CN108174162A - A kind of city video monitoring space optimization method based on the POI that registers - Google Patents
A kind of city video monitoring space optimization method based on the POI that registers Download PDFInfo
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Abstract
The present invention relates to geography information and space optimization technical field more particularly to a kind of city video monitoring space optimization methods based on the POI that registers.A kind of city video monitoring space optimization method based on the POI that registers, including：Maximum is monitored Probability Point as candidate point in all units in selection monitoring area；Using monitored space intra domain user register POI data calculate candidate point at camera field；The unit of camera field covering at candidate point is removed, the maximum chosen in remaining unit is monitored Probability Point；Judge whether all units are all covered in monitoring area；Video monitoring optimization is carried out with reference to maximal cover Optimized model.The present invention improves the science of video monitoring deployment, reduces monitoring covering Duplication, reduces monitoring blind area.
Description
Technical field
The present invention relates to geography information and space optimization technical fields more particularly to a kind of city based on the POI that registers to regard
Frequency monitoring space optimization method.
Background technology
With the continuous propulsion that the fast development of China's urbanization is built with safe city, municipal public safety becomes at different levels
One of focal issue of attention from government；The video monitoring system for Urban Public Space is built, becomes and ensures public safety, prestige
Fear the key link of criminal offence.It is minimum with effective monitoring model based on construction cost in the range of specific Urban Public Space
Maximum double goal constraint is enclosed, how in specific position science configuration certain amount, different types of monitoring camera, and is dropped
Low or reduction monitoring blind area becomes the Basic Problems of extensive video monitoring system construction.
From the point of view of influence factor, video monitoring is by camera parameters (such as focal length, field of view angle, visual range), prison
Control the influence of the Multiple factors such as scene (such as building circumstance of occlusion), supervision subjects (people/vehicle etc.).Current monitor system deployment
When majority only consider some factors, based on having had experience or simply estimated, the artificial monitoring probe for selecting respective model,
Several key positions (such as intersection, block entrance) of monitoring area carry out video camera deployment.
(1) video monitoring system deployment at present comes in and goes out mainly in combination with monitoring area and its internal passageway intersection, building
Mouth is disposed, and does not consider the influence of supervision subjects factor and monitor camera parameter factor.
(2) video monitoring system deployment at present, mainly chooses camera installation locations, lacks to monitor camera quantity
And its optimization and the configuration process of type parameter.
(3) monitoring system deployment at present has certain subjectivity based on experience, leads to monitor camera covering overlapping
Rate is high, and there are more monitoring blind areas.
Invention content
The problem of for above-mentioned current video monitoring system deployment, the present invention propose a kind of based on registering POI's
City video monitoring space optimization method improves the science of video monitoring deployment, reduces monitoring covering Duplication, reduces prison
Control blind area.
To achieve these goals, the present invention uses following technical scheme：
A kind of city video monitoring space optimization method based on the POI that registers, includes the following steps：
Step 1：Maximum is monitored Probability Point as candidate point in all grid cells in selection monitoring area；
Step 2：Using monitored space intra domain user register POI data calculate candidate point at camera field；
Step 3：The grid cell of camera field covering at candidate point is removed, the maximum chosen in remaining grid cell is monitored
Probability Point；
Step 4：Judge whether all grid cells are all covered in monitoring area, if it is not, then carrying out step 1；If so, into
Row is in next step；
Step 5：Video monitoring optimization is carried out with reference to maximal cover Optimized model.
Preferably, the video camera includes spherical camera and gun shaped video camera.
Preferably, it is further included before the step 1：
Divide monitoring area grid：Range is taken up an area according to monitoring area range and building, chooses fixed-size grid cell,
Monitoring area is subjected to spatial spreading, is divided into fixed-size grid cell, and rejects the grid list of building covering part
Member；
Calculate monitoring area visual field：Using grid cell each in monitoring area as viewpoint, using blinding analysis method, calculate every
The field range of one grid cell；
Calculate maximum monitored probability：According to each grid cell field range result of calculation, applying equation (1) calculates monitoring area
The monitored probability of interior each grid cell, the maximum value of the monitored probability is maximum monitored probability：
In formula, probability that P (g | D) is monitored for grid cell g in all grid cell set D of monitoring area, cgFor g grids
Unit is contained in the number of other grid cell field ranges, and n is grid cell number in D, FOViVisual field for i grid cells
Range.
