Abstract:
A method for recognizing a slope condition is provided. The method includes obtaining an image of a slope and setting a region of interest thereof, calculating an initial slope model information of the region of interest and an optical flow information of the region of interest, first determining a first possibility of a slope failure based on the optical flow information, and when a degree of the first possibility is determined that the slope failure can occur, second determining a second possibility of the slope failure by a comparison between the initial slope model information and a slope information, wherein the slope information is obtained based on the optical flow information by scanning on a portion of the region of interest that the slope failure can occur

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    This application claims priority to and the benefit of Korean Patent Application No. 10-2014-0057706, filed on May 14, 2014, the disclosure of which is incorporated herein by reference in its entirety. 
       BACKGROUND 
       [0002]    The present invention relates to a method of recognizing a slope condition capable of monitoring a condition of a slope in real time, a system using the same, and a recording medium for performing the same. 
         [0003]    In general, a phenomenon in which a slope collapses is known as a landslide or a slope failure and includes falls, topples, slides, flows, spreads, and the like. Korea is a highly mountainous region. When roads, houses, and the like are constructed, slopes are cut out of mountains, hills, and the like. Slopes are also formed when dams, banks, and the like are built. 
         [0004]    As described above, when the slope occurs, determination of stability of the slope is very important. A possibility of a landslide (a slope failure) needs to be predicted and responses thereto need to be devised in advance. The causes of the landslide may include inner factors such as geological qualities, soil qualities, geological feature structures, and geological vulnerabilities, natural external factors such as rain, snow melting, groundwater, erosion of rivers and coasts, and earthquakes, and artificial external factors such as cutting the ground, landfills, and dam building. When a shearing stress increases or a shearing strength decreases due to the above-described factors and a safety factor (=shearing strength/shearing stress) becomes 1, the landslide occurs. In Korea, landslides mainly occur during a period of heavy rain in the summer and during periods of thawing, which causes massive damage to life and property. However, in the slope in general, a single slope has different geological properties according to a depth, a degree of weathering, a degree of degradation, a presence and a type of a geological feature structure, and the like. Therefore, it is very difficult to accurately predict stability of the slope. 
         [0005]    Therefore, recently, instruments capable of quantitatively determining behaviors of the slope have been used in order to evaluate stability of the slope. However, as current slope measuring methods, a system configured to detect and predict a displacement of a main occurrence place through an image sensor such as a general CCD camera is exemplified, but it is difficult to perform robust detection and prediction in real time when external environments are complex or ambient environments are changed. Also, it is difficult to perform prevention and prediction due to insufficient road safety systems in Korea. Therefore, the development of natural disaster warning systems is necessary. 
       SUMMARY 
       [0006]    The present invention provides a method of recognizing a slope condition in which a possibility of slope failure is primarily determined according to optical flow information, precise scan is performed on a partial region of a slope having a high possibility of failure, and a possibility of slope failure may be secondarily determined, a system using the same, and a recording medium for performing the same. 
         [0007]    According to an aspect of the present invention, there is provided a method of recognizing a slope condition. The method includes obtaining an image of a slope and setting a region of interest, calculating initial slope model information and optical flow information of the region of interest, performing primary determination of a possibility of slope failure according to the optical flow information, and performing secondary determination of a possibility of slope failure such that, when it is determined in the primary determination that there is a possibility of slope failure, slope information is obtained by performing precise scan on a region having a high possibility of slope failure among the region of interest according to the optical flow information, and the initial slope model information and the slope information are compared. 
         [0008]    The obtaining of an image of a slope and setting of a region of interest may include manually setting the region of interest by a user or automatically setting a preset region as the region of interest, and when the region of interest is set, a corresponding region may be divided into a plurality of blocks, and the initial slope model information and the optical flow information may be calculated. 
         [0009]    In the calculating of the initial slope model information, information on the slope may be calculated as 3D geometric information by reflecting each piece of information of a plurality of blocks within the region of interest. 
         [0010]    In the performing of the primary determination of a possibility of slope failure according to the optical flow information, an optical flow vector of a unit feature vector and a direction vector in a slope direction within the region of interest may be calculated, a first determination value may be calculated by an inner product of the optical flow vector and the direction vector in the slope direction, and when the first determination value is greater than a predetermined threshold value, it may be determined that there is a possibility of slope failure. 
