CN111683221B - Real-time video monitoring method and system for natural resources embedded with vector red line data - Google Patents

Real-time video monitoring method and system for natural resources embedded with vector red line data Download PDF

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CN111683221B
CN111683221B CN202010438052.2A CN202010438052A CN111683221B CN 111683221 B CN111683221 B CN 111683221B CN 202010438052 A CN202010438052 A CN 202010438052A CN 111683221 B CN111683221 B CN 111683221B
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feature points
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邵振峰
李从敏
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Wuhan University WHU
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/48—Matching video sequences

Abstract

A natural resource real-time video monitoring method and system with embedded vector red line data comprises the steps of obtaining the vector red line data of natural resources and a corresponding high-resolution remote sensing image, and obtaining an initial picture image of a camera; superposing the vector red line data to the high-resolution remote sensing image, identifying homonymous points on the video image and the vector red line data by means of semantic information of the remote sensing image, and calculating a geometric mapping relation; mapping the vector red line data to a real-time video to obtain a video monitoring area; matching adjacent frame images of the monitored video to obtain homonymous points by obtaining characteristic points uniformly distributed in scale and space; obtaining a geometric transformation relation between the images according to the same name points, and mapping a monitoring area range on the previous frame of image to the next frame of image to realize the association of the monitoring areas between the image frames; according to the geometric relation, area measurement is carried out on the monitoring video, and the subsequent relevant target motion analysis is carried out based on the determined video area, so that automatic area early warning and alarming are realized.

Description

Real-time video monitoring method and system for natural resources embedded with vector red line data
Technical Field
The invention belongs to the technical field of image processing, and relates to a natural resource real-time video monitoring method and system with embedded vector red line data.
Background
Ecological red lines, permanent basic farmlands and urban boundaries are three bottom lines which need strict defense in ecological environment construction. At present, the phenomena of damaged fertile farmlands, disorder and excessive cutting of forest resources, non-construction of urban development areas and the like are frequent, the problem that national natural resources are abused is increasingly prominent, and how to support accurate, real-time and quick efficient monitoring of the natural resources through technical means is an important and urgent matter.
With the rapid development of sensor technology, video compression processing technology and network communication technology, remote video monitoring of these natural resources has become possible by using modern technological means. If the technology suitable for practical application exists, relevant users can find illegal behaviors in the field of the homeland resources in time by looking up the monitoring camera, and corresponding places are treated immediately. However, in the existing mode, a large amount of manual troubleshooting real-time monitoring systems are still required, and automatic monitoring of natural resources in a designated area cannot be achieved. How to monitor the designated area range in the video field by using the image processing technology to realize the automatic early warning and alarm of the area is the fundamental way to really realize the video monitoring of natural resources. Therefore, the invention provides a natural resource real-time video monitoring method and system with embedded vector red line data.
The common monitoring camera can only monitor a fixed position, has limited visual field and is not suitable for monitoring natural resources in the type. The PTZ (Pan-Tilt-Zoom, security monitoring equipment) camera with the Pan-Tilt control function and capable of adjusting the camera to rotate, translate and stretch is more suitable for the scene. The definition of the natural resource distribution area is basically stored in the form of a vector file, and how to embed GIS graphic data in the form of vectors into a real-time PTZ monitoring video image is a problem worthy of research. Based on the area, the area can be analyzed, such as area measurement, and the behavior of people can be detected and prejudged, so that automatic early warning and alarming of illegal activities can be realized. Here the face mainly comprises two parts: (1) mapping the vector data into a video picture; (2) when the camera is rotated, the vector data needs to be remapped, so that the relative position of the vector data in different video pictures is kept unchanged.