Preferably, the step 2 includes：
Step 2.1：The spherical camera ken is identified：Pass through grid cell field range where calculating candidate point position
The ratio between area and perimeter identify that for round or subcircular candidate point, visual field is calculated with grid cell where candidate point for the ken
Farthest visual range during range is as radius；
Step 2.2：Analyze POI cuclear density of registering：It is registered POI data by acquiring social network user, is analyzed using cuclear density
Method calculates each grid cell cuclear density value in monitoring area；
Step 2.3：Calculate the monitoring direction of gun type camera：Vertex p using candidate point as the ken, the ken are sector,
The cuclear density maximum of points q that candidate point field range is covered is chosen, with vectorDirection is monitoring direction；
Step 2.4：Gun type camera monitoring distance calculates：Straightway pq and the intersection point t on candidate point field range boundary are calculated, with
Straightway pt length is fan-shaped radius R；
Step 2.5：Calculate the angle of gun type camera camera lens：Using pq as initial line is played, expanded- angle step-length is set, respectively to pq two
Ray is made in side extension, calculates distance S between the intersection point in its range face with the ken and p points and the intersection point；
Step 2.6：Judge whether R is more than S, if it is not, then continuing to execute step 2.5；If so, stopping extension, intersection point is write down；
Step 2.7：Generate the ken of video camera：According to the round ken of identification, spherical camera is drawn with reference to its center of circle, radius
The range of the ken；According to selected candidate point, monitoring direction, radius and central angle, the range of the gun type camera ken is drawn.
Preferably, the intersection point has 2, respectively positioned at pq both sides.
Preferably, angle of the central angle between 2 intersection points and the line of sector vertex p, central angle is gun-type
The field angle of video camera.
Preferably, the step 5 includes：
Step 5.1：Determine the MCLP model optimization parameters of video monitoring optimization：Overlapping monitoring area grid cell and video camera regard
The range in domain reads camera field set and the grid cell information of each ken covering, determines MCLP model optimization parameters,
The MCLP models are maximal cover Optimized model；
Step 5.2：Build the MCLP models of video monitoring optimization：According to determining MCLP model optimization parameters, setting monitoring is taken the photograph
Camera quantity constructs MCLP models according to the following formula：
∑jxj=m (5)
Wherein, i is demand point, and j is candidate point, NiIt is the set for the j for covering demand point i, aiFor the weight of demand point i, m is waits
The number of reconnaissance；xj、yiFor decision variable, facility layout is when candidate point j, xj=1, it is otherwise 0；When demand point i is capped,
yi=1, it is otherwise 0；
Step 5.3：Video monitoring optimizes MCLP model solutions：Solution is optimized, and reads optimum results, is supervised after obtaining optimization
Control camera position and coverage area as a result, and read corresponding parameter, the parameter include monitoring distance, monitoring towards, regard
Rink corner；
Step 5.4：Video monitoring prioritization scheme is evaluated：Ken coverage rate, Duplication and three indexs of rate that are blocked are chosen, to excellent
Change result and carry out evaluation analysis；Combining assessment result forms final video monitoring deployment and monitor camera selecting type scheme.
Compared with prior art, the device have the advantages that：
The present invention is monitored probability with reference to the maximum of grid cell each in monitoring area, realizes candidate using POI data of registering
Point iteration is chosen；Using grid cell in monitoring area as demand point, using camera field as the facilities services area of coverage, using maximum
Coverage optimization model carries out video monitoring space optimization.The present invention has considered multiple influence factors, significantly reduces monitoring
Covering Duplication and the rate that is blocked reduce monitoring blind area, improve monitoring coverage percentage, and provide monitoring distance, monitoring court simultaneously
To relevant parameters such as, field angles, so as to form scientific and reasonable monitoring deployment scheme.
Description of the drawings
Fig. 1 is a kind of basic procedure schematic diagram of the city video monitoring space optimization method based on the POI that registers of the present invention
One of.
