CN105225249B - High ferro platform crosses the border detection method - Google Patents
High ferro platform crosses the border detection method Download PDFInfo
- Publication number
- CN105225249B CN105225249B CN201410715458.5A CN201410715458A CN105225249B CN 105225249 B CN105225249 B CN 105225249B CN 201410715458 A CN201410715458 A CN 201410715458A CN 105225249 B CN105225249 B CN 105225249B
- Authority
- CN
- China
- Prior art keywords
- image
- platform
- detection
- pattern
- coordinate
- 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.)
- Active
Links
Landscapes
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
A kind of high ferro platform crosses the border detection method, can be arranged as required to monitoring regional extent, monitors the red floor tile region on the outside of white line, when passenger is when platform is waited, if into platform monitoring area, system sends out prompting alarm automatically, and prompting picks up attendant and passengers please far from danger zone.Additionally, due to the technology for employing real-time background updating, the present invention can overcome illumination variation round the clock and the external environments such as daily Changes in weather influence throughout the year, and natural conditions variation is avoided to interfere detection.
Description
Technical field
The present invention relates to the technical field of picture recognition, specifically a kind of high ferro platform crosses the border detection method.
Background technology
The detection of high ferro platform is mainly monitored the red floor tile region of platform edge, since high ferro platform is than in platform
High-speed railway rail will height, so close to the region be the easy danger for occurring to be slipped to from platform on track when there is passenger, meanwhile,
When high ferro is arrived at a station, passenger also be easy to cause risk of collision into this region.High ferro platform from the platform region that passenger waits to
Inner orbit region of standing is divided into, and platform is waited area, and yellow sidewalk for visually impaired people area, cross the border warning white line, waits and forbids entering red floor tile
Region and platform track for a train region.
Invention content
It crosses the border detection method the technical problem to be solved in the present invention is to provide a kind of high ferro platform.
The present invention is adopted the technical scheme that solve technical problem present in known technology:
The high ferro platform of the present invention crosses the border detection method, includes the following steps：
A, detection starts, loading system parameter；
B, image is loaded into, camera is carried out and self-adjusting judgement is carried out to image；
C, image preprocessing establishes image buffers；
D, background modeling is carried out to image, determines the detection range in platform region：The region position detected according to actual needs
It puts and clicks corresponding tie point on the video images with size；Selected dot sequency is connected, confirms marked region；Mark
Determine the safe line position of white on platform, prediction drop shadow spread of the calibration people on platform, since there are illumination, the shadows in environment
Range needs be excluded from image procossing, so need predict drop shadow spread；By region labeling point, white safety line and people
The image coordinate of prediction drop shadow spread is combined, as the systematic parameter of detection zone, when reloading systematic parameter, according to
The parameter coordinate of detection zone automatically generates the image template of detection zone, realizes the real image extraction to detection zone；
E, foreground extraction is carried out to image；
F, judge whether the foreground object in image is detection target, i.e., whether is people on platform；
G, during the non-inlet parking of train, the detection target in tracking prospect enters above-mentioned detection when there is target in image
In region, i.e., when someone crosses the border on platform, system carries out geofence；When pull in stopping after, detection zone switchs to can be into
Enter region, detection of crossing the border stops, and system is no longer alarmed.
The present invention can also use following technical measures：
The detection zone parameter calibration formula is：I (x, y)=α * I ' (x, y)+δ,
I (x, y) is coordinate in image, and I ' (x, y) is practical platform coordinate, and α is transformation coefficient, and δ is empirical parameter.
Assuming that the real space coordinate in prosecution region, in the plane of coordinate system Z '=0, detection zone is by a group echo point
Connection composition, is denoted as I 'i(xi, yi), while corresponding mark point I is found in camera image coordinate system Zi(xi, yi) and record
Coordinate value calculates coordinate transform factor alpha by coordinate transform.
Adaptive Gauss background modeling is carried out to detection zone, by the foreground and background in perimeter detection area image point
It opens, while is analyzed for prospect.