Vector data is a graph and visually displays geometric structure information, attribute information of the vector data is stored in a text form, and the geometric relation between a vector structure and a monitoring video image is difficult to directly establish, so that the establishment of the mapping relation between the vector structure and the monitoring video image needs to be assisted by a high-resolution remote sensing image. At present, in the research of the fusion of the GIS data and the surveillance video, two methods can be roughly classified: homography matrix based methods and camera view intersection with DEM based methods. The former is to solve the geometric transformation relation between the monitoring video and the remote sensing image through homonymous feature points, generally described by a homography matrix, and is suitable for a plane or an approximate plane area. The latter is to blend the monitoring video into the 3DGIS, and map the video image into the 3DGIS based on the real world coordinate system, so as to realize the unification of the two. The method needs to acquire the position and attitude information of the camera during imaging and a high-precision DSM (digital Surface model), and is suitable for images of small scenes. For a common monitoring camera, the position parameters and the attitude parameters during photographing can be acquired, but for a PTZ camera, the acquired parameter information is not accurate when the camera is controlled by a holder. In addition, large scale fine DSMs are expensive to acquire.
A video may be understood as a collection of a series of images. When the camera rotates to obtain the monitoring picture, the camera can acquire images from different visual angles, and certain geometric deformation exists among the images due to the problem of the acquisition angle. At present, a method based on feature matching is widely applied to the calculation of geometric relationships between images, wherein the SIFT algorithm is widely concerned due to the image invariance kept in the aspects of rotation and scale, but when the geometric distortion is large, a satisfactory result is difficult to obtain. On the basis, ASIFT (affinity-SIFT) is provided, and because a series of geometric deformation simulation is carried out on an original image before SIFT matching, the ASIFT can better solve the matching problem of geometric distortion between images, and a certain number of homonymous points can be obtained even if the viewing angle is greatly different. However, these feature-based matching methods do not take into account the distribution problem of the feature points when obtaining the matching points. The number and distribution of the homonymous points are two determining factors affecting the accuracy of the geometric transformation matrix.
How to conveniently and accurately acquire the area of a specific area in the visual field of the camera is also an urgent problem to be solved. At present, the methods for measuring the area from the video mainly include: the method comprises the steps of measuring on the spot, calculating based on an estimation method of a calibration template and calculating according to a simple proportional relation among image pixel coordinates, a camera focal length and vertical height, has certain limitation, and is not suitable for PTZ monitoring videos with a holder function.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a natural resource real-time video monitoring method and system with embedded vector red line data, which can greatly improve the monitoring efficiency of natural resources.
In order to achieve the above object, the technical solution of the present invention provides a real-time video monitoring method for natural resources embedded with vector red line data, comprising the following steps:
step 1, acquiring natural resource vector red line data and a corresponding high-resolution remote sensing image of a certain area with the same projection coordinate system, and simultaneously acquiring an initial picture image of a PTZ camera;
step 2, superposing the vector red line data to the high-resolution remote sensing image, identifying homonymy points on the video image and the vector red line data respectively by means of semantic information of the remote sensing image, and then calculating a geometric mapping relation between the vector red line data and the monitoring video by adopting a least square method;
step 3, mapping the vector red line data to a real-time video according to the mapping relation calculated in the step 2 to obtain a video monitoring area;
step 4, matching adjacent frame images of the monitoring video by obtaining feature points uniformly distributed in scale and space, so as to obtain homonymous points on the two images;
step 5, obtaining a geometric transformation relation between the two images according to the homonymy points obtained in the step 4, and mapping the monitoring area range on the previous frame of image to the next frame of image according to the relation to realize the association of the monitoring area between the monitoring video image frames;
and 6, according to the geometric relationship established in the step, carrying out area measurement on the monitoring video, and supporting the subsequent relevant target motion analysis based on the determined video area, thereby realizing the automatic early warning and alarming of the specific area.
In step 2, the geometric mapping relation between the monitoring video image and the remote sensing image is modeled into projection transformation, and a homography matrix H is used0The method comprises the steps that at least 4 non-collinear same-name points are identified on two images, and the mutual mapping relation between a monitoring video image and a remote sensing image is quickly established;
corresponding homonymous points q (X, Y) on the remote sensing image are set to be corresponding to the monitoring video image I0At any point p (x, y) above, the equation is satisfied as follows,
Figure BDA0002503034100000031
wherein the homography matrix H0The expression of (a) is as follows,
Figure BDA0002503034100000032
in step 3, plane vector data { P } is provided1,P2,...,PnFor any one of the faces PiThe coordinates are expressed as { (X)i1,Yi1),(Xi2,Yi2),...,(Xim,Yim)}，
For arbitrary vertex (X)ij,Yij) I 1,2, n, j 1,2, m is the number of planes and m is the number of points, and the coordinates in the surveillance video are (x) according to the geometric relationship established in step 2ij',yij'), calculated as follows,
Figure BDA0002503034100000033
in step 4, the feature points uniformly distributed in scale and space are obtained by determining the number of feature points extracted from each layer of pyramid image according to the scale coefficient in the aspect of scale; in the aspect of images, performing uniform grid division on the images, and determining the quantity of feature points extracted from each grid; then, feature points are screened according to the uniqueness and density of the feature points.
