CN108052585A - The determination method of dynamic object in a kind of complex environment - Google Patents
The determination method of dynamic object in a kind of complex environment Download PDFInfo
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- CN108052585A CN108052585A CN201711307523.0A CN201711307523A CN108052585A CN 108052585 A CN108052585 A CN 108052585A CN 201711307523 A CN201711307523 A CN 201711307523A CN 108052585 A CN108052585 A CN 108052585A
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- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
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Abstract
The present invention provides dynamic object determination method in complex environment, and mainly include：Define several samples pictures, and samples pictures are carried out with the first granularity gridding processing to form the grid picture for including several griddings, and preserve to the database of GIS-Geographic Information System after data are described to grid picture filling geographical location information and environment and establish index association；Then, picture to be determined is based on the first granularity gridding processing mode and carries out gridding processing, and first time comparison is carried out with grid picture, to filter out several grid pictures for generating variation；Finally, the environment in the grid picture with same geographic location information and samples pictures is described into data and carries out second of comparison, to judging whether target is dynamic object.By the present invention, external environmental interference is effectively eliminated, the accurate judgement to dynamic object in complex scene is realized, and has the advantages that computing cost is small.
Description
Technical field
The present invention relates to Multimedia content analysis technical fields, specifically, are related to dynamic object in a kind of complex environment
Determination method.
Background technology
Inter-State boundary line needs accurately to judge dynamic object, and to the attribute of dynamic object (for example,
People, vehicle, object etc.) judged.At present, boundary line is usually by being manually monitored or on duty, so as to cause in complicated field
To the low-down defect of the monitoring efficiency of dynamic object in scape.Therefore, occur in the prior art using closed-circuit television system pair
Boundary line carries out concentration remote monitoring, but this prior art still rely on manually to the dynamic object that occurs in monitor video into
Row judges, therefore there is also the defects such as monitoring efficiency is low.
Further, since the limitation of video quality and traditional Video content analysis technique means etc., so that
It is not accurate enough to the judgement of the attribute of the dynamic object in complex scene, the problems such as erroneous judgement easily occurs, fails to report.
Finally, outdoors in environment, formed shade is floated on boundary line since there are illumination, misty rain snow, clouds
Monitor video be easy to cause great interference, so as in the presence of can not find dynamic object or be mistaken for moving by non-dynamic target
The defects of state target.This also greatly affected the accuracy judged dynamic object to a certain extent.
The content of the invention
Problem to be solved by this invention is that the dynamic object in complex scene is accurately judged, to reduce extraneous ring
The interference that the disturbing factor in border judges dynamic object so as to carry out monitoring exactly to dynamic object and judge, reduces simultaneously
Dynamic object in complex scene is carried out to judge required time complexity and space complexity expense.
For achieving the above object, the present invention provides dynamic object determination method in a kind of complex environment, including with
Lower step：
Step S1, several samples pictures are defined, and samples pictures are carried out with the first granularity gridding processing and is included with being formed
The grid picture of several griddings, the grid filling geographical location information in grid picture and environment are described to preserve after data to
The database of GIS-Geographic Information System, and establish index association；
Step S2, picture to be determined is based on the first granularity gridding processing mode and carries out gridding processing, and and step
It is right for the first time that the grid picture that data are described by filling geographical location information and environment preserved in database in S1 carries out
Than to filter out several grid pictures for generating variation, shown in the calculation formula such as following formula (1) of the first time comparison：
Δi=| Xi-X*|>ηi, i=1,2 ..., m (1)；
Wherein, ηiFor empirical parameter, ηiValue range be 0%~30%, X*It is to be preserved in database by filling ground
Reason location information and environment describe the grid picture of data, XiFor picture to be determined, m is grid selected when comparing for the first time
The quantity of picture, m take the positive integer more than or equal to 1；
Then, the environment in the grid picture with same geographic location information and samples pictures is described into data and carries out the
Secondary comparison, to judge whether the target in the grid picture region for generating variation is dynamic object, described second
Shown in the calculation formula of secondary comparison such as following formula (2)：
Δj=| Yj-Y*|>ηj, j=1,2 ..., n (2)；
Wherein, ηjFor empirical parameter, ηjValue range be 0%~30%, Y*It is to be preserved in database by filling ground
Reason location information and environment describe the grid picture of data, YjTo be to be determined and generate the grid picture of variation, n is right for second
Than when selected grid picture quantity, n takes the positive integer more than or equal to 1.