The Gaussian Background modeling includes the following steps：
A, each new pixel value XtIt is compared as the following formula with current K model, point until finding matching new pixel value
Cloth model, i.e., with the mean bias of the model in 2.5 σ
|Xt-μI, t-1|≤2.5σI, t-1
If b, the matched pattern of institute meets context request, which belongs to background, otherwise belongs to prospect；
C, each schema weight updates as follows, and wherein α is learning rate, for matched pattern MK, t=1, otherwise
MK, t=0, then the weight of each pattern be normalized；
wK, t=(1- α) * wK, t-1+α*MK, t
D, the mean μ of non-match pattern and standard deviation sigma are constant, and the parameter of match pattern is updated according to equation below：
ρ=α * η (Xt|μk, σk)
μt=(1- ρ) * μt-1+ρ*Xt
If e, not having any pattern match in a steps, the pattern of weight minimum is replaced, i.e. the mean value of the pattern is
Current pixel, standard deviation are initialization higher value, and weight is smaller value；
F, each pattern is according to w/ α2It arranges in descending order, weight is big, and the small pattern arrangement of standard deviation is forward；
G, for B pattern as background, B meets following formula before selecting, and parameter T represents background proportion,
The invention has the advantages and positive effects that:
The high ferro platform of the present invention crosses the border detection method, can be arranged as required to monitoring regional extent, and main monitoring exists
Red floor tile region on the outside of white line, when passenger is when platform is waited, if into platform monitoring area, system sends out prompting automatically
Alarm, prompting pick up attendant and passengers please far from danger zone.Additionally, due to the technology for employing real-time background updating, this hair
It is bright to overcome illumination variation round the clock and the throughout the year influence of the external environments such as daily Changes in weather, avoid natural conditions variation pair
Detection interferes.
Description of the drawings
The high ferro platform that Fig. 1 is the present invention crosses the border the flow chart of detection method.
Specific embodiment
The high ferro platform of the present invention crosses the border detection method, includes the following steps：
A, detection starts, loading system parameter；
B, image is loaded into, camera is carried out and self-adjusting judgement is carried out to image, is sentenced by carrying out Image Adjusting to infrared image
It is disconnected, it avoids detecting mistake caused by carrying out image pixel adjustment due to camera, loading figure again after then adjusting if you need to self-adjusting
Picture；
C, image preprocessing establishes image buffers, and infrared image Gaussian smoothing is pre-processed, and reduces picture noise to detection
The influence of arithmetic result；
D, background modeling is carried out to image, determines the detection range in platform region：The region position detected according to actual needs
It puts and clicks corresponding tie point on the video images with size；Selected dot sequency is connected, confirms marked region；Mark
Determine the safe line position of white on platform, prediction drop shadow spread of the calibration people on platform；By region labeling point, white safety line
The image coordinate for predicting drop shadow spread with people is combined, as the systematic parameter of detection zone, when reloading systematic parameter,
The image template of detection zone is automatically generated according to the parameter coordinate of detection zone, realizes and the real image of detection zone is carried
It takes；
E, foreground extraction is carried out to image；
F, judge whether the foreground object in image is detection target, i.e., whether is people on platform；
G, during the non-inlet parking of train, tracing detection target, calculate target position and warning zone with the presence or absence of sky
Between on coincidence, when there is target to enter in above-mentioned detection zone in image, i.e., when someone crosses the border on platform, system is crossed the border
Alarm；When pull in parking after, detection zone switchs to accessible area, and detection of crossing the border stops, and system no longer alarms.
The detection zone parameter calibration formula is：I (x, y)=α * I ' (x, y)+δ,
I (x, y) is coordinate in image, and I ' (x, y) is practical platform coordinate, and α is transformation coefficient, and δ is empirical parameter.
Assuming that the real space coordinate in prosecution region, in the plane of coordinate system Z ＇=0, detection zone is by a group echo point
Connection composition, is denoted as I 'i(xi, yi), while corresponding mark point I is found in camera image coordinate system Zi(xi, yi) and record
Coordinate value calculates coordinate transform factor alpha by coordinate transform.
