CN102521578B - Method for detecting and identifying intrusion - Google Patents
Method for detecting and identifying intrusion Download PDFInfo
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- CN102521578B CN102521578B CN 201110427589 CN201110427589A CN102521578B CN 102521578 B CN102521578 B CN 102521578B CN 201110427589 CN201110427589 CN 201110427589 CN 201110427589 A CN201110427589 A CN 201110427589A CN 102521578 B CN102521578 B CN 102521578B
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- 238000000034 method Methods 0.000 title claims abstract description 21
- 239000013598 vector Substances 0.000 claims abstract description 6
- 238000013475 authorization Methods 0.000 claims abstract description 3
- 238000001514 detection method Methods 0.000 claims description 11
- 239000000284 extract Substances 0.000 claims description 9
- 230000033001 locomotion Effects 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 3
- 230000019771 cognition Effects 0.000 claims description 3
- 230000009545 invasion Effects 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 238000012545 processing Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005452 bending Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
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- 238000012549 training Methods 0.000 description 1
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Abstract
The invention discloses a method for detecting and identifying intrusion, which is applied to the field of video monitoring. By the aid of the method, human body identification, human face identification, cloud identification and manual assisted identification can be integrated, video sessions are divided, and representative frame images are selected; moving targets and areas are detected by the aid of background subtraction, and then a representative frame image which records a moving target with the size/shape closest to a threshold value is selected from a video session by the aid of a filter; the moving target is extracted from the representative frame image by the aid of background subtraction, then a head and shoulder two-dimensional model of a moving human body is built, and invariant moment of a contour of the model is computed to form feature vectors; and technical authorization of identification of a human body target and the like is carried out by a first classifier. Accordingly, video processing workload can be reduced, real-time identification and real-time alarm efficiency can be improved, and safety protection efficiency of families, communities and the like is enhanced.
Description
Technical field
The present invention relates to technical field of video monitoring, particularly a kind of video frequency monitoring method of integrated multiple recognition technology.
Background technology
At present, video surveillance applications is very extensive.Family, office building, community etc., by automatic video monitoring, a large amount of savings manpower and materials.But deficiency is that present most of video monitoring can only carry out recording monitor to the environment in the guarded region, and can't the people in the zone be identified automatically, can't automatically identify the stranger, then takes corresponding security protection measure.
Carry out the people and automatically identify, technology commonly used comprises human body identification, recognition of face.Wherein the recognition of face discrimination is higher, but often need the identified person to ajust posture, then take pictures/record a video over against camera and just can accurately identify, but for invasion personnel such as thief, terrorists, realize that the standard face that finishes takes a picture, then identification is impossible.So, only adopting face recognition technology in the real-time video monitoring, obtained discrimination accuracy rate is very limited.With respect to recognition of face, human body identification it utilized the contour shape in shoulders of human body and above zone basicly stable, the advantage such as be not vulnerable to block, consider that bending moment does not have translation, Rotation and Zoom unchangeability, in order to process not homonymy and the variation of shoulder shape, set up the two-dimentional model of cognition of human body head and shoulder shape, and then identify.But, only use human body identification, for real-time dynamic intrusion detection and identification, or inadequate.
Summary of the invention
Purpose of the present invention is for above-mentioned existing issue, propose a kind of intrusion detection and recognition methods that is applied to field of video monitoring, it can integrated various human recognition technology, gives full play to simultaneously the distributed collaborative technology, in order to improve recognition efficiency, improve the security protection efficient of family, community etc.
