CN102521578A - Method for detecting and identifying intrusion - Google Patents

Method for detecting and identifying intrusion Download PDF

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Publication number
CN102521578A
CN102521578A CN201110427589XA CN201110427589A CN102521578A CN 102521578 A CN102521578 A CN 102521578A CN 201110427589X A CN201110427589X A CN 201110427589XA CN 201110427589 A CN201110427589 A CN 201110427589A CN 102521578 A CN102521578 A CN 102521578A
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video
identification
moving target
recognition
control system
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CN201110427589XA
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CN102521578B (en
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卢林发
叶灿才
黄家祺
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Guangdong Xuzhi Travel Technology Co ltd
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ZHONGSHAN IKER DIGITAL TECHNOLOGY Co Ltd
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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

A kind of intrusion detection and recognition methods
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 or the like, through 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 discerned automatically, can't discern the stranger automatically, takes corresponding security protection measure then.
Carry out the people and discern automatically, technology commonly used comprises human body identification, recognition of face.Wherein the recognition of face discrimination is than higher; But often need the identified person to ajust posture, take pictures/record a video over against camera then and just can accurately discern, but for invasion personnel such as thief, terrorists; The standard face that realizes accomplishing takes a picture, and identification is impossible then.So, only adopting face recognition technology in the real-time video monitoring, the discrimination accuracy rate that is obtained is very limited.With respect to recognition of face; Human body identification it utilized the contour shape in human body shoulder and above zone basicly stable; Advantage such as be not vulnerable to block, consider that invariant moments has translation, rotation and convergent-divergent unchangeability, shoulder shape variation in order to handle not homonymy; Set up the two-dimentional model of cognition of human body head and shoulder shape, and then discern.But, only use human body identification, for real-time dynamic intrusion detection and identification, still not enough.
Summary of the invention
The object of the invention is to 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 the distributed collaborative technology simultaneously; In order to improve recognition efficiency, improve the security protection efficient of family, community etc.
The present invention realizes through following scheme:
A kind of intrusion detection and recognition methods, is characterized in that through video data acquiring, image recognition, last method of carrying out security control according to recognition result for successively, also comprise step:
A). gather the real-time video in the area of visual field through imageing sensor;
B). divide video-frequency band, and from video-frequency band, isolate the image of every frame, utilize the background subtraction branch to detect moving target and zone, the size/shape of moving target of selecting record in the video-frequency band through filtrator then is near the representative frame image of threshold values;
C). utilize the background difference to extract moving target, set up the head shoulder two dimensional model of movement human and the invariant moments of computation model profile then and form proper vector from this representative frame image; Utilize first sorter to carry out the identification of human body target;
D). according to the result of human body target identification, if decidable is the stranger, then master control system is reported to the police through control bus startup this locality or network alarming device; If can not accurately judge whether to be 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 through 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 device 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 through human eye,, allow master control system that this moving target is operated mandate if be judged as the people of understanding; Otherwise be judged to be the stranger of invasion, the user reports to the police through network terminal control master control system or stops the operation of this moving target in 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 the ratio of width to height of moving target earlier, and judge whether 0.28～0.36; Calculate the vertical direction projection histogram, find out near the local maximum in the crown, confirm head width; Calculate a shoulder length degree at last, set up head shoulder model then; When extracting the failure of a head shoulder model, then think the moving target that belongs to non-human body.
Further, during described recognition of face, the second used sorter overall sorter and the local classifiers mode through weighted sum of serving as reasons walks abreast and integrates whole sorter.The client that described user's the network terminal is installed through this locality receives the image from the cloud platform, carries out the mutual and data transmission of control signal simultaneously.
In sum, the present invention has following distinguishing feature:
1. employing multiple technologies, integrated human body identification, recognition of face, cloud identification and artificial aid identification can effectively improve recognition efficiency and accuracy rate.