         [0011]    In the secondary determination of a possibility of slope failure by comparing the initial slope model information with slope information obtained by performing the precise scan, the number of changed corner feature points may be calculated by comparing distance information obtained by performing partial scan on candidate blocks having a high possibility of slope failure within the region of interest with the initial slope model information, and the possibility of slope failure may be secondarily determined by a ratio of the number of blocks included in the initial slope model information and the number of blocks whose distance information is changed. 
         [0012]    According to another aspect of the present invention, there is provided a computer-readable recording medium. The medium may be a computer-readable recording medium recording a computer program for executing the method of recognizing a slope condition according to any of the above-described aspects. 
         [0013]    According to still another aspect of the present invention, there is provided a system for recognizing a slope condition. The system includes a camera configured to capture a slope and obtain an image, a laser instrument configured to measure distance information of the image and measure depth information of the slope, a region of interest setting unit configured to set a region of interest of the image, an initial model generating unit configured to obtain initial slope model information of the region of interest, an optical flow calculating unit configured to calculate optical flow information of the region of interest, a first determining unit configured to determine a possibility of failure of the slope using the optical flow information, and a second determining unit configured to, when it is primarily determined that there is a possibility of failure of the slope, perform precise scan on a partial region having a relatively large optical flow vector within the region of interest, and determine a possibility of failure of the slope by comparing slope information that is generated by the precise scan with the initial slope model information. 
         [0014]    The region of interest setting unit may divide the region of interest into a plurality of blocks, and the initial model generating unit may calculate information on the slope as 3D geometric information by reflecting distance information of the plurality of blocks. 
         [0015]    The first determining unit may calculate an optical flow vector of a unit feature vector and a direction vector in a slope direction within the region of interest, calculate a first determination value by an inner product of the optical flow vector and the direction vector in a slope direction, and determine that there is a possibility of slope failure when the first determination value is greater than a predetermined threshold value. 
         [0016]    The second determining unit may calculate the number of changed corner feature points by comparing distance information obtained by performing partial scan on a candidate region having a high possibility of slope failure within the region of interest with the initial slope model information, and secondarily determine a possibility of slope failure by a ratio of the number of blocks included in the initial slope model information and the number of blocks whose distance information is changed. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0017]    The above and other objects, features and advantages of the present invention will become more apparent to those of ordinary skill in the art by describing in detail exemplary embodiments thereof with reference to the accompanying drawings, in which: 
           [0018]      FIG. 1  is a diagram illustrating a system for recognizing a slope condition according to an embodiment of the present invention; 
           [0019]      FIG. 2  is a control block diagram of the system for recognizing a slope condition according to the embodiment of the present invention; 
           [0020]      FIGS. 3   a  and  3   b  show setting of a region of interest and obtained initial slope model information in the system for recognizing a slope condition according to the embodiment of the present invention; 
           [0021]      FIGS. 4   a  and  4   b  are diagrams illustrating the results of computation of an optical flow from a corner feature in the system for recognizing a slope condition according to the embodiment of the present invention; 
           [0022]      FIGS. 5   a  and  5   b  are diagrams illustrating detection of optical flow information using the Lucas-Kanade method in the system for recognizing a slope condition according to the embodiment of the present invention; 
           [0023]      FIGS. 6   a  and  6   b  are diagrams illustrating optical flow occurrence blocks when the system for recognizing a slope condition according to the embodiment of the present invention performs secondary determination and a result of a partial scan on a region thereof; 
           [0024]      FIG. 7  is a diagram illustrating a triangle string method used for outputting a stereoscopic image in the system for recognizing a slope condition according to the embodiment of the present invention; 
           [0025]      FIG. 8  is a diagram illustrating a configuration of rendering an image texture in the system for recognizing a slope condition according to the embodiment of the present invention; 
           [0026]      FIG. 9  is a diagram illustrating computation of a displacement volume of a place in which a displacement occurs in order to compute an exact amount of displacement in the system for recognizing a slope condition according to the embodiment of the present invention; 
           [0027]      FIGS. 10   a  and  10   b  are diagrams illustrating the result of a precise scan on a block in which an optical flow occurs and computation of an amount of displacement using the result; 
           [0028]      FIG. 11  is a diagram illustrating a method of calculating a volume of the block that has been precisely scanned due to occurrence of the optical flow; and 
