CN105513043B - The coal mine underground operators method of counting of view-based access control model - Google Patents

The coal mine underground operators method of counting of view-based access control model Download PDF

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CN105513043B
CN105513043B CN201510801099.XA CN201510801099A CN105513043B CN 105513043 B CN105513043 B CN 105513043B CN 201510801099 A CN201510801099 A CN 201510801099A CN 105513043 B CN105513043 B CN 105513043B
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oval
image
coal mine
random process
represent
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CN105513043A (en
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伍云霞
张宏
于晨晨
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China University of Mining and Technology Beijing CUMTB
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China University of Mining and Technology Beijing CUMTB
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30196Human being; Person

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Abstract

The invention discloses a kind of coal mine underground operators method of counting of view-based access control model, one piece of elliptic region in personnel targets image in image characterizes, picture material is expressed with stochastic model, oval appearance is then a random process in image, and the random process is described i.e. with Gibbs densityThe oval configuration for making Gibbs density maximum is found, that is, solves optimization problemStatisticsIn oval number, as total number of persons's mesh.This method can count automatically to coal mine operation number, be cooperated with one's own initiative without personnel, realize noiseless monitoring in real time.

Description

The coal mine underground operators method of counting of view-based access control model
Technical field
The present invention relates to coal mine underground operators method of counting, more particularly to a kind of coal mine operation of view-based access control model Personnel's method of counting.
Background technology
In order to prevent colliery spy, major accident occurs, and colliery forbids overdetermination person to produce, and monitors coal mine operation people in real time Number is one of effective measures for preventing overdetermination person from producing.At present, coal mine operation number is to carry to identify by personnel in the pit The more card phenomenons of people one of identification card system None- identified, there is skip often come what is counted in card, thus is unable to accurate counting coal mine Lower operating personnel.
Need a kind of coal mine operation people for solving or at least improving one or more problems intrinsic in the prior art Member's method of counting.
The content of the invention
It is an object of the invention to provide a kind of coal mine underground operators method of counting of view-based access control model, this method can be with Coal mine operation number is counted automatically, cooperated with one's own initiative without personnel, realizes noiseless monitoring.
According to a kind of embodiment form, there is provided a kind of coal mine underground operators method of counting of view-based access control model, its feature It is:One piece of elliptic region in personnel targets image in image characterizes, and defines elliptic space χ=[0, X in imageM] × [0, YM]×[am, aM]×[bm, bM] × [0, π], wherein, XMAnd YMThe width and height of image, (a are represented respectivelym, aM) and (bm, bM) minimum value and maximum of expression transverse and short axle, θ ∈ [0, π] represent oval direction respectively;With random mould Type expresses picture material, and oval appearance is then a random process in image, and the random process is described i.e. with Gibbs densityWherein, U (o) represents the potential energy of oval random process, o={ o1=(x1, m1) ..., on=(xn, mn) ∈ χ represent a kind of configuration of oval random process,For normaliztion constant;Searching makes Gibbs The maximum oval configuration of density, that is, solve optimization problemStatisticsIn oval number, as total number of persons Mesh.
Brief description of the drawings
By following explanation, accompanying drawing embodiment becomes aobvious and seen, it is only preferred with least one being described in conjunction with the accompanying But the way of example of non-limiting example provides.
Fig. 1 is the method for the invention flow chart.
Fig. 2 is that the method for the invention personnel targets characterize schematic diagram.
Fig. 3 is the method for the invention personnel targets candidate region and background schematic diagram.
Embodiment
Fig. 1 is the flow chart of the inventive method, the flow of compares figure 1, is described.
Coal mine underground operators must safe wearing cap, safety cap is in subcircular, and usually yellow, with gray background Contrast is obvious.When underground coal mine is installed and catches operation area scene camera, because underground coal mine is tunnel, camera optical axis Generally there is the angle for being less than 90 more than 0 with ground, therefore, the safety cap that operating personnel is worn is in the picture then near oval Shape, 2 points based on more than, the personnel targets in image are characterized with one piece of elliptic region in image.Personnel in the pit is dispersed in one Determine operation in scope, they are different with the distance of video camera and visual angle, therefore safety cap position in the picture and form Also it is different, therefore, space χ=K × M=[0, X belonging to definition ellipse in the pictureM] × [0, YM]×[am, aM]×[bm, bM]× [0, π], K represent oval locational space, and M represents oval attribute space, XMAnd YMThe width and height of image, (a are represented respectivelym, aM) and (bm, bM) minimum value and maximum of expression transverse and short axle, θ ∈ [0, π] represent oval direction, elliptical modes respectively Type is as shown in Figure 2.Picture material is expressed with stochastic model, oval appearance is then a random process in image, close with Gibbs Degree describes this random processU (o) represents the potential energy of oval random process, o={ o1=(x1, m1) ..., on=(xn, mn) ∈ χ represent a kind of configuration of oval random process,For normalization Constant;The oval configuration for making Gibbs density maximum is found, that is, solves optimization problemStatisticsIn ellipse Number, as total number of persons's mesh.
Potential-energy function U (o)=Up(o)+Ud(o), Up(o) priori energy, U are representedd(o) data capacity is represented.
Mutually blocked sometimes in view of underground work area personnel, then show as ellipse in the picture and have overlapping, Up(o) Punished, defined according to each oval Maximum overlap with neighbouring ellipseA(oi, oj) ∈ [0,1] expression overlap coefficients,C is normal number, and μ () is ellipse area, μ (oi∩oj) represent oval oiWith ojOverlapping area, γpThe weight to overlapping relation punishment is represented, its size is between ellipse The size of overlapping area and change, overlapping area is big, then assigns greater weight, and corresponding priori energy is also bigger.
Data item Ud(o) confidence level that candidate's elliptic region in image is personnel is expressed, safety cap is in the picture It is yellow ellipse, and background is grey, with both color characteristic difference sizes come to characterize candidate's elliptic region be personnel Confidence level, i.e.,
oiRepresent candidate's elliptic region, F (oi) represent candidate's area elliptica The neighborhood in domain, as shown in Figure 3.dB(oi, F (oi)) represent candidate's elliptic region distribution of color and the Pasteur of its neighborhood distribution of color Distance:
Wherein, pj() and qj() represents candidate's elliptic region respectively, and each bin is corresponding with its neighborhood color histogram Probability distribution, L represents color histogram bin number, normalized to [- 1,1] scope,
Wherein, d0Oval personnel targets region and the average of Pasteur's distance of its neighborhood distribution of color are represented, D is a yardstick Parameter, od(dB) ∈ [- 1,1] have evaluated the similarity of candidate's elliptic region and its neighborhood distribution of color, for less Pasteur away from From od(dB) it is on the occasion of expression candidate's elliptic region is that the confidence level of personnel is big, and negative value represents that candidate's elliptic region is personnel Confidence level is low.
Solved using the simulated annealing (SA) based on birth and death processOptimization problem, algorithm include Following steps:
E1. temperature parameter is initializedTime discretization step-length δ=δ0
E2. raw step:For each pixel s ∈ I, if existed without target, increase by one at s with probability δ B (s) Individual target, wherein,Z is To a parameter;
E3. sequence step:Calculate configuration target oiData item ud(oi), the order successively decreased according to data capacity sorts;
E4. the step of going out:To each target o according to this orderiCalculate the death rateWherein aβ(oi)=exp (- β U (oi)), then target is with probability d (oi) be destroyed;
E5. convergence test:If all targets are destroyed in step E4 just added by step E2, convergence, algorithm are realized Terminate, otherwise, reduce temperature T (n+1)=kT (n) and discretization step-length δ (n+1)=δ (n)-Δ δ, be then return to step E2, its Middle n represents cooling number, and k < 1 are constant, and Δ δ is constant.