And, in terms of scale, the number of the feature points extracted from the pyramid image of each layer is determined according to the scale coefficient, which is implemented as follows,
suppose that the total number of feature points extracted from an image is NtotalAnd the weight of each layer of image in each group of pyramid images is W (o, l), so that the number N of feature points extracted from each layer of image is NscaleThe expression of (o, l) is,
Nscale(o,l)＝Ntotal×W(o,l)
wherein, o is the mark number of the Gaussian pyramid, and l is the mark number of the image layer;
the weight of each scale image is defined as follows,
Figure BDA0002503034100000041
wherein, SColThe scale coefficient, SC, of the image of the l layer in the o group of Gaussian pyramids11Scale coefficient, r, representing the level 1 image in the set 1 Gaussian pyramid0Is an intermediate parameter; SC (Single chip computer)olThe expression is as follows:
Figure BDA0002503034100000042
wherein σ0As an initial scale parameter, NoNumber of pyramid images generated, NsThe number of layers of each group of pyramids, K is a constant,
Figure BDA0002503034100000043
o＝1,2,3,...,No，l＝1,2,3,...,Ns。
in the aspect of the image, the image is uniformly meshed, the number of the feature points extracted in each mesh is determined, and the method is realized as follows,
the quantity of the feature points extracted from each layer of image of the pyramid is assumed to be Nscale(o, l) uniformly dividing the layer image into n2Number of feature points to be extracted in each grid, kth grid, N _ gridkThe way of calculating (a) is as follows,
N_gridk＝Nscale(o,l)×fk
wherein f iskAs grid weight coefficients, from the entropy E of the information in the grid areakThe number N of the characteristic pointskAnd the contrast mean C of the feature pointskDetermining that the expression is:
Figure BDA0002503034100000051
wherein, we、wnAnd wcAre respectively information entropy EkThe number N of the characteristic pointskA special featureMean contrast C of feature pointskWeight coefficient of (d), we+wn+wc＝1。
In step 4, the selected N _ grid is screened out in each grid by adopting the following strategykThe number of the characteristic points is one,
step a, calculating the contrast of each feature point, sorting the feature points from high to low according to the contrast value, and then selecting the top 3 XN _ gridkThe characteristic points are used as pre-selected characteristic points;
b, accurately determining the positions and the scales of the feature points by adopting an SIFT method, and removing edge response points in the feature points obtained in the step a;
step C, calculating the information entropy of the feature points in the neighborhood with the feature points as the center, and ordering the feature points from large to small according to the information entropy to obtain an initial feature point set Cint；
Step d, sequentially taking point sets CintRespectively calculating Euclidean distance between the point and the rest characteristic points, if the minimum distance is still larger than the corresponding threshold value, keeping the point to a target point set CsetFrom (1) to (C)setNumber of middle feature points and N _ gridkUntil equal.
Furthermore, in step 6, the area measurement result is obtained for the polygon in the Mth frame image of the current monitoring video in the following manner,
firstly, determining the geometric mapping relation between the current video frame image M and GIS data, and for any point P on the video frame image MM(xM,yM) The homography matrix with the previous frame image is HM-1Inversion of homography HM-1 -1Then corresponds to the coordinate P on the M-1 frame imageM-1(xM-1,yM-1)＝HM-1 -1PM(xM,yM) And so on, calculating the coordinate P in the projection coordinate systemM(XM,YM)＝H0 -1H1 -1...HM-1 -1P(xM,yM)；
Then, according to the relation, the polygon coordinates on the video frame image are subjected to inverse calculation to obtain the coordinates corresponding to the projection coordinate system;
and finally, calculating the area of a polygon by using the cross product of the vectors to obtain an area measurement result.