           [0029]      FIGS. 12 and 13  are control flowcharts illustrating a method of recognizing a slope condition according to an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0030]    Detailed descriptions of the invention will be made with reference to the accompanying drawings illustrating specific embodiments of the invention as examples. These embodiments will be described in detail such that the invention can be performed by those skilled in the art. It should be understood that various embodiments of the invention are different but are not necessarily mutually exclusive. For example, a specific shape, structure, and characteristic of an embodiment described herein may be implemented in another embodiment without departing from the scope and spirit of the invention. In addition, it should be understood that a position or an arrangement of each component in each disclosed embodiment may be changed without departing from the scope and spirit of the invention. Accordingly, there is no intent to limit the invention to the detailed descriptions to be described below. The scope of the invention is defined by the appended claims and encompasses all equivalents that fall within the scope of the appended claims. Like numbers refer to the same or like functions throughout the description of the figures. 
         [0031]    Hereinafter, exemplary embodiments of the present invention will be described in greater detail with reference to the drawings. 
         [0032]      FIG. 1  is a diagram illustrating a system for recognizing a slope condition according to an embodiment of the present invention. 
         [0033]    A system for recognizing a slope condition  100  may predict a slope failure by recognizing a condition of a slope. The system for recognizing a slope condition  100  may set a region of interest of the slope, obtain 3D information by scanning a corresponding region, and extract initial slope model information. The region of interest may be arbitrarily set by a user. Information on a desired place may be obtained by designating a size and a location of the region of interest, and the number of scan points. 
         [0034]    The system for recognizing a slope condition  100  may obtain initial slope model information through image information input from a camera and distance information recognized by a laser instrument. 
         [0035]    The system for recognizing a slope condition  100  may compute optical flow information in order to perform primary determination of a possibility of slope failure. Several methods such as the known Lucas-Kanade method, Black-Jepson method, Horn-Schunck method, and the like may be used to compute the optical flow information. A method of computing the optical flow using the Lucas-Kanade method is described in detail in “Title of the paper: An Iterative Image Registration Technique with an Application to Stereo Vision, author: Bruce D. Lucas Takeo Kanade.” The Black-Jepson method is described in detail in “Title of the paper: Estimating Optical Flow in Segmented Images Using Variable-Order Parametric Models With Local Deformations, author: Michael J. Black, Member, IEEE, and Allan D. Jepson.” The Horn-Schunck method is described in detail in “Title of the paper: Determining Optical Flow, author: Berthold K. P Horn and Brian G. Schunck.” Hereinafter, computation of the optical flow using the Lucas-Kanade method will be exemplified. However, needless to say, the embodiment of the present invention is not limited to computation of the optical flow information using the Lucas-Kanade method. 
         [0036]    The system for recognizing a slope condition  100  may perform primary determination of a possibility of slope failure in consideration of a size and a direction of an optical flow vector included in the optical flow information. When it is primarily determined that there is a possibility of slope failure, the system for recognizing a slope condition  100  may perform a secondary determining operation by performing a precise scan on a block of an initial slope model having a large optical flow vector using the laser instrument. 
         [0037]    When it is measured in the primary determining operation that an amount of occurrence of an optical flow in a slope direction is equal to or greater than a threshold value in consecutive image frames, the system for recognizing a slope condition  100  may determine that the slope has a high possibility of failure. In order to determine a possibility of slope failure more accurately, the system for recognizing a slope condition  100  performs a precise scanning operation on a region having a high possibility of slope failure using the laser instrument, and may perform secondary determination of a possibility of failure within the region of interest by comparing the result with the initial slope model. 
         [0038]    The system for recognizing a slope condition  100  may realistically output a 3D stereoscopic image of a region that is determined to have a high possibility of slope failure through the above-described primary determining operation and secondary determining operation. In order to realistically represent a stereoscopic image, the system for recognizing a slope condition  100  may perform rendering a texture of a real image using each scan point as a starting point. 