2. through dividing video-frequency band, choosing 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 is formed synoptic diagram;
Fig. 2 is a 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 with identification, at first, the moving target that carries out in the zone is monitored through carrying out real-time video acquisition, for identification material is provided simultaneously.Then, video is carried out pre-service, comprising: divide video-frequency band, isolate every frame image, select representative frame image etc., last, successively through 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, gather the real-time video in the area of visual field through imageing sensor; Acquisition rate is suitable with the speed of 5～15 frame/seconds, and wherein preferred version was 10 frame/seconds.With traditional in order to improve real-time; And stress that acquisition rate is different, the video of collection of the present invention is handled for the multiple identification of satisfying 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;
Step 102, the video processing module of master control system are cut apart the real-time video input of gathering and are divided into video-frequency band.Can divide in batches in the time of division, also can divide as required in order.Video-frequency band length is 1～5 minute.Specifically can be according to imageing sensor or picture pick-up device institute region covered, the assessor decides the length of video-frequency band at needed time of this zone of normally passing by then.
Step 103 is isolated the image of every frame from video-frequency band, utilize the background subtraction branch to detect moving target and zone then, extracts moving target.Step 104, when adopting the background difference in this video-frequency band, to extract moving target, also just explain do not have in this time period moving target also with regard to the object do not discerned, do not have the invador, so can carry out next video-frequency band processing, rotate back into 102; When proposing moving target, get into step 105;
Step 105, the filtrator through master control system are selected the size/shape of moving target of record in the video-frequency band near the representative frame image of threshold values.Representative frame image can be the maximum frame of moving target of record in this video-frequency band, also can be record the moving target element at most, a profile frame the most clearly.Practical implementation can be done further definition.
Step 106 is extracted moving target from representative frame image;
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 moments of computation model profile forms 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 discerned and the output category result through first sorter; Wherein the BP network distributor is brought in constant renewal in or is revised through the BP network training from sample set.When the head shoulder two dimensional model of movement human is set up, calculate the ratio of width to height of moving target earlier, and judge whether 0.28～0.36; Calculate the vertical direction projection histogram, find out near the local maximum in the crown, confirm head width; Calculate a shoulder length degree at last, set up head shoulder model then; When extracting the failure of a head shoulder model, then think the moving target that belongs to non-human body.
Step 108 judges whether that can be able to form a head shoulder two dimensional model from representative frame image maybe extract proper vector, if could would carry out human body identification through first sorter, forward 109 to; Otherwise the account for motion target is not the people, need not carry out next step recognition of face.
Step 109, first sorter are carried out human body identification, if recognition result is the stranger, then forward step 110 to, and master control system starts this locality through control bus or the network alarming device is reported to the police, and forbids each generic operation that it is follow-up.If can't accurately discern, then start the recognition of face program, get into step 111.
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, get into 115;
Step 113 utilizes second sorter to carry out recognition of face.Second sorter is judged the people's appearance image together that whether exists in the master control system database with moving target; Exist, then forward step 114 to, master control system allows it to carry out relative operation for this people authorizes; Otherwise starting this locality or network alarming device reports to the police.If can't judge, then start cloud identification according to the face characteristic that extracts.Second sorter overall sorter and the local classifiers mode through weighted sum of serving as reasons walks abreast and integrates whole sorter.
Step 115, master control system sends to the cloud platform through the network image that representative frame is corresponding.
Step 116, cloud platform are forwarded to the corresponding network terminal of one or more users according to the forwarding mechanism of reaching an agreement between the master control system with this image, realize identification through manual work.
Step 117, the network terminal that the user answers is received this image, the user judges whether to the people who is familiar with, if then directly or indirectly feed back to master control system, allows this target mandate (step 119); Otherwise forbid the operation that it is follow-up, through starting this locality or network alarming device report to the police (step 120).User's the network terminal can receive the image from the cloud platform through the client that install this locality, carries out the mutual and data transmission of control signal simultaneously.