Fig. 2 is a kind of basic procedure schematic diagram of the city video monitoring space optimization method based on the POI that registers of the present invention
Two.
Fig. 3 is a kind of camera field plane of the city video monitoring space optimization method based on the POI that registers of the present invention
Schematic diagram.
Fig. 4 is monitored for a kind of grid cell of the city video monitoring space optimization method based on the POI that registers of the present invention
Probability schematic diagram.
Fig. 5 is that a kind of candidate point of city video monitoring space optimization method based on the POI that registers of the present invention and correspondence regard
Domain schematic diagram.
Fig. 6 is a kind of video monitoring optimization knot of city video monitoring space optimization method based on the POI that registers of the present invention
Fruit curve graph.
Fig. 7 is a kind of 90% coverage rate target of the city video monitoring space optimization method based on the POI that registers of the present invention
Lower camera position and scope of sight schematic diagram.
Specific embodiment
In order to make it easy to understand, explanation explained below is made to the part noun occurred in the specific embodiment of the present invention：
POI：Point Of Interest, point of interest, POI are the most crucial data based on location-based service.
Below in conjunction with the accompanying drawings with specific embodiment the present invention will be further explained explanation：
Embodiment one：
As shown in Figure 1, a kind of city video monitoring space optimization method based on the POI that registers of the present invention, includes the following steps：
Step S101：Maximum is monitored Probability Point as candidate point in all grid cells in selection monitoring area.
Step S102：Using monitored space intra domain user register POI data calculate candidate point at camera field.
Step S103：The grid cell of camera field covering at candidate point is removed, is chosen in remaining grid cell most
Probability Point monitored greatly.
Step S104：Judge whether all grid cells are all covered in monitoring area, if it is not, then carrying out step
S101；If so, it carries out in next step.
Step S105：Video monitoring optimization is carried out with reference to maximal cover Optimized model.
Embodiment two：
As shown in Fig. 2, another city video monitoring space optimization method based on the POI that registers of the present invention, including following step
Suddenly：Step S201：Divide monitoring area grid：
Range is taken up an area according to monitoring area range and building, chooses fixed-size grid cell, monitoring area is carried out empty
Between it is discrete, be divided into fixed-size grid cell, and reject the grid of building covering part.
Step S202：Calculate monitoring area visual field：
Using grid cell each in monitoring area as viewpoint, using blinding analysis method, the visual field of each grid cell is calculated
Range；As a kind of embodiment, using Line of Sight (LOS) blinding analysis method, each grid cell is calculated
Field range.
Step S203：Calculate maximum monitored probability：
According to each grid cell field range result of calculation, applying equation (1) calculates the quilt of each grid cell in monitoring area
Probability is monitored, the maximum value of the monitored probability is maximum monitored probability：
In formula, probability that P (g | D) is monitored for a certain grid cell g in all grid cell set D of monitoring area, cgFor g
Grid cell is contained in the number of other grid cell field ranges, and n is grid cell number in D, FOViFor i grid cells
Field range.
Step S204：Maximum is monitored Probability Point as candidate point in all grid cells in selection monitoring area.