The invention also provides a natural resource real-time video monitoring system embedded with the vector red line data, which is used for realizing the natural resource real-time video monitoring method embedded with the vector red line data.
According to the method, the natural resource vector data are embedded into the real-time monitoring video, the geometric mapping relation between the 2D GIS data and the PTZ monitoring video is established, the area measurement can be directly carried out on the monitoring video, and on the basis of the area, a foundation can be provided for automatic early warning and alarming of a specific area in the subsequent video field, so that the monitoring efficiency of natural resources is greatly improved.
Compared with the prior art, the technical scheme provided by the invention has the beneficial effects that:
(1) the invention provides a geographic vector data and real-time video fusion scheme based on semi-automatic mapping;
(2) the invention provides a multi-view image matching method based on an improved ASIFT matching mode;
(3) the invention provides a PTZ monitoring video area extraction method based on 2DGIS data.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
Referring to fig. 1, a method and a system for real-time video monitoring of natural resources embedded with vector red line data provided by the embodiment of the present invention include the following steps:
(1) acquiring natural resource vector red line data and a corresponding high-resolution remote sensing image of a certain area with the same projection coordinate system,obtaining initial picture image I of PTZ camera at the same time0；
When the method is specifically implemented, the vector red line data and the remote sensing image are ensured to belong to the same projection coordinate system, and a basis is provided for semantic auxiliary recognition of homonymous points and area calculation of the subsequent remote sensing image. In specific implementation, coordinate conversion can be performed to obtain vector red line data of uniform coordinates.
(2) Superposing the vector red line data to the high-resolution remote sensing image, identifying homonymous points on the video image and the vector red line data respectively by means of semantic information of the remote sensing image, and then calculating a geometric mapping relation between the vector red line data and the monitoring video by adopting a least square method;
the vector red line data is a graphic structure, which shows geometric information, and the attribute information is hidden, so that the geometric corresponding relation between the vector red line data and the monitoring video cannot be directly determined, and therefore, the semantic information of the remote sensing image is needed to assist in identifying the homonymous points of the monitoring video and the vector red line data. When the geometric mapping relation between the two is calculated, a monitoring video image (PTZ camera initial picture image I) can be obtained0) Modeling the geometric mapping relation between the image and the remote sensing image as projection transformation by using a homography matrix H0To indicate. By identifying at least 4 non-collinear homonymous points on the two images, the mutual mapping relation between the monitoring video image and the remote sensing image can be quickly established. Corresponding homonymous points q (X, Y) on the remote sensing image and the monitoring video image I0Any point p (x, y) above, satisfies the equation:
Figure BDA0002503034100000071
wherein the homography matrix H0The expression of (a) is:
Figure BDA0002503034100000072
wherein, a0、b0Etc. are elements in the matrix.
In specific calculation, a least square method is adopted to calculate the geometric mapping relation between the two.
(3) According to the mapping relation estimated in the step (2), vector red line data can be mapped to a real-time video to obtain a video monitoring area;
suppose a surveillance video image I0Followed by an image frame I1…InCorresponding to the faceted vector data { P1,P2,...,PnFor any one of the faces PiThe coordinates of which are expressed as { (X)i1,Yi1),(Xi2,Yi2),...,(Xim,Yim) Where i is 1, 2.., n,
for arbitrary vertex (X)ij,Yij) Where i is 1,2,., n, j is 1,2,.., m, n is the number of planes, and m is the number of points. According to the geometric relationship established in the step 2, the coordinate in the monitoring video is (x)ij',yij') it is calculated as:
Figure BDA0002503034100000073
(4) matching adjacent frame images of the monitoring video by adopting an improved ASIFT algorithm so as to obtain homonymous points on the two images;
the invention provides a method for matching features of adjacent frame images of a video by adopting an improved ASFIT method, and a feature selection method considering scale and image space distribution is adopted during feature extraction to obtain feature points uniformly distributed in the scale and the space. The main idea of uniformly extracting the feature points in the aspect of scale is as follows: determining the number of the feature points extracted from each layer of pyramid image according to the scale coefficient; the main idea of uniformly extracting the feature points on the aspect of the image is as follows: carrying out uniform grid division on the image, and determining the number of feature points extracted from each grid; then, feature points are screened according to the uniqueness and density of the feature points.