Adaptive Gauss background modeling is carried out to detection zone, by the foreground and background in perimeter detection area image point
It opens, while is analyzed for prospect.
Gaussian Background modeling includes the following steps：
A, each new pixel value XtIt is compared as the following formula with current K model, point until finding matching new pixel value
Cloth model, i.e., with the mean bias of the model in 2.5 σ
|Xt-μI, t-1|≤2.5σI, t-1
If b, the matched pattern of institute meets context request, which belongs to background, otherwise belongs to prospect；
C, each schema weight updates as follows, and wherein α is learning rate, for matched pattern MK, t=1, otherwise
MK, t=0, then the weight of each pattern be normalized；
wK, t=(1-α)*wK, t-1+α*MK, t
D, the mean μ of non-match pattern and standard deviation sigma are constant, and the parameter of match pattern is updated according to equation below：
ρ=α * η (Xt|μk, σk)
μt=(1- ρ) * μt-1+ρ*Xt
If e, not having any pattern match in a steps, the pattern of weight minimum is replaced, i.e. the mean value of the pattern is
Current pixel, standard deviation are initialization higher value, and weight is smaller value；
F, each pattern is according to w/ α2It arranges in descending order, weight is big, and the small pattern arrangement of standard deviation is forward；
G, for B pattern as background, B meets following formula before selecting, and parameter T represents background proportion；
Due to there are the influence that scene illumination changes, for example, the cloudy day is different with brightness in fine day platform；Daytime is due to the sun
Position is different, so sunlight is different in the range of platform；Night exists since platform opens headlamp, and brightness becomes high ring
Border changes.The method that we employ real-time background updating carries out Adaptive Gauss model background modeling for detection zone, will
The foreground and background that platform crosses the border in detection zone image separates, meanwhile, it is analyzed for prospect.Meanwhile with reference to detection zone
Learning rate parameter during the brightness of image variation adjustment background modeling in domain, the wherein variation range of parameter 0.01~
Between 0.001, algorithm is made to greatly improve the Stability and veracity of target detection.
The above described is only a preferred embodiment of the present invention, not make limitation in any form to the present invention, though
The right present invention has been described by way of example and in terms of the preferred embodiments, however, the present invention is not limited to, any technology people for being familiar with this profession
Member without departing from the scope of the present invention, can utilize the technology contents disclosed to make a little change or modification certainly, into
For the equivalent embodiment of equivalent variations, as long as being the content without departing from technical solution of the present invention, technical spirit according to the present invention
To any simple modification, equivalent change and modification that above example is made, belong in the range of technical solution of the present invention.
Claims (3)
- The detection method 1. a kind of high ferro platform crosses the border, which is characterized in that include the following steps：A, detection starts, loading system parameter；B, image is loaded into, camera is carried out and self-adjusting judgement is carried out to image；C, image preprocessing establishes image buffers；D, background modeling is carried out to image, determines the detection range in platform region：The regional location that detects according to actual needs and Size clicks corresponding tie point on the video images；Selected dot sequency is connected, confirms marked region；Calibration station The safe line position of white on platform, prediction drop shadow spread of the calibration people on platform；By region labeling point, white safety line and people The image coordinate of prediction drop shadow spread is combined, as the systematic parameter of detection zone, when reloading systematic parameter, according to The parameter coordinate of detection zone automatically generates the image template of detection zone, realizes the real image extraction to detection zone；E, foreground extraction is carried out to image；F, judge whether the foreground object in image is detection target, i.e., whether is people on platform；G, during the non-inlet parking of train, the detection target in tracking prospect enters above-mentioned detection zone when there is target in image In, i.e., when someone crosses the border on platform, system carries out geofence；When pull in stopping after, detection zone switchs to that area can be entered Domain, detection of crossing the border stop, and system is no longer alarmed；Wherein, the parameter calibration formula of detection zone is：I (x, y)=α * I ' (x, y)+δ,I (x, y) is coordinate in image in this formula, and I ' (x, y) is practical platform coordinate, and α is transformation coefficient, and δ is empirical parameter；In the plane of coordinate system Z '=0, detection zone is made of the real space coordinate of detection zone group echo point connection, It is denoted as I 'i(xi, yi), while corresponding mark point I is found in camera image coordinate system Zi(xi, yi), and coordinate value is recorded, it passes through It crosses coordinate transform and calculates coordinate transform factor alpha.