As a further improvement on the present invention, in the step S1 samples pictures by image capture device to Same Scene
Under Various Seasonal and/or the picture acquired in daytime change condition.
As a further improvement on the present invention, the environment describes data and describes data, daytime delta data, dynamic by season
State goal description data, non-dynamic goal description data, elevation data, dynamic object probability of occurrence data or non-dynamic target
One or two kinds of data above composition in probability of occurrence data, and mark unique environment description for each grid picture
Data and geographical location information.
As a further improvement on the present invention, formed grid is handled in the step S1 by the first granularity gridding
The resolution ratio of picture is 0.1m × 0.1m~1.0 × 1.0m.
As a further improvement on the present invention, the step S2 is further included：To filtering out several grid charts for generating variation
Piece carries out the processing of two level gridding at least once to handle smaller gridding granularity compared with the first granularity gridding, to be formed
Several sub-grids；The sub-grid includes all geographical location information and the environment description for generating that the grid picture of variation is included
Data.
As a further improvement on the present invention, the step S2 is further included：The grid for generating data variation is formed
Minimum enclosed rectangle, according to the order that area coverage in adjacent four quadrants is descending, successively to corresponding to four quadrants
Grid is handled with carrying out two level gridding at least once compared with the smaller section granularity of the first granularity gridding processing, to be formed
Several sub-grids, and the geographical location information of the affiliated superior grid of the sub-grid is closed in the database according to the index of foundation
Connection carries out Data Matching, finally to determine whether the target image that minimum enclosed rectangle is covered is dynamic object.
Compared with prior art, the beneficial effects of the invention are as follows：The present invention can effectively eliminate external environmental interference (example
Such as：Cloud floats pseudo- dynamic object caused by projected shade movement etc.), it will be non-dynamic so as to farthest avoid
State target is mistaken for dynamic object, realizes the accurate judgement to dynamic object in complex scene, reduces to non-dynamic target
It is determined as the False Rate of dynamic object, describes data in combination with geographical location information and environment, significantly reduce in complexity
Dynamic object is carried out in scene to judge required time complexity and space complexity expense.
Description of the drawings
Fig. 1 is the samples pictures in step S1；
Fig. 2 handles formed grid picture to carry out the first granularity gridding to Fig. 1；
Fig. 3 is the schematic diagram to filling geographical location information in grid picture；
Fig. 4 is by an image capture device samples pictures taken under some scene；
Fig. 5 is captured another under angle illustrated in fig. 4 by same image capture device under the same conditions
Pictures；
Fig. 6 carries out the first granularity gridding by Fig. 5 and handles the grid picture formed, and will have same geographic location letter
The grid picture of breath will generate the grid picture institute changed after carrying out secondary compare with the description data in picture illustrated in fig. 5
Target discrimination in the zone is the schematic diagram of dynamic object；
Fig. 7 is that the dynamic object in Fig. 6 carries out the schematic diagram that a two level gridding is handled；
The dynamic object that Fig. 8 is extracted for the minimum enclosed rectangle according to determined by the dynamic object shown in Fig. 7
Schematic diagram.
Specific embodiment
The present invention is described in detail for shown each embodiment below in conjunction with the accompanying drawings, but it should explanation, these
Embodiment is not limitation of the present invention, those of ordinary skill in the art according to these embodiment institute work energy, method,
Or equivalent transformation or replacement in structure, all belong to the scope of protection of the present invention within.
Join first shown in Fig. 1 to Fig. 3, be used to illustrate in present embodiment and the is based on to samples pictures and picture to be determined
One granularity gridding processing mode carries out the detailed process of first time gridding processing.
Join shown in Fig. 1, there is one tree A (being regarded as static object in advance in embodiment) in samples pictures 10.Such as
Multiple samples pictures 10 are referred to as samples pictures, and samples pictures are gathered by image capture device and obtained.In order to improve to complexity
The accuracy that dynamic object in environment is judged, in the present embodiment, the samples pictures 10 in step S1 are adopted by image
Collection equipment (such as video camera, camera) has both Various Seasonal, the different daytime change condition or both under Same Scene
When acquired photo.