Claims (6)

1. intrusion detection and recognition methods, is characterized in that through video data acquiring, image recognition, the method for carrying out security control according to recognition result at last for successively, also comprises step:
A). gather the real-time video in the area of visual field through imageing sensor;
B). divide video-frequency band, and from video-frequency band, isolate the image of every frame, utilize the background subtraction branch to detect moving target and zone, the size/shape of moving target of selecting record in the video-frequency band through filtrator then is near the representative frame image of threshold values;
C). utilize the background difference to extract moving target, set up the head shoulder two dimensional model of movement human and the invariant moments of computation model profile then and form proper vector from this representative frame image; Utilize first sorter to carry out the identification of human body target;
D). according to the result of human body target identification, if decidable is the stranger, then master control system is reported to the police through control bus startup this locality or network alarming device; If can not accurately judge whether to be 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 through 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 device 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 through human eye,, allow master control system that this moving target is operated mandate if be judged as the people of understanding; Otherwise be judged to be the stranger of invasion, the user reports to the police through network terminal control master control system or stops the operation of this moving target in 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. according to claim 1 or claim 2 intrusion detection 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 the ratio of width to height of moving target earlier, and judge whether 0.28～0.36; Calculate the vertical direction projection histogram, find out near the local maximum in the crown, confirm head width; Calculate a shoulder length degree at last, set up head shoulder model then; When extracting the failure of a 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, the second used sorter overall sorter and the local classifiers mode through weighted sum of serving as reasons walks abreast and integrates whole sorter.
6. like 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 through this locality receives the image from the cloud platform, carry out the mutual and data transmission of control signal simultaneously.
CN 201110427589 2011-12-19 2011-12-19 Method for detecting and identifying intrusion Expired - Fee Related CN102521578B (en)

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Cited By (21)

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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
CN104079881A (en) * 2014-07-01 2014-10-01 中怡(苏州)科技有限公司 Monitoring device and monitoring method related to monitoring 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
CN105006089A (en) * 2015-07-01 2015-10-28 国家电网公司 Safety monitoring alarm method and system based on images
CN105100724A (en) * 2015-08-13 2015-11-25 电子科技大学 Remote and safe intelligent household monitoring method and device based on visual 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
CN105654647A (en) * 2016-01-28 2016-06-08 中北大学 Identification method for judging home invasion in real time
CN105744345A (en) * 2014-12-12 2016-07-06 深圳Tcl新技术有限公司 Video compression method and video compression device
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
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
CN108781276A (en) * 2016-03-11 2018-11-09 株式会社专业无人机 Organism search system
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
CN109145766A (en) * 2018-07-27 2019-01-04 北京旷视科技有限公司 Model training method, device, recognition methods, electronic equipment and storage medium
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
CN109460787A (en) * 2018-10-26 2019-03-12 北京交通大学 IDS Framework method for building up, device and data processing equipment
CN109643480A (en) * 2016-07-22 2019-04-16 路晟（上海）科技有限公司 Security system and method
CN110895663A (en) * 2018-09-12 2020-03-20 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
CN111191498A (en) * 2019-11-07 2020-05-22 腾讯科技（深圳）有限公司 Behavior recognition method and related product
CN112532938A (en) * 2020-11-26 2021-03-19 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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Cited By (26)

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Publication number Priority date Publication date Assignee Title
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
CN104079881A (en) * 2014-07-01 2014-10-01 中怡(苏州)科技有限公司 Monitoring device and monitoring method related to monitoring device
CN105744345A (en) * 2014-12-12 2016-07-06 深圳Tcl新技术有限公司 Video compression method and video compression 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
CN105006089A (en) * 2015-07-01 2015-10-28 国家电网公司 Safety monitoring alarm method and system based on images
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
CN105100724A (en) * 2015-08-13 2015-11-25 电子科技大学 Remote and safe intelligent household monitoring method and device based on visual 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
CN105654647A (en) * 2016-01-28 2016-06-08 中北大学 Identification method for judging home invasion in real time
CN108781276A (en) * 2016-03-11 2018-11-09 株式会社专业无人机 Organism search system
CN111401237A (en) * 2016-03-11 2020-07-10 株式会社专业无人机 Organism 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
CN109643480A (en) * 2016-07-22 2019-04-16 路晟（上海）科技有限公司 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
CN109145766A (en) * 2018-07-27 2019-01-04 北京旷视科技有限公司 Model training method, device, recognition methods, electronic equipment and storage medium
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
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CN109460787A (en) * 2018-10-26 2019-03-12 北京交通大学 IDS Framework method for building up, 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
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