for multiple events detection in surveillance videos | |
| Kumar et al. | Study of robust and intelligent surveillance in visible and multi-modal framework | |
| KR102356666B1 (en) | Method and apparatus for risk detection, prediction, and its correspondence for public safety based on multiple complex information | |
| CN105809679A (en) | Mountain railway side slope rockfall detection method based on visual analysis | |
| CN112907869B (en) | Intrusion detection system based on multiple sensing technologies | |
| CN113963301A (en) | A video fire smoke detection method and system based on fusion of spatiotemporal features | |
| CN110002315A (en) | Vertical ladder electric vehicle detection method and warning system based on deep learning | |
| CN113870509B (en) | An intrusion detection system | |
| CN110780356A (en) | Subway platform clearance foreign matter detecting system | |
| CN114511978B (en) | Intrusion early warning method, device, vehicle and computer readable storage medium | |
| Malhi et al. | Vision based intelligent traffic management system | |
| KR101454644B1 (en) | Loitering Detection Using a Pedestrian Tracker | |
| CN111814510A (en) | Detection method and device for remnant body | |
| CN104392464A (en) | Human intrusion detection method based on color video image | |
| CN202904792U (en) | Intelligent visualized alarm system | |
| CN116798176A (en) | Data management system based on big data and intelligent security | |
| CN117649736A (en) | Video management method and system based on AI video management platform | |
| CN117523807A (en) | Data processing method and system for external damage prevention warning lamp of electric power facility | |
| CN101814134A (en) | Intelligent flame detection method based on computer vision monitoring technology | |
| CN112102543A (en) | Security check system and method | |
| CN102999988B (en) | Intelligent visualization alarm system and method for alarming by using system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| ASS | Succession or assignment of patent right | Owner name:BEIJING HESHUN ZHISHI TECHNOLOGY CO., LTD. Free format text:FORMER OWNER: YIN ZHAOJIE Effective date:20130528 | |
| C41 | Transfer of patent application or patent right or utility model | ||
| TR01 | Transfer of patent right | Effective date of registration:20130528 Address after:100085, Beijing, Haidian District on the road No. 2, No. 1, building 13, 13D Patentee after:BEIJING HESHUN ZHISHI TECHNOLOGY Co.,Ltd. Address before:100085, Beijing, Haidian District on the road No. 2, No. 1, building 13, 13D Patentee before:Yin Zhaojie | |
| TR01 | Transfer of patent right | Effective date of registration:20210120 Address after:100080 2w20, 2nd floor, building 1, No.35, West Street, Haidian Town, Haidian District, Beijing Patentee after:Beijing Rongtai Xinchuang Technology Co.,Ltd. Address before:100085 13D, 13 / F, building 1, No.2, Shangdi Information Road, Haidian District, Beijing Patentee before:BEIJING HESHUN ZHISHI TECHNOLOGY Co.,Ltd. | |
| TR01 | Transfer of patent right | ||
| CX01 | Expiry of patent term | Granted publication date:20130424 | |
| CX01 | Expiry of patent term |