Claims (1)

  1. A kind of 1. method of counting of the coal mine underground operators of view-based access control model, it is characterised in that:Underground coal mine is tunnel, shooting Machine optical axis generally has the angle for being less than 90 ° more than 0 ° with ground, and the safety cap that operating personnel is worn is in the picture then near ellipse Circle, one piece of elliptic region characterizes in the personnel targets image in image, defines elliptic space χ=[0, X in imageM]× [0, YM]×[am, aM]×[bm, bM] × [0, π], wherein, XMAnd YMThe width and height of image, a are represented respectivelym, aMTable respectively Show the minimum value and maximum of transverse, bm, bMThe minimum value and maximum of ellipse short shaft are represented respectively, and θ ∈ [0, π] are represented Oval direction;Picture material is expressed with stochastic model, oval appearance is then a random process in image, close with Gibbs Degree describes the random processWherein, U (o) represents the potential energy of oval random process, o={ o1 =(x1, m1) ..., oi=(xi, mi) ..., on=(xn, mn)) ∈ χ represent a kind of configuration of oval random process,For normaliztion constant;The oval configuration for making Gibbs density maximum is found, that is, solves optimization problemStatisticsIn oval number, as total number of persons's mesh;Potential-energy function U (o)=Up(o)+Ud(o), Up(o) Represent priori energy, Ud(o) data capacity is represented;Up(o) punished according to each oval with close oval Maximum overlap, DefinitionA(oi, oj) ∈ [0,1] expression overlap coefficients,C For normal number, μ () is ellipse area, μ (oi∩oj) represent oiAnd ojOverlapping area, γpRepresent to overlapping relation punishment Weight, its size change with the size of overlapping area between ellipse, and overlapping area is big, then assigns larger weight, accordingly Priori energy is also bigger.
CN201510801099.XA 2015-11-20 2015-11-20 The coal mine underground operators method of counting of view-based access control model Expired - Fee Related CN105513043B (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103218598A (en) * 2013-03-26 2013-07-24 中国科学院电子学研究所 Method for automatically detecting remote sensing ground object target based on stochastic geometry model
CN104732552A (en) * 2015-04-09 2015-06-24 西安电子科技大学 SAR image segmentation method based on nonstationary condition field

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1916624B1 (en) * 2006-10-25 2016-11-23 Agfa HealthCare NV Method for segmenting a digital medical image.

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103218598A (en) * 2013-03-26 2013-07-24 中国科学院电子学研究所 Method for automatically detecting remote sensing ground object target based on stochastic geometry model
CN104732552A (en) * 2015-04-09 2015-06-24 西安电子科技大学 SAR image segmentation method based on nonstationary condition field

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
一种基于双椭圆模型的视频人数统计方法;张继法 等;《计算机科学》;20120630;第39卷(第6A期);第499-500页 *
一种新的基于吉布斯随机场的视频运动对象分割算法;刘龙 等;《自动化学报》;20070630;第33卷(第6期);第609、611页 *
非参数吉布斯模型和多波段遥感影像纹理分割方法研究;龚衍 等;《武汉大学学报信息科学版》;20070731;第32卷(第7期);第581-584页 *

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