ASIFT is short for affinity-SIFT, is an improvement on the SIFT method, and is also a classical method. The invention improves the ASIFT method, mainly screens the characteristic points on the scale and space distribution in the selection of the characteristic points.
In terms of scale, the number of feature points is determined as follows.
Suppose that the total number of feature points extracted from an image is NtotalAnd the weight of each layer of image in each group of pyramid images is W (o, l), so that the number N of feature points extracted from each layer of image is NscaleThe expression of (o, l) is:
Nscale(o,l)＝Ntotal×W(o,l) (4)
wherein, o is the mark number of the Gaussian pyramid, and l is the mark number of the image layer.
The weights for each scale image are defined as follows:
Figure BDA0002503034100000081
wherein, SColThe scale coefficient, SC, of the image of the l layer in the o group of Gaussian pyramids11Scale coefficient, r, representing the level 1 image in the set 1 Gaussian pyramid0Is an intermediate parameter; SC (Single chip computer)olThe expression is as follows:
Figure BDA0002503034100000082
wherein σ0As an initial scale parameter, NoNumber of pyramid images generated, NsThe number of layers of each group of pyramids, K is a constant,
Figure BDA0002503034100000083
o＝1,2,3,...,No，l＝1,2,3,...,Ns。
in terms of image spatial distribution, the pyramid image is first divided into uniform grids, and then the number of feature points in each grid on each layer of pyramid image is determined in the following manner.
The quantity of the feature points extracted from each layer of image of the pyramid is assumed to be Nscale(o, l) mapping the layerImage uniform division into n2Grid, then, the number of feature points N _ grid to be extracted in the k-th gridkThe calculation method of (c) is as follows:
N_gridk＝Nscale(o,l)×fk (7)
wherein f iskFor the grid weight coefficients, in particular by the entropy E of the information in the grid areakThe number N of the characteristic pointskAnd the contrast mean C of the feature pointskDetermining that the expression is as follows:
Figure BDA0002503034100000084
wherein, we、wnAnd wcAre respectively information entropy EkThe number N of the characteristic pointskContrast mean value C of feature pointskThe three satisfy the following relationship:
we+wn+wc＝1 (9)
the invention provides that the following strategy is adopted in each grid to screen out and select N _ gridkAnd (4) a characteristic point.
Step 1): calculating the contrast of each feature point, sorting the feature points from high to low according to the contrast value, and then selecting the top 3 XN _ gridkThe characteristic points are used as pre-selected characteristic points;
step 2): accurately determining the positions and the scales of the feature points by adopting a method in SIFT, and removing edge response points in the feature points obtained in the step 1); wherein SIFT refers to scale invariant feature transform;
step 3): calculating the information entropy of the feature points in a certain neighborhood taking the feature points as centers, and sequencing the feature points from large to small according to the information entropy to obtain an initial feature point set Cint；
Step 4): sequentially taking point set CintRespectively calculating Euclidean distances between the point and the rest characteristic points, and if the minimum distance is still larger than a threshold value d, keeping the point to a target point set CsetFrom (1) to (C)setNumber of middle feature points and N _ gridkUntil equal. In specific implementation, the value of the threshold d may be set according to specific image content and the number of feature points.
(5) Estimating the geometric transformation relation between the two images according to the homonymous points obtained in the step (4), and mapping the monitoring area range on the previous frame of image to the next frame of image according to the relation to realize the association of the monitoring area between the monitoring video image frames;
iterative solution of geometric relationship between adjacent frame images by using RANSAC method to obtain homography matrix H of mapping ith frame image to (i + 1) th frame imagei1,2, ·, n; the value of n is obtained according to the length of the video frame.
(6) According to the geometric relationship established in the steps, area measurement can be directly carried out on the monitoring video, and the subsequent related target analysis can be supported based on the determined video area, so that automatic early warning and alarming of a specific area are realized.
For polygons on the mth frame image of the current surveillance video, the area can be calculated as follows.