The present invention is achieved by the following scheme:
A kind of intrusion detection and recognition methods, is characterized in that by video data acquiring, image recognition, last method of carrying out security control according to recognition result for successively, also comprise step:
A). by the real-time video in the imageing sensor collection area of visual field;
B). divide video-frequency band, and from video-frequency band, isolate the image of every frame, utilize background subtraction to divide to detect moving target and zone, then select the size/shape of moving target of record in the video-frequency band near the representative frame image of threshold values by filtrator;
C). utilize the background difference to extract moving target from this representative frame image, then set up the head shoulder two dimensional model of movement human and the invariant rectangle of computation model profile and become proper vector; Utilize the first sorter to carry out the identification of human body target;
D). according to the result of human body target identification, if can be judged to be the stranger, then master control system is reported to the police by control bus startup this locality or network alarming apparatus; If can not accurately determine whether the stranger, then carry out the recognition of face step;
E). when carrying out recognition of face, extract the overall situation or the local feature of people's face in the representative frame image;
If can extract the overall situation or the local feature of people's face, then judge the people's appearance image together that whether exists in the master control system database with moving target by the second sorter; Exist, then master control system allows it to carry out relative operation for this people authorizes; Otherwise starting this locality or network alarming apparatus reports to the police;
If can't be from the overall situation or the local feature of extraction people face in the representative frame image, then master control system starts the cloud identification step;
F). the cloud identification step, master control system sends to the cloud platform with this representative frame image, and then is forwarded to user's the network terminal by the cloud platform; The user utilizes the network terminal, carry out artificial cognition by human eye, if be judged as the people of understanding, allow master control system that this moving target is carried out authorization, otherwise be judged to be the stranger of invasion, the user reports to the police by network terminal control master control system or stops this moving target in the operation of system.
As preferably, described imageing sensor carries out video image acquisition with the speed of 5～15 frame/seconds; Described video-frequency band length is 1～5 minute.
Further, when the head shoulder two dimensional model of described movement human is set up, calculate first the ratio of width to height of moving target, and judge whether 0.28～0.36; Calculate the vertical direction projection histogram, find out near the local maximum in the crown, determine head width; Calculate at last a shoulder length degree, then set up head shoulder model; When extracting the failure of head shoulder model, then think the moving target that belongs to non-human body.
Further, during described recognition of face, the second used sorter integrates whole sorter for being walked abreast by global classification device and the local classifiers mode by weighted sum.The client that described user's the network terminal is installed by this locality receives the image from the cloud platform, carries out simultaneously the mutual and data transmission of control signal.
In sum, the present invention has following distinguishing feature:
1. employing multiple technologies, integrated human body identification, recognition of face, cloud identification and human assistance identification, but Effective Raise recognition efficiency and accuracy rate.
2. by dividing video-frequency band, choosing the technological means such as representative frame image, can reduce the Video processing amount, improve the efficient of Real time identification, Realtime Alerts.
Description of drawings
Fig. 1 is that the core procedure of inventive method forms synoptic diagram;
Fig. 2 is human body identification block diagram;
Fig. 3 is the particular flow sheet of inventive method.
Embodiment
With reference to figure 1, the inventive method is integrated human body identification, recognition of face, cloud recognition technology.When realizing intrusion detection and identification, at first by carrying out real-time video acquisition, the moving target that carries out in the zone is monitored, provide material for identification simultaneously.Then, video is carried out pre-service, comprising: divide video-frequency band, isolate every frame image, select representative frame image etc., last, successively by human body identification, recognition of face, cloud identification and the identification of user's artificial assistance.
With reference to figure 3, be the main flow process of realization of the present invention.At first:
See step 101, by the real-time video in the imageing sensor collection area of visual field; Acquisition rate is take the speed of 5～15 frame/seconds as suitable, and wherein preferred version was 10 frame/seconds.With traditional in order to improve real-time, and emphasize that acquisition rate is different, the video of collection of the present invention is processed for the Multiple recognition that satisfies the later stage, so acquisition rate needn't be too high, if the too high calculated amount that must strengthen identification is unfavorable for the raising of recognition accuracy and efficient on the contrary;
Human body identification module in the step 107, master control system will be set up the head shoulder two dimensional model of this movement human and the invariant rectangle of computation model profile becomes proper vector.With reference to figure 2, for carrying out the human body whole FB(flow block) in identification time.After proper vector is extracted, will be that the BP network distributor is identified and the output category result by the first sorter; Wherein the BP network distributor is constantly updated or is revised by the BP network training from sample set.When the head shoulder two dimensional model of movement human is set up, calculate first the ratio of width to height of moving target, and judge whether 0.28～0.36; Calculate the vertical direction projection histogram, find out near the local maximum in the crown, determine head width; Calculate at last a shoulder length degree, then set up head shoulder model; When extracting the failure of head shoulder model, then think the moving target that belongs to non-human body.