         [0039]      FIG. 2  is a control block diagram of the system for recognizing a slope condition according to the embodiment of the present invention.  FIGS. 3   a  and  3   b  show setting of a region of interest and obtained initial slope model information in the system for recognizing a slope condition according to the embodiment of the present invention.  FIGS. 4   a  and  4   b  are diagrams illustrating the results of computation of an optical flow from a corner feature in the system for recognizing a slope condition according to the embodiment of the present invention.  FIGS. 5   a  and  5   b  are diagrams illustrating detection of optical flow information using the Lucas-Kanade method in the system for recognizing a slope condition according to the embodiment of the present invention.  FIGS. 6   a  and  6   b  are diagrams illustrating optical flow occurrence blocks when the system for recognizing a slope condition according to the embodiment of the present invention performs secondary determination and a result of a partial scan on a region thereof.  FIG. 7  is a diagram illustrating a triangle string method used for outputting a stereoscopic image in the system for recognizing a slope condition according to the embodiment of the present invention.  FIG. 8  is a diagram illustrating a configuration of rendering an image texture in the system for recognizing a slope condition according to the embodiment of the present invention.  FIG. 9  is a diagram illustrating computation of a displacement volume of a place in which a displacement occurs in order to compute an exact amount of displacement in the system for recognizing a slope condition according to the embodiment of the present invention.  FIGS. 10   a  and  10   b  are diagrams illustrating the result of a precise scan on a block in which an optical flow occurs and computation of an amount of displacement using the result. 
         [0040]    The system for recognizing a slope condition  100  may include a camera  20  configured to capture a slope and obtain an image, a laser instrument  40  configured to measure depth information of the slope, a slope failure determination unit  60  configured to determine a possibility of slope failure, and an image output unit  80  configured to output a stereoscopic image of a corresponding region when it is determined that there is a possibility of slope failure. 
         [0041]    The slope failure determination unit  60  may include a region of interest setting unit  61  configured to set a region of interest of the obtained image, an initial model generating unit  62  configured to obtain initial slope model information of a corresponding region when the region of interest is set, an optical flow calculating unit  63  configured to calculate an optical flow of the region of interest, a first determining unit  64  configured to determine a possibility of slope failure using the calculated optical flow, and a second determining unit  65  configured to determine a possibility of slope failure by performing precise scan on a corresponding region when it is primarily determined that there is a possibility of slope failure. 
         [0042]    The camera  20  may capture a slope according to the user&#39;s manipulation and obtain an image. Among the image captured by the camera  20 , a part of the region may be set as a region of interest. The region of interest is a region that is set to check whether there is a possibility of slope failure, and may be arbitrarily set by the user. 
         [0043]    The laser instrument  40  may scan the region that is set as the region of interest among the region captured by the camera  20  and obtain 3D geometric information of the slope. The initial slope model may be formed through distance information of a scan point obtained by precisely scanning a slope whose conditions are to be recognized. The initial slope model may be used as a determination reference when secondary determination of a possibility of slope failure to be described is performed. 
         [0044]    The region of interest setting unit  61  may set the region of interest in the image captured by the camera  20 . The region of interest may be passively set by the user or a predetermined region set in advance may be automatically set as the region of interest. 
         [0045]    When the region of interest is set in the image, the region of interest setting unit  61  may divide a corresponding region into N blocks 
         [0046]    When distance information of the slope of the region of interest is measured by the laser instrument  40 , the initial model generating unit  62  may calculate the initial slope model using corresponding information. The initial slope model may be derived as 3D geometric information by reflecting each piece of distance information of a plurality of blocks within the region of interest. As illustrated in  FIG. 3 , the region of interest is set in the image of the captured slope, and the initial slope model may be generated by performing a laser scan on the set region of interest. 
         [0047]    The optical flow calculating unit  63  may calculate optical flow information of the region of interest. As described above, the optical flow information may be computed using several methods such as the known Lucas-Kanade method, Black-Jepson method, Horn-Schunck method, and the like. Here, a method of computing the optical flow information using the Lucas-Kanade method will be exemplified. 
         [0048]    In the Lucas-Kanade method, the optical flow may be computed using a corner feature of which a value is found in perpendicular directions by performing spatial differentiation in X axis and Y axis directions of the image.  FIG. 4  shows the results of optical flow vectors calculated using the Lucas-Kanade algorithm in a slope landslide image. 
         [0049]    The optical flow vector may be computed from brightness improvement, time persistence, and space consistency. In general, a value of a pixel in a specific object is not significantly changed when an image frame is changed. That is, it is assumed that brightness of a corner pixel to be tracked in an input gray image in order to compute an optical flow of corner feature points is not changed. This assumption is valid since an amount of change of an object between consecutive image frames is not large when a time is more rapidly changed than a movement of the object in the image. Also, points adjacent to each other in a space are highly likely to be included in the same object and may have the same movement. 
         [0050]    According to the above-described assumption, the same two points of the same object in consecutive image frames may be represented as Equation 1. When the right hand side of Equation 1 is expanded as Taylor series, Equation 2 may be calculated. In order to simultaneously satisfy Equation 1 and Equation 2, a sum of the differential equation of Equation 2 should be 0. By dividing the differential equation by dt, an optical flow constraint equation, Equation 3, may be calculated. 
         [0000]    
       