Step S205：The range of corresponding camera field is calculated using monitored space intra domain user POI data of registering；It is described to take the photograph
Camera includes spherical camera and gun shaped video camera；Camera field on the left of wherein Fig. 3 for gun shaped video camera as shown in figure 3, regard
Domain,For the monitoring direction of gun shaped video camera, Fig. 3 right sides are the spherical camera ken；Including：
Step S2051：The spherical camera ken is identified：
By calculating the ratio between the area of grid cell field range where candidate point position and perimeter, identify the ken for circle or
The candidate point of subcircular, farthest visual range when calculating field range using grid cell where candidate point is as radius；
Step S2052：POI cuclear density of registering is analyzed, formula is as follows：
Wherein, h is bandwidth (h>0)；x-xiFor estimation point x to sample xiDistance (the d at placei)；K is quartic polynomial kernel function；Step
Rapid S2053：Calculate the monitoring direction of gun type camera：
Vertex p using candidate point as the ken, the ken are sector, choose the cuclear density that the candidate point field range is covered
Maximum of points q, with vectorDirection is monitoring direction；
Step S2054：Gun type camera monitoring distance calculates：
Straightway pq and the intersection point t on the candidate point field range boundary are calculated, using straightway pt length as fan-shaped radius R；
Step S2055：Calculate the angle of gun type camera camera lens：
Using pq as initial line is played, expanded- angle step-length is set, makees ray to the extension of pq both sides respectively, calculates its range face with the ken
Intersection point and p points and the intersection point between distance S；
Step S2056：Judge whether R is more than S, if it is not, then continuing to execute step S2055；If so, stopping extension, friendship is write down
Point, at this point, share 2 intersection points in pq both sides, using the line of this 2 intersection points and sector vertex p between angle as sector circle
Heart angle θ, the central angle θ is field angle；
Step S2057：Generate the ken of video camera：
According to the round ken of identification, spherical camera scope of sight is drawn with reference to its center of circle, radius；According to selected candidate
Point, monitoring direction, radius and central angle, draw gun type camera scope of sight.
Step S206：The grid cell of camera field covering at candidate point is removed, is chosen in remaining grid cell most
Probability Point monitored greatly.
Step S207：Judge whether all grid cells are all covered in monitoring area, if it is not, then carrying out step
S204；If so, it carries out in next step.
Step S208：Video monitoring optimization is carried out with reference to maximal cover Optimized model, including：
Step S2081：Determine the MCLP model optimization parameters of video monitoring optimization：
The range of monitoring area grid cell and camera field is overlapped, what reading camera field set and each ken covered
Grid cell information, determines MCLP model optimization parameters, and the MCLP models are maximal cover Optimized model；
Step S2082：Build the MCLP models of video monitoring optimization：
According to determining MCLP model optimization parameters, monitor camera quantity is set, constructs MCLP models according to the following formula：
∑jxj=m (5)
Wherein, i is demand point, and j is candidate point, NiIt is the set for the j for covering demand point i, aiFor the weight of demand point i, m is waits
The number of reconnaissance；xj、yiFor decision variable, facility layout is when candidate point j, xj=1, it is otherwise 0；When demand point i is capped,
yi=1, it is otherwise 0；
Step S2083：Video monitoring optimizes MCLP model solutions：
Solution is optimized, and reads optimum results, monitor camera position and coverage area are as a result, and read after obtaining optimization
Corresponding parameter, the parameter include field angle；
As a kind of embodiment, solution is optimized using GLPK optimizers；
Step S2084：Video monitoring prioritization scheme is evaluated：
Ken coverage rate, Duplication and three indexs of rate that are blocked are chosen, evaluation analysis is carried out to optimum results；Combining assessment knot
Fruit shape monitors deployment and monitor camera selecting type scheme into final video；
Ken coverage rate is defined as area coverage and monitoring area area ratio after camera field fusion；Duplication is defined as institute
Ken area ratio after having the sum of camera field area of laying and merging；The rate of being blocked is defined as camera field and is built
Object shield portions area with merge after ken area ratio.
As a kind of embodiment, choose between Zhengzhou City Erqi District Han Jianglu, Changjiang Road, Huai Nanjie, University Road
The POI data of registering of block chooses candidate monitoring location point, that is, candidate point 966, corresponding 966 prisons of generation as experimental data
Camera field is controlled, wherein the round ken 16, the fan-shaped ken 950.
Grid cell is monitored probability as shown in figure 4, the monitored probability in the wherein shallower region of color is higher, color compared with
It is relatively low that deep region is monitored probability.
Candidate point is distributed and the corresponding ken is as shown in Figure 5.
Video monitoring optimum results are as shown in fig. 6, as seen from the figure, with the growth of video camera number, coverage rate is in rising
Trend, when coverage rate reaches 90%, ascendant trend slows down.
The range of camera position and the ken under 90% coverage rate target as shown in fig. 7, include gun type camera altogether at this time
223, and different cameras includes many kinds of parameters such as different field angles, radius and monitoring direction；Spherical camera 16.