Cutting is carried out with 10 × 10 gridding cutting scale to samples pictures 10 illustrated in fig. 1, to form 100 nets
The grid picture formatted, i.e. grid picture A00~grid picture A99 (shown in ginseng Fig. 2).So, the tree A institutes as static object
The grid picture of covering just includes grid picture A67, grid picture A68, grid picture A77 and grid picture A78.Then to this
Four grid picture filling geographical location information and environment are preserved after describing data into the database of geographic position information system,
And establish index association.Wherein, the environment describes data and describes data, daytime delta data, dynamic object description by season
Data, non-dynamic goal description data, elevation data, dynamic object probability of occurrence data or non-dynamic target probability of occurrence number
One or two kinds of data above composition in, and mark unique environment for each grid picture and describe data and geography
Location information.
Specifically, filling is performed respectively to 100 grid pictures included in grid picture A00~grid picture A99
Geographical location information and environment describe the operation of data.For example, describing data to the environment of grid picture A00 institutes standard and being：Summer
My god (can be specific to specific month and/or date)+daytime (can be specific to sometime)+meadow+longitude and latitude+elevation data+
Dynamic object probability of occurrence+objective attribute target attribute is (for example, the target that grid picture is belonged to is one tree, a road, an animal
Or people etc.), and above-mentioned all subenvironments are described data be indexed with grid picture A00 to associate.
Meanwhile join shown in Fig. 3, for there is definite static object (for example, tree A) in samples pictures 10, then to static mesh
The geographical location information of four covered grid pictures of mark is filled.Specifically, the geographical location information of grid picture A67
By (x1, y1), (x2, y2), (x4, y4) and (x5, y5) common programme, the geographical location information of grid picture A68 by (x2,
Y2), (x3, y3), (x5, y5) and (x6, y6) common programme, the geographical location information of grid picture A77 by (x4, y4), (x5,
Y5), (x7, y7) and (x8, y8) common programme, the geographical location information of grid picture A78 by (x5, y5), (x6, y6), (x8,
) and (x9, y9) common programme y8.It can be to setting specific locations of the A in samples pictures 10 by above-mentioned geographical location information
It is described.
Specifically, in the present embodiment, formed grid picture is handled in step S1 by the first granularity gridding
Resolution ratio be 0.1m × 0.1m~1.0 × 1.0m.Specifically, grid picture is carried out by step S1 at the first granularity gridding
The formed cutting granularity of reason is depending on the pixel quantity and resolution ratio of original samples pictures 10.Usually, in order to ensure
Original samples pictures 10 can be divided into 100 by the problem of carrying out judging exactly to dynamic object and considering computing cost
A grid picture, and the shape of each grid picture is not limited to square, for example, rectangular grid picture, positive six side
The grid picture of shape all may be used.Difference lies in step sl, geographical location information is filled to the grid picture of regular hexagon for it
When, it is necessary to mark the coordinate data of six coordinate points.
Join shown in Fig. 4, next perform step S2.There is a dynamic object B to samples pictures 20 in a certain frame in us
The process that Shi Jinhang judges is described in detail.
In Fig. 4, a river 30 and tree A are shown in samples pictures 20.In Figure 5, identical environment describes data
On the premise of, occur a ship 40 in river 30.So, for Fig. 5 for Fig. 4, ship 40 is exactly dynamic mesh B.In ginseng
Described in text, still need to carry out grid to samples pictures 20 in a manner that the first granularity gridding is handled to samples pictures 20 first
Change is handled, to form 100 grid pictures, i.e. grid picture A00~grid picture A99.
Next, picture 20a to be determined illustrated in fig. 6 is compared with samples pictures 20, to judge for sample
Gridding processing is carried out based on the first granularity gridding processing mode in this picture 20, and with being protected in the database in step S1
The grid picture that data are described by filling geographical location information and environment deposited carries out first time comparison, and variation is generated to filter out
Grid picture, carry out second comparison using grid pictures of these generation variations as height suspect objects.
In figure 6, the grid picture of above-mentioned generation variation includes grid picture A38 and grid picture A39；And in figure 6,
Grid picture A67, grid picture A68, grid picture A77 and grid picture A67 in picture 20a to be determined do not become
Change.So far tree A can be thoroughly determined as static object by computer.