Step 111, system are extracted the overall situation or the local feature of people's face in the representative frame image;
Can step 112, judgement extract the overall situation or the local feature of people's face, if can extract then forward 113 to, otherwise start cloud identification, enter 115;
Claims (6)
1. an intrusion detection and recognition methods, is characterized in that by video data acquiring, image recognition, the method for carrying out security control according to recognition result at last for successively, also comprises step:
A). by the real-time video in the imageing sensor collection area of visual field;
B). divide video-frequency band, and from video-frequency band, isolate the image of every frame, utilize background subtraction to divide to detect moving target and zone, then select the size/shape of moving target of record in the video-frequency band near the representative frame image of threshold values by filtrator;
C). utilize the background difference to extract moving target from this representative frame image, then set up the head shoulder two dimensional model of movement human and the invariant rectangle of computation model profile and become proper vector; Utilize the first sorter to carry out the identification of human body target;
D). according to the result of human body target identification, if can be judged to be the stranger, then master control system is reported to the police by control bus startup this locality or network alarming apparatus; If can not accurately determine whether the stranger, then carry out the recognition of face step;
E). when carrying out recognition of face, extract the overall situation or the local feature of people's face in the representative frame image;
If can extract the overall situation or the local feature of people's face, then judge the people's appearance image together that whether exists in the master control system database with moving target by the second sorter; Exist, then master control system is authorized and is allowed it to carry out relative operation; Otherwise starting this locality or network alarming apparatus reports to the police;
If can't be from the overall situation or the local feature of extraction people face in the representative frame image, then master control system starts the cloud identification step;
F). the cloud identification step, master control system sends to the cloud platform with this representative frame image, and then is forwarded to user's the network terminal by the cloud platform; The user utilizes the network terminal, carry out artificial cognition by human eye, if be judged as the people of understanding, allow master control system that this moving target is carried out authorization, otherwise be judged to be the stranger of invasion, the user reports to the police by network terminal control master control system or stops this moving target in the operation of system.
2. intrusion detection as claimed in claim 1 and recognition methods is characterized in that, described imageing sensor carries out video image acquisition with the speed of 5～15 frame/seconds.
3. intrusion detection as claimed in claim 1 or 2 and recognition methods is characterized in that, described video-frequency band length is 1～5 minute.
4. intrusion detection as claimed in claim 3 and recognition methods is characterized in that, when the head shoulder two dimensional model of described movement human is set up, calculate first the ratio of width to height of moving target, and judge whether 0.28～0.36; Calculate the vertical direction projection histogram, find out near the local maximum in the crown, determine head width; Calculate at last a shoulder length degree, then set up head shoulder model; When extracting the failure of head shoulder model, then think the moving target that belongs to non-human body.
5. intrusion detection as claimed in claim 4 and recognition methods is characterized in that, during described recognition of face, and the whole sorter of the second used sorter for walking abreast and integrate by global classification device and the local classifiers mode by weighted sum.
6. such as claim 1,2,4,5 arbitrary described intrusion detection and recognition methodss, it is characterized in that the client that described user's the network terminal is installed by this locality receives the image from the cloud platform, carry out simultaneously the mutual and data transmission of control signal.