- The detection method 2. high ferro platform according to claim 1 crosses the border, it is characterised in that：Detection zone is carried out adaptive Gaussian Background models, and the foreground and background in perimeter detection area image is separated, while is analyzed for prospect.
- The detection method 3. high ferro platform according to claim 2 crosses the border, it is characterised in that：Adaptive Gauss background modeling packet Include following steps：A, each new pixel value XtIt is compared as the following formula with current K model, the distributed mode until finding matching new pixel value Type, i.e., with the mean bias of the model in 2.5 σ|Xt-μI, t-1|≤2.5σI, t-1If b, the matched pattern of institute meets context request, which belongs to background, otherwise belongs to prospect；C, each schema weight updates as follows, and α is learning rate in formula, for matched pattern MK, t=1, otherwise MK, t =0, then the weight of each pattern be normalized；wK, t=(1- α) * wK, t-1+α*MK, tD, the mean μ of non-match pattern and standard deviation sigma are constant, and the parameter of match pattern is updated according to equation below：ρ=α * η (Xt|μk, σk)μt=(1- ρ) * μt-1+ρ*XtIf e, not having any pattern match in a steps, the pattern of weight minimum is replaced, i.e. the mean value of the pattern is current Pixel, standard deviation are initialization higher value, and weight is smaller value；F, each pattern is according to w/ α2It arranges in descending order, weight is big, and the small pattern arrangement of standard deviation is forward；G, for B pattern as background, B meets following formula before selecting, and parameter T represents background proportion,
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410715458.5A CN105225249B (en) | 2014-12-01 | 2014-12-01 | High ferro platform crosses the border detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410715458.5A CN105225249B (en) | 2014-12-01 | 2014-12-01 | High ferro platform crosses the border detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105225249A CN105225249A (en) | 2016-01-06 |
| CN105225249B true CN105225249B (en) | 2018-06-12 |
Family
ID=54994198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410715458.5A Active CN105225249B (en) | 2014-12-01 | 2014-12-01 | High ferro platform crosses the border detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105225249B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109920186A (en) * | 2019-04-19 | 2019-06-21 | 沈阳风驰软件股份有限公司 | A kind of detection of platform edge and geofence control system and method |
| CN110009857A (en) * | 2019-05-14 | 2019-07-12 | 沈阳风驰软件股份有限公司 | A kind of detection of platform one-meter line and geofence control system |
| CN113034866A (en) * | 2021-03-01 | 2021-06-25 | 哈尔滨市科佳通用机电股份有限公司 | Railway platform line crossing detection device and line crossing monitoring method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002197445A (en) * | 2000-12-26 | 2002-07-12 | Railway Technical Res Inst | Detector for abnormality in front of train utilizing optical flow |
| JP2007317052A (en) * | 2006-05-29 | 2007-12-06 | Japan Airlines International Co Ltd | System for measuring waiting time for lines |
| CN101635833A (en) * | 2008-07-22 | 2010-01-27 | 深圳市朗驰欣创科技有限公司 | Method, device and system for video monitoring |
| CN102663405A (en) * | 2012-05-14 | 2012-09-12 | 武汉大学 | Prominence and Gaussian mixture model-based method for extracting foreground of surveillance video |
| JP5045449B2 (en) * | 2008-01-17 | 2012-10-10 | 株式会社明電舎 | Environment-adaptive intruder detection device by image processing |
| CN103488993A (en) * | 2013-09-22 | 2014-01-01 | 北京联合大学 | Crowd abnormal behavior identification method based on FAST |
-
2014
- 2014-12-01 CN CN201410715458.5A patent/CN105225249B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002197445A (en) * | 2000-12-26 | 2002-07-12 | Railway Technical Res Inst | Detector for abnormality in front of train utilizing optical flow |