In the present embodiment, the calculation formula that first time comparison is carried out to samples pictures 20 and picture 20a to be determined is as follows
Shown in formula (1)：
Δi=| Xi-X*|>ηi, i=1,2 ..., m (1)；
Wherein, ηiFor empirical parameter, ηiValue range for 0%~30%, X* be preserved in database by filling ground
Reason location information and environment describe the grid picture of data, XiFor picture to be determined, m is grid selected when comparing for the first time
The quantity of picture, m take the positive integer more than or equal to 1.
Then, the environment in the grid picture with same geographic location information and samples pictures 20 is described data to carry out
Second of comparison, to judge in the grid picture region (i.e. grid picture A38 and grid picture A39) for generating variation
Target whether be dynamic object, shown in the calculation formula such as following formula (2) of second comparison：
Δj=| Yj-Y*|>ηj, j=1,2 ..., n (2)；
Wherein, ηjFor empirical parameter, ηjValue range be 0%~30%, Y*It is to be preserved in database by filling ground
Reason location information and environment describe the grid picture of data, YjTo be to be determined and generate the grid picture of variation, n is right for second
Than when selected grid picture quantity, n takes the positive integer more than or equal to 1.
In the revealed method of the present embodiment, it can find to generate the grid picture place of variation by second of comparison
Whether the target in region is dynamic object, to judge there be depositing for dynamic object really in the grid picture of above-mentioned generation variation
.Dynamic object is not limited to ship, pedestrian, animal.
Meanwhile in order to reduce the computing cost compared to dynamic object and improve the accuracy of detection to dynamic object.
Step S2 in a kind of revealed complex environment of the present embodiment in dynamic object determination method is further included to screening
Generate the grid picture of variation, i.e. grid picture A38 and grid picture A39 are with smaller gridding granularity progress at least once two
Grade gridding processing.
In actual calculating process, the region that the ship 40 (i.e. dynamic object B) appeared in river 30 is covered is excessive.
Therefore, with reference to shown in Fig. 7 and Fig. 8, it is preferable that in the present embodiment, step S2 is further included：Change to filtering out several generate
Grid picture (i.e. grid picture A38 and grid picture A39) to handle smaller gridding compared with the first granularity gridding
Granularity carries out two level gridding at least once and handles, and (amounts to 16 sons to form several sub-grid A3800~sub-grid A3833
Grid) and sub-grid A3900~sub-grid A3933 (amounting to 16 sub-grids).
It should be noted that in the present embodiment, so-called smaller gridding granularity refers to compared with first time gridding
Resolution ratio during processing；Also, smaller gridding granularity in the present embodiment, can be used to hold the grid picture for generating variation
Row is once or more gridding is handled；Further, gridding operation can both be cut with transversally cutting or longitudinal direction
Point or illustrated in fig. 7 gridding operation is carried out in transverse direction and longitudinal direction.
Above-mentioned all sub-grids, which include, generates all geographical location information and environment that the grid picture of variation is included
Data are described.Specifically, since sub-grid A3800~sub-grid A3833 belongs to grid picture A38, sub-grid A3900~son
Grid A3933 belongs to grid picture A39.Therefore, formed sub-grid is handled above by least some two level griddings
The index search of foundation to corresponding geographical location information and environment can be passed through in the database of GIS-Geographic Information System
Data are described, and above-mentioned geographical location information and environment are described into data to handle the multiple sons formed by two level gridding
Grid carries out assignment, so as to judge whether the grid picture region for generating variation provides for dynamic object for the later stage
Reference data and foundation.
Preferably, in the present embodiment, step S2 is further included：The minimum formed to the grid for generating data variation is outer
Rectangle 70 is connect, according to the order that area coverage in adjacent four quadrants is descending, successively to the grid corresponding to four quadrants
The processing of two level gridding at least once is carried out to handle smaller gridding granularity compared with the first granularity gridding, if to be formed
Dry sub-grid, and the geographical location information of the affiliated superior grid of the sub-grid is associated in the database according to the index of foundation
Data Matching is carried out, finally to determine whether the target image that minimum enclosed rectangle 70 is covered is dynamic object.
Specifically, can be in grid picture A38 and the joint 200 of grid picture A39 boundarys as origin, it will be outside minimum
It connects rectangle 70 and is divided into the Ith quadrant, the IIth quadrant, the IIIth quadrant and the IVth quadrant.So that finally to whether being dynamic mesh
Target determination range is further limited in sub-grid A3910, sub-grid A3911, sub-grid A3833, sub-grid A3920, subnet
In lattice A3921 and sub-grid A3833, so as to significantly reduce the computing cost of scanning comparison.