         
           
             
               
                 
                   
                       
                   
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         [0051]    When a spatial differential value can be computed in an X axis direction and a Y axis direction of the image from Equation 3, it is possible to predict the optical flow that is a movement vector of the object in image coordinates. 
         [0052]    In the Lucas-Kanade algorithm, a window Ω having a predetermined size is set based on corner feature points of a t-th image frame and then a location of an image that is the most similar to the set window is found in a (t+1)-th image frame. 
         [0000]        I   x ( q   1 ) V   x   +I   y ( q   1 ) V   y   =−I   t ( q   1 ) 
         [0000]        I   x ( q   2 ) V   x   +I   y ( q   2 ) V   y   =−I   t ( q   2 ) 
         [0000]      . . . 
         [0000]        I   x ( q   m ) V   x   +I   y ( q   m ) V   y   =−I   t ( q   m )  Equation 4
 
         [0053]    Here, q 1 , q 2 , and q m  are pixels included in the window Ω. That is, q 1 , q 2 , . . . , q m εΩ. 
         [0054]    An optical flow constraint equation in the window Ω set in the t-th image frame may be represented as Equation 4, and may be represented as Equation 5 when it is expressed in the form of a determinant Ax=b. 
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         [0055]    When an expression of an optical flow vector V is summarized from Equation 5, Equation 6 may be calculated. When a least mean square is applied to (A T A) of Equation 6, Equation 7 may be obtained. A movement vector of the set window in the t-th image frame may be calculated from Equation 7. 
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                                
                               
                                 
                                   I 
                                   x 
                                 
                                  
                                 
                                   ( 
                                   
                                     q 
                                     i 
                                   
                                   ) 
                                 