Firstly, determining the geometric mapping relation between the current video frame image M and GIS data, and for any point P on the video frame image MM(xM,yM) The homography matrix with the previous frame image is HM-1Inversion of homography HM-1 -1Then it corresponds to the coordinate P on the M-1 frame imageM-1(xM-1,yM-1)＝HM-1 -1PM(xM,yM) By analogy, the coordinate P of the projection coordinate system can be calculatedM(XM,YM)＝H0 -1H1 -1...HM-1 -1P(xM,yM)；
Then, according to the relation, the polygon coordinates on the video frame image are subjected to inverse calculation to obtain the coordinates of the polygon coordinates in a projection coordinate system;
finally, the cross product of the vectors is used to calculate the area of a polygon.
According to the method, continuous videos of the monitoring videos are targetedFrame, accordingly video surveillance area A is available0、A1…An. In specific implementation, based on the determined video region, the related target analysis can be implemented as required, which is not described in detail in the present invention,
In specific implementation, the invention can adopt a computer software technology to realize an automatic operation process. System means for carrying out the method of the invention should also be within the scope of the invention.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (9)

1. A real-time video monitoring method for natural resources embedded with vector red line data is characterized by comprising the following steps:
step 1, acquiring natural resource vector red line data and a corresponding high-resolution remote sensing image of a certain area with the same projection coordinate system, and simultaneously acquiring an initial picture image of a PTZ camera;
step 2, superposing the vector red line data to the high-resolution remote sensing image, identifying homonymy points on the video image and the vector red line data respectively by means of semantic information of the remote sensing image, and then calculating a geometric mapping relation between the vector red line data and the monitoring video by adopting a least square method;
step 3, mapping the vector red line data to a real-time video according to the mapping relation calculated in the step 2 to obtain a video monitoring area;
step 4, matching adjacent frame images of the monitoring video by obtaining feature points uniformly distributed in scale and space, so as to obtain homonymous points on the two images;
step 5, obtaining a geometric transformation relation between the two images according to the homonymy points obtained in the step 4, and mapping the monitoring area range on the previous frame of image to the next frame of image according to the relation to realize the association of the monitoring area between the monitoring video image frames;
and 6, according to the geometric relationship established in the step, carrying out area measurement on the monitoring video, and supporting the subsequent relevant target motion analysis based on the determined video area, thereby realizing the automatic early warning and alarming of the specific area.
2. The real-time video monitoring method for natural resources embedded with vector red line data according to claim 1, characterized in that: in step 2, modeling the geometric mapping relation between the monitoring video image and the remote sensing image into projection transformation, and using a homography matrix H0The method comprises the steps that at least 4 non-collinear same-name points are identified on two images, and the mutual mapping relation between a monitoring video image and a remote sensing image is quickly established;
corresponding homonymous points q (X, Y) on the remote sensing image are set to be corresponding to the monitoring video image I0At any point p (x, y) above, the equation is satisfied as follows,
Figure FDA0003116470450000011
wherein the homography matrix H0The expression of (a) is as follows,
Figure FDA0003116470450000012
3. the real-time video monitoring method for natural resources embedded with vector red line data according to claim 1, characterized in that: in step 3, plane vector data { P is set1,P2,...,PnFor any one of the faces PiThe coordinates are expressed as { (X)i1,Yi1),(Xi2,Yi2),...,(Xim,Yim)}，
For arbitrary vertex (X)ij,Yij) I 1,2, n, j 1,2, m is the number of planes and m is the number of points, and the coordinates in the surveillance video are (x) according to the geometric relationship established in step 2ij',yij'), calculated as follows,
Figure FDA0003116470450000021
4. the real-time video monitoring method for natural resources embedded with vector red line data according to claim 1, characterized in that: in the step 4, the feature points uniformly distributed in the scale and space are obtained by determining the number of the feature points extracted from each layer of pyramid image according to the scale coefficient in the aspect of scale; in the aspect of images, performing uniform grid division on the images, and determining the quantity of feature points extracted from each grid; then, feature points are screened according to the uniqueness and density of the feature points.