Those listed above is a series of to be described in detail only for feasibility embodiment of the invention specifically
Bright, they are not to limit the scope of the invention, all equivalent implementations made without departing from skill spirit of the present invention
Or change should all be included in the protection scope of the present invention.
It is obvious to a person skilled in the art that the invention is not restricted to the details of above-mentioned exemplary embodiment, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Profit requirement rather than above description limit, it is intended that all by what is fallen within the meaning and scope of the equivalent requirements of the claims
Variation is included within the present invention.Any reference numeral in claim should not be considered as to the involved claim of limitation.
Moreover, it will be appreciated that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
Using specification as an entirety, the technical solutions in each embodiment can also be properly combined, forms those skilled in the art
It is appreciated that other embodiment.
Claims (6)
1. the determination method of dynamic object in a kind of complex environment, which is characterized in that comprise the following steps：
Step S1, several samples pictures are defined, and samples pictures are carried out with the first granularity gridding processing to be formed comprising several
The grid picture of gridding, and preserved after describing data to grid picture filling geographical location information and environment to geography information system
The database of system and foundation index association；
Step S2, by picture to be determined be based on the first granularity gridding processing mode carry out gridding processing, and in step S1
Database in preserved data are described by filling geographical location information and environment grid picture carry out first time comparison, with
Several grid pictures for generating variation are filtered out, shown in the calculation formula such as following formula (1) of the first time comparison：
Δi=| Xi-X*|>ηi, i=1,2 ..., m (1)；
Wherein, ηiFor empirical parameter, ηiValue range be 0%~30%, X*It is to be preserved in database by the geographical position of filling
Confidence breath and environment describe the grid picture of data, XiFor picture to be determined, m is grid picture selected when comparing for the first time
Quantity, m takes the positive integer more than or equal to 1；
Then, the environment in the grid picture with same geographic location information and samples pictures is described data to carry out second
Comparison, to judge whether the target in the grid picture region for generating variation is dynamic object, described second right
Shown in the calculation formula of ratio such as following formula (2)：
Δj=| Yj-Y*|>ηj, j=1,2 ..., n (2)；
Wherein, ηjFor empirical parameter, ηjValue range be 0%~30%, Y*It is to be preserved in database by the geographical position of filling
Confidence breath and environment describe the grid picture of data, YjTo be to be determined and generate the grid picture of variation, when n compares for second
The quantity of selected grid picture, n take the positive integer more than or equal to 1.
2. according to the method described in claim 1, it is characterized in that, in the step S1 samples pictures selection set from Image Acquisition
For to the Various Seasonal under Same Scene and/or the picture acquired in daytime change condition.
3. according to the method described in claim 1, it is characterized in that, the environment describes data describes data, daytime by season
Delta data, dynamic object describe data, non-dynamic goal description data, elevation data, dynamic object probability of occurrence data or
One or two kinds of data above composition in the non-dynamic target probability of occurrence data of person, and for each grid picture mark only
One environment describes data and geographical location information.
4. according to the method described in claim 1, it is characterized in that, institute is handled by the first granularity gridding in the step S1
The resolution ratio of the grid picture of formation is 0.1m × 0.1m~1.0 × 1.0m.
5. according to the method described in claim 1, it is characterized in that, the step S2 is further included：Become to filtering out several generate
The grid picture of change carries out two level gridding at least once to handle smaller gridding granularity compared with the first granularity gridding
Processing, to form several sub-grids；The sub-grid, which includes, generates all geographical locations letter that the grid picture of variation is included
Breath and environment describe data.
6. according to the method described in claim 5, it is characterized in that, the step S2 is further included：To generating the net of data variation
The minimum enclosed rectangle that lattice are formed, according to the order that area coverage in adjacent four quadrants is descending, successively to four as
The corresponding grid of limit carries out two level grid at least once to handle smaller gridding granularity compared with the first granularity gridding
Change is handled, to form several sub-grids, and by the geographical location information of the affiliated superior grid of sub-grid root in the database
Data Matching is carried out according to the index association of foundation, finally to determine whether the target image that minimum enclosed rectangle is covered is dynamic
Target.
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