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| CN 201110427589 CN102521578B (en) | 2011-12-19 | 2011-12-19 | Method for detecting and identifying intrusion |
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| CN 201110427589 CN102521578B (en) | 2011-12-19 | 2011-12-19 | Method for detecting and identifying intrusion |
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| CN102521578A CN102521578A (en) | 2012-06-27 |
| CN102521578B true CN102521578B (en) | 2013-10-30 |
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| CN104010106A (en) * | 2014-04-10 | 2014-08-27 | 福建伊时代信息科技股份有限公司 | Copying machine security monitoring method and system based on real-time video analysis |
| CN104079881B (en) * | 2014-07-01 | 2017-09-12 | 中磊电子（苏州）有限公司 | The relative monitoring method of supervising device |
| CN105744345B (en) * | 2014-12-12 | 2019-05-31 | 深圳Tcl新技术有限公司 | Video-frequency compression method and device |
| CN104463174A (en) * | 2014-12-16 | 2015-03-25 | 广州南方电力集团科技发展有限公司 | Multi-angle dynamic people recognition and behavior prediction system |
| CN105006089B (en) * | 2015-07-01 | 2017-11-10 | 国家电网公司 | A kind of security monitoring alarm method and system based on image |
| CN105100724B (en) * | 2015-08-13 | 2018-06-19 | 电子科技大学 | A kind of smart home telesecurity monitoring method of view-based access control model analysis |
| CN105243773A (en) * | 2015-09-25 | 2016-01-13 | 国网山东省电力公司经济技术研究院 | Portable intelligent alarm fence and human proximity detection method |
| CN105654647B (en) * | 2016-01-28 | 2017-11-28 | 中北大学 | A kind of real-time judge someone invades indoor recognition methods |
| JP6340538B2 (en) * | 2016-03-11 | 2018-06-13 | 株式会社プロドローン | Biological search system |
| CN105979230A (en) * | 2016-07-04 | 2016-09-28 | 上海思依暄机器人科技股份有限公司 | Monitoring method and device realized through images by use of robot |
| WO2018014326A1 (en) * | 2016-07-22 | 2018-01-25 | 绿仕科技控股有限公司 | Security system and method |
| CN106372576A (en) * | 2016-08-23 | 2017-02-01 | 南京邮电大学 | Deep learning-based intelligent indoor intrusion detection method and system |
| CN106781449A (en) * | 2017-02-21 | 2017-05-31 | 青岛智能产业技术研究院 | Crossing pedestrian crosses the street integrated management control system |
| CN107610392A (en) * | 2017-09-20 | 2018-01-19 | 北京亚欧震达科技发展有限公司 | A kind of pedestrian detection of motor car inspection and repair storehouse pipe gallery and the apparatus and method of alarm |
| CN109145766B (en) * | 2018-07-27 | 2021-03-23 | 北京旷视科技有限公司 | Model training method and device, recognition method, electronic device and storage medium |
| CN109117812A (en) * | 2018-08-24 | 2019-01-01 | 深圳市赛为智能股份有限公司 | House safety means of defence, device, computer equipment and storage medium |
| CN110895663B (en) * | 2018-09-12 | 2023-06-02 | 杭州海康威视数字技术股份有限公司 | Two-wheel vehicle identification method and device, electronic equipment and monitoring system |
| CN109460787B (en) * | 2018-10-26 | 2020-08-18 | 北京交通大学 | Intrusion detection model establishing method and device and data processing equipment |
| CN109359625A (en) * | 2018-11-16 | 2019-02-19 | 南京甄视智能科技有限公司 | The method and system of customer identification is judged based on head and shoulder detection and face recognition technology |
| CN111191498A (en) * | 2019-11-07 | 2020-05-22 | 腾讯科技（深圳）有限公司 | Behavior recognition method and related product |
| CN112532938B (en) * | 2020-11-26 | 2021-08-31 | 武汉宏数信息技术有限责任公司 | Video monitoring system based on big data technology |
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| CN101174298A (en) * | 2006-11-02 | 2008-05-07 | 上海银晨智能识别科技有限公司 | Scattered-point high-volume face recognition system and recognizing method thereof |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US6181806B1 (en) * | 1993-03-29 | 2001-01-30 | Matsushita Electric Industrial Co., Ltd. | Apparatus for identifying a person using facial features |
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