5. The method of claim 4, wherein the real-time video monitoring method for natural resources embedded with vector red line data comprises: in the aspect of scale, the number of the feature points extracted from the pyramid image of each layer is determined according to the scale coefficient, the implementation mode is as follows,
suppose that the total number of feature points extracted from an image is NtotalAnd the weight of each layer of image in each group of pyramid images is W (o, l), so that the number N of feature points extracted from each layer of image is NscaleThe expression of (o, l) is,
Nscale(o,l)＝Ntotal×W(o,l)
wherein, o is the mark number of the Gaussian pyramid, and l is the mark number of the image layer;
the weight of each scale image is defined as follows,
Figure FDA0003116470450000022
wherein, SColThe scale coefficient, SC, of the image of the l layer in the o group of Gaussian pyramids11Scale coefficient, r, representing the level 1 image in the set 1 Gaussian pyramid0Is an intermediate parameter; SC (Single chip computer)olThe expression is as follows:
Figure FDA0003116470450000023
wherein σ0As an initial scale parameter, NoNumber of pyramid images generated, NsThe number of layers of each group of pyramids, K is a constant,
Figure FDA0003116470450000031
o＝1,2,3,...,No，l＝1,2,3,...,Ns。
6. the method according to claim 5, wherein the real-time video monitoring method for natural resources embedded with vector red line data comprises: in the aspect of the image, the image is uniformly meshed, the number of the characteristic points extracted from each mesh is determined, the realization mode is as follows,
the quantity of the feature points extracted from each layer of image of the pyramid is assumed to be Nscale(o, l) uniformly dividing the layer image into n2Number of feature points to be extracted in each grid, kth grid, N _ gridkThe way of calculating (a) is as follows,
N_gridk＝Nscale(o,l)×fk
wherein f iskAs grid weight coefficients, from the entropy E of the information in the grid areakThe number N of the characteristic pointskAnd the contrast mean C of the feature pointskDetermining that the expression is:
Figure FDA0003116470450000032
wherein, we、wnAnd wcAre respectively information entropy EkThe number N of the characteristic pointskContrast mean value C of feature pointskWeight coefficient of (d), we+wn+wc＝1。
7. The method of claim 6, wherein the real-time video monitoring method for natural resources embedded with vector red line data comprises: in step 4, the selected N _ grid is screened out in each grid by adopting the following strategykThe number of the characteristic points is one,
step a, calculating the contrast of each feature point, sorting the feature points from high to low according to the contrast value, and then selecting the top 3 XN _ gridkThe characteristic points are used as pre-selected characteristic points;
b, accurately determining the positions and the scales of the feature points by adopting an SIFT method, and removing edge response points in the feature points obtained in the step a;
step C, calculating the information entropy of the feature points in the neighborhood with the feature points as the center, and ordering the feature points from large to small according to the information entropy to obtain an initial feature point set Cint；
Step d, sequentially taking point sets CintRespectively calculating Euclidean distance between the point and the rest characteristic points, if the minimum distance is still larger than the corresponding threshold value, keeping the point to a target point set CsetFrom (1) to (C)setNumber of middle feature points and N _ gridkUntil equal.
8. A real-time video monitoring method for natural resources embedded with vector red line data according to claim 1,2, 3, 4, 5, 6 or 7, characterized in that: in step 6, for the polygon on the Mth frame image of the current monitoring video, the area measurement result is obtained by adopting the following method,
firstly, determining the geometric mapping relation between the current video frame image M and GIS data, and for any point P on the video frame image MM(xM,yM) The homography matrix with the previous frame image is HM-1Inversion of homography HM-1 -1Then pairCoordinate P to be on M-1 frame imageM-1(xM-1,yM-1)＝HM-1 -1PM(xM,yM) And so on, calculating the coordinate P in the projection coordinate systemM(XM,YM)＝H0 -1H1 -1...HM-1 -1P(xM,yM)；
Then, according to the relation, the polygon coordinates on the video frame image are subjected to inverse calculation to obtain the coordinates corresponding to the projection coordinate system;
and finally, calculating the area of a polygon by using the cross product of the vectors to obtain an area measurement result.
9. A real-time video monitoring system of natural resources of embedding vector red line data which characterized in that: a real-time video monitoring method of natural resources for embedding vector red line data according to any one of claims 1 to 8.
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