Illustrated above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (7)
- A kind of 1. city video monitoring space optimization method based on the POI that registers, which is characterized in that include the following steps：Step 1：Maximum is monitored Probability Point as candidate point in all grid cells in selection monitoring area；Step 2：Using monitored space intra domain user register POI data calculate candidate point at camera field；Step 3：The grid cell of camera field covering at candidate point is removed, the maximum chosen in remaining grid cell is monitored Probability Point；Step 4：Judge whether all grid cells are all covered in monitoring area, if it is not, then carrying out step 1；If so, into Row is in next step；Step 5：Video monitoring optimization is carried out with reference to maximal cover Optimized model.
- 2. a kind of city video monitoring space optimization method based on the POI that registers according to claim 1, feature exist In the video camera includes spherical camera and gun shaped video camera.
- 3. a kind of city video monitoring space optimization method based on the POI that registers according to claim 1, feature exist In the step 1 further includes before：Divide monitoring area grid：Range is taken up an area according to monitoring area range and building, chooses fixed-size grid cell, Monitoring area is subjected to spatial spreading, is divided into fixed-size grid cell, and rejects the grid list of building covering part Member；Calculate monitoring area visual field：Using grid cell each in monitoring area as viewpoint, using blinding analysis method, calculate every The field range of one grid cell；Calculate maximum monitored probability：According to each grid cell field range result of calculation, applying equation (1) calculates monitoring area The monitored probability of interior each grid cell, the maximum value of the monitored probability is maximum monitored probability：In formula, probability that P (g | D) is monitored for grid cell g in all grid cell set D of monitoring area, cgFor g grids Unit is contained in the number of other grid cell field ranges, and n is grid cell number in D, FOViVisual field for i grid cells Range.
- 4. a kind of city video monitoring space optimization method based on the POI that registers according to claim 2, feature exist In the step 2 includes：Step 2.1：The spherical camera ken is identified：Pass through grid cell field range where calculating candidate point position The ratio between area and perimeter identify that for round or subcircular candidate point, visual field is calculated with grid cell where candidate point for the ken Farthest visual range during range is as radius；Step 2.2：Analyze POI cuclear density of registering：It is registered POI data by acquiring social network user, is analyzed using cuclear density Method calculates each grid cell cuclear density value in monitoring area；Step 2.3：Calculate the monitoring direction of gun type camera：Vertex p using candidate point as the ken, the ken are sector, The cuclear density maximum of points q that candidate point field range is covered is chosen, with vectorDirection is monitoring direction；Step 2.4：Gun type camera monitoring distance calculates：Straightway pq and the intersection point t on candidate point field range boundary are calculated, with Straightway pt length is fan-shaped radius R；Step 2.5：Calculate the angle of gun type camera camera lens：Using pq as initial line is played, expanded- angle step-length is set, respectively to pq two Ray is made in side extension, calculates distance S between the intersection point in its range face with the ken and p points and the intersection point；Step 2.6：Judge whether R is more than S, if it is not, then continuing to execute step 2.5；If so, stopping extension, intersection point is write down；Step 2.7：Generate the ken of video camera：According to the round ken of identification, spherical camera is drawn with reference to its center of circle, radius The range of the ken；According to selected candidate point, monitoring direction, radius and central angle, the range of the gun type camera ken is drawn.
- 5. a kind of city video monitoring space optimization method based on the POI that registers according to claim 4, feature exist In the intersection point has 2, respectively positioned at pq both sides.
- 6. a kind of city video monitoring space optimization method based on the POI that registers according to claim 4, feature exist In angle of the central angle between 2 intersection points and the line of sector vertex p, central angle is the visual field of gun type camera Angle.