| JP2007317052A (en) * | 2006-05-29 | 2007-12-06 | Japan Airlines International Co Ltd | System for measuring waiting time for lines |
| JP5045449B2 (en) * | 2008-01-17 | 2012-10-10 | 株式会社明電舎 | Environment-adaptive intruder detection device by image processing |
| CN101635833A (en) * | 2008-07-22 | 2010-01-27 | 深圳市朗驰欣创科技有限公司 | Method, device and system for video monitoring |
| CN102663405A (en) * | 2012-05-14 | 2012-09-12 | 武汉大学 | Prominence and Gaussian mixture model-based method for extracting foreground of surveillance video |
| CN103488993A (en) * | 2013-09-22 | 2014-01-01 | 北京联合大学 | Crowd abnormal behavior identification method based on FAST |
Non-Patent Citations (1)
| Title |
|---|
| "行人异常智能视频监控系统研究与实现";赵凤娟;《中国优秀硕士学位论文全文数据库 信息科技辑》;20111215(第12期);论文第13-28、63页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105225249A (en) | 2016-01-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105260719B (en) | Railroad platform gets over line detecting method | |
| CN106144796B (en) | Depth sensor based occupant sensing for air passenger transport envelope determination | |
| CN104470058B (en) | A kind of control method of subregion lighting energy-saving switch control system | |
| CN106144801B (en) | Depth sensor based sensing for special passenger transport vehicle load conditions | |
| CN106144861B (en) | Depth sensor based passenger sensing for passenger transport control | |
| CN106144797B (en) | Traffic list generation for passenger transport | |
| CN106144798B (en) | Sensor fusion for passenger transport control | |
| CN106144862B (en) | Depth sensor based passenger sensing for passenger transport door control | |
| CN204680085U (en) | Based on the quick authentication gate control system of recognition of face | |
| CN104828664B (en) | Automatic debugging system and method | |
| CN105225249B (en) | High ferro platform crosses the border detection method | |
| CN102332167A (en) | Target detection method for vehicles and pedestrians in intelligent traffic monitoring | |
| CN103986906A (en) | Door opening and closing detection method based on monitoring videos | |
| CN109878552A (en) | Monitoring foreign bodies device between rail traffic platform door based on machine vision and train | |
| KR101117071B1 (en) | Energy saving apparatus for crosswalk safty lighting adaptively according to various conditions | |
| JP2015120573A (en) | Elevator with image recognition function | |
| CN108012075B (en) | Integrated control method and system for monitoring equipment and lighting equipment | |
| CN109145736B (en) | A kind of detection method that the subway station pedestrian based on video analysis inversely walks | |
| CN110969131B (en) | Subway people flow counting method based on scene flow | |
| CN105303582B (en) | High ferro platform perimeter detection method | |
| CN110533011B (en) | Detection method and detection system for issuing leaflet behavior | |
| CN108280427A (en) | A kind of big data processing method based on flow of the people | |
| CN105279767B (en) | Train arrives at a station the recognition methods of state | |
| CN104361317B (en) | A kind of bayonet type video analysis is not fastened the safety belt behavioral value system and method | |
| JP2006254206A (en) | Image signal processing apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP02 | Change in the address of a patent holder | ||
| CP02 | Change in the address of a patent holder |
Address after: 300450 West 3-303, 18 Haitai West Road, Huayuan Industrial Zone, Binhai New Area, Tianjin Patentee after: TIANJIN OPTICAL ELECTRICAL GAOSI COMMUNICATION ENGINEERING TECHNOLOGY Co.,Ltd. Address before: No.6, Taishan Road, Hexi District, Tianjin 300210 Patentee before: TIANJIN OPTICAL ELECTRICAL GAOSI COMMUNICATION ENGINEERING TECHNOLOGY Co.,Ltd. |