                               
                                
                               
                                 
                                   I 
                                   t 
                                 
                                  
                                 
                                   ( 
                                   
                                     q 
                                     i 
                                   
                                   ) 
                                 
                               
                             
                           
                         
                         
                           
                             
                               
                                 - 
                                 
                                   Σ 
                                   
                                     
                                       q 
                                       i 
                                     
                                     ∈ 
                                     Ω 
                                   
                                 
                               
                                
                               
                                 
                                   I 
                                   y 
                                 
                                  
                                 
                                   ( 
                                   
                                     q 
                                     i 
                                   
                                   ) 
                                 
                               
                                
                               
                                 
                                   I 
                                   t 
                                 
                                  
                                 
                                   ( 
                                   
                                     q 
                                     i 
                                   
                                   ) 
                                 
                               
                             
                           
                         
                       
                       ] 
                     
                   
                 
               
               
                 
                   Equation 
                    
                   
                       
                   
                    
                   7 
                 
               
             
           
         
       
     
         [0056]    In the Lucas-Kanade algorithm according to the embodiment of the present invention, an image pyramid is formed from an original image, tracking is performed from an upper layer to a lower layer, and a feature point having a large movement may be found in a short time. 
         [0057]    In order for the laser instrument to efficiently scan, a unit feature vector X r  formed of corner feature points X rk (X k ,Y k ) within N blocks of the initial slope model may be represented as Equation 8. 
         [0000]        X   r   =[X   r1   X   r2    . . . X   rN ] t   Equation 8
 
         [0000]        V   r   =[V   r1   V   r2    . . . V   rN ] t   Equation 9
 
         [0058]    An optical flow of each corner feature forming a unit feature vector is predicted using the Lucas-Kanade algorithm, and an optical flow vector V, of the unit feature vector may be represented as Equation 9. 
         [0059]      FIG. 5  shows the results obtained by applying the Lucas-Kanade algorithm to unit feature vectors of the region of interest.  FIG. 5  shows that a displacement with a large or small change can be detected robustly. 
         [0060]    The first determining unit  64  refers to the initial slope model formed of N blocks, and may calculate a vector V g  formed of unit vectors in a slope gradient direction in each corner feature vector as Equation 10. 
         [0000]        V   g   =[V   g1   V   g2    . . . V   gN ] t   Equation 10
 
         [0061]    Here, V gk (V x ,V y ) denotes a unit vector in a slope gradient direction in corner feature points X rk  of a k-th block. 
         [0000]    That is, |V gk (V x ,V y )|=(V x   2 +V x   2 ) 0.5 =1 is established. 
         [0062]    The first determining unit  64  may perform primary determination for efficient scanning of the laser instrument by calculating an optical flow vector (V r ) of a unit feature vector (X k ) and a direction vector (V g ) in a slope direction. 
         [0063]    The first determining unit  64  may primarily determine whether there is a slope failure in consideration of a size and a direction of the optical flow vector. The first determining unit  64  calculates a first determination value that is obtained by an inner product of the optical flow vector (V r ) and the slope direction vector (V g ). A primary discriminant is the same as in Equation 11. 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             D 
                             1 
                           
                            
                           
                             ( 
                             
                               
                                 V 
                                 r 
                               
                               , 
                               
                                 V 
                                 g 
                               
                             
                             ) 
                           
                         
                         = 
                         
                           
                             V 
                             r 
                           
                           · 
                           
                             V 
                             g 
                           
                         
                       
                     
                   
                   
                     
                       
                         = 
                         
                           
                             ∑ 
                             
                               k 
                               = 
                               0 
                             
                             N 
                           
                            
                           
                               
                           
                            
                           
                             ( 
                             
                               
                                 V 
                                 rk 
                               
                               · 
                               
                                 V 
                                 gk 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                   
                     
                       
                         = 
                         
                           
                             ∑ 
                             
                               k 
                               = 
                               0 
                             
                             N 
                           
                            
                           