- 7. a kind of city video monitoring space optimization method based on the POI that registers according to claim 1, feature exist In the step 5 includes：Step 5.1：Determine the MCLP model optimization parameters of video monitoring optimization：Overlapping monitoring area grid cell and video camera regard The range in domain reads camera field set and the grid cell information of each ken covering, determines MCLP model optimization parameters, The MCLP models are maximal cover Optimized model；Step 5.2：Build the MCLP models of video monitoring optimization：According to determining MCLP model optimization parameters, setting monitoring is taken the photograph Camera quantity constructs MCLP models according to the following formula：∑jxj=m (5)Wherein, i is demand point, and j is candidate point, NiIt is the set for the j for covering demand point i, aiFor the weight of demand point i, m is waits The number of reconnaissance；xj、yiFor decision variable, facility layout is when candidate point j, xj=1, it is otherwise 0；When demand point i is capped, yi=1, it is otherwise 0；Step 5.3：Video monitoring optimizes MCLP model solutions：Solution is optimized, and reads optimum results, is supervised after obtaining optimization Control camera position and coverage area as a result, and read corresponding parameter, the parameter include monitoring distance, monitoring towards, regard Rink corner；Step 5.4：Video monitoring prioritization scheme is evaluated：Ken coverage rate, Duplication and three indexs of rate that are blocked are chosen, to excellent Change result and carry out evaluation analysis；Combining assessment result forms final video monitoring deployment and monitor camera selecting type scheme.
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|---|---|---|---|---|
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| CN110602438A (en) * | 2018-06-13 | 2019-12-20 | 浙江宇视科技有限公司 | Road network-based video monitoring layout optimization method and device |
| CN110751341A (en) * | 2019-10-29 | 2020-02-04 | 浪潮天元通信信息***有限公司 | Video planning analysis system and method based on Internet of things |
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| CN111932868A (en) * | 2020-06-23 | 2020-11-13 | 南京市公安局 | Road network-based video monitoring blind area detection method and system |
| CN111932870A (en) * | 2020-06-23 | 2020-11-13 | 南京市公安局 | Road network and visual field based blind area detection method and system |
| CN112434846A (en) * | 2020-11-11 | 2021-03-02 | 上海芯翌智能科技有限公司 | Method and apparatus for optimizing camera deployment |
| CN113538577A (en) * | 2021-06-10 | 2021-10-22 | 广州杰赛科技股份有限公司 | Multi-camera coverage optimization method, device, equipment and storage medium |
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| CN116071373A (en) * | 2023-03-01 | 2023-05-05 | 南通大学 | Automatic U-net model tongue segmentation method based on fusion PCA |
| CN117156111A (en) * | 2023-10-27 | 2023-12-01 | 长春长光睿视光电技术有限责任公司 | Coverage planning method of wide-area photoelectric imaging system based on static platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106027962A (en) * | 2016-05-24 | 2016-10-12 | 浙江宇视科技有限公司 | Video monitoring coverage rate calculation method and device, and video monitoring layout method and system |
| US9536019B2 (en) * | 2013-08-07 | 2017-01-03 | Axis Ab | Method and system for selecting position and orientation for a monitoring camera |
| CN106331618A (en) * | 2016-08-22 | 2017-01-11 | 浙江宇视科技有限公司 | Method and device for automatically confirming visible range of camera |
| CN106412402A (en) * | 2016-10-31 | 2017-02-15 | 浙江宇视科技有限公司 | Configuration method and apparatus of camera preset positions |
| CN106897417A (en) * | 2017-02-22 | 2017-06-27 | 东南大学 | A kind of construction method of the city space holographic map based on the fusion of multi-source big data |
-
2018
- 2018-01-05 CN CN201810012334.9A patent/CN108174162B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9536019B2 (en) * | 2013-08-07 | 2017-01-03 | Axis Ab | Method and system for selecting position and orientation for a monitoring camera |
| CN106027962A (en) * | 2016-05-24 | 2016-10-12 | 浙江宇视科技有限公司 | Video monitoring coverage rate calculation method and device, and video monitoring layout method and system |
| CN106331618A (en) * | 2016-08-22 | 2017-01-11 | 浙江宇视科技有限公司 | Method and device for automatically confirming visible range of camera |
| CN106412402A (en) * | 2016-10-31 | 2017-02-15 | 浙江宇视科技有限公司 | Configuration method and apparatus of camera preset positions |
| CN106897417A (en) * | 2017-02-22 | 2017-06-27 | 东南大学 | A kind of construction method of the city space holographic map based on the fusion of multi-source big data |
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