                             ( 
                             
                               
                                  
                                 
                                   V 
                                   rk 
                                 
                                  
                               
                                
                               cos 
                                
                               
                                   
                               
                                
                               
                                 θ 
                                 r 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   Equation 
                    
                   
                       
                   
                    
                   11 
                 
               
             
           
         
       
     
         [0064]    Here, θ r  denotes an angle formed by an optical flow vector (V r ) in a corner feature vector (X rk ) and a direction vector (V gk ) in a slope direction. When the first determination value is greater than a predefined threshold value T 1 , it is determined that there is a possibility of slope failure. 
         [0065]    When it is primarily determined through the above operation that there is a possibility of failure of the slope including the region of interest, the second determining unit  65  performs precise scan on a block of the initial slope model having a large optical flow vector using the laser instrument  40  with reference to the unit feature vector and the optical flow vector. 
         [0066]    When the first determination value is greater than the threshold value, the second determining unit  65  performs a precise scanning operation on a block having a high possibility of slope failure by controlling the laser instrument  40  in order to secondarily determine a possibility of slope failure, and may perform secondary determination based on 3D information obtained by the precise scanning operation. 
         [0067]    In the secondary determination, precise scan on blocks including corner features having high occurrence of the optical flow is performed, the result is compared with the initial slope model, and it is possible to verify whether there is a slope failure using a displacement in the region of interest 
         [0068]      FIG. 6  shows a block in which the optical flow occurs in the region of interest and a direction of the optical flow. As illustrated in  FIG. 6 , the block in which the optical flow occurs is shown in green, and may be shown in a darker color when more optical flow occurs. Also, since partial scan is performed on only the corresponding block in which the optical flow occurs, it is possible to decrease a scan time of the laser instrument  40  and quickly determine an emergency situation such as a slope failure. 
         [0069]    The second determining unit  65  compares distance information obtained by partial scan on candidate blocks having a high possibility of slope failure by the first determining unit  64  with the initial slope model, and computes the number of changed corner feature points N c . As shown in Equation 12, secondary determination may be determined as a ratio of the number of blocks in the region of interest (N) of the initial slope model and the number of blocks whose distance information is changed (N c ). 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       D 
                       2 
                     
                      
                     
                       ( 
                       
                         N 
                         , 
                         
                           N 
                           c 
                         
                       
                       ) 
                     
                   
                   = 
                   
                     
                       N 
                       c 
                     
                     N 
                   
                 
               
               
                 
                   Equation 
                    
                   
                       
                   
                    
                   12 
                 
               
             
           
         
       
     
         [0070]    When a second determination value is greater than a predefined threshold value T 2 , the second determining unit  65  may determine that a landslide or an emergency situation has occurred. 
         [0071]    When it is determined through the above operation that there is a high possibility of slope failure, the image output unit  80  may perform a rendering operation to generate a texture of a real image using each scan point of the region of interest as a starting point in order to realistically express a 3D stereoscopic image. 
         [0072]    The image output unit  80  may represent each scan point first on a stereoscopic coordinate system using a triangle string method for rendering. As illustrated in  FIG. 7 , since the triangle string method forms a stereoscopic plane with a plurality of triangles, it is inefficient to draw each triangle separately. In the triangle string method, a triangle is drawn using three vertices, and then objects may be expressed by adding vertices. 
         [0073]    The image output unit  80  may perform rendering of the initial slope model and depth information obtained by partial scan using an image texture.  FIG. 8  illustrates an example. 
         [0074]    When all condition recognitions are completed, the image output unit  80  may compute a displacement volume of a place in which a displacement occurs in order to compute an exact amount of displacement. The displacement volume may be measured by precisely scanning a block in which the optical flow occurs in the primary determination. A measurement value of the laser instrument may be obtained as distance and angle information of a spherical coordinate system. 
         [0075]    The image output unit  80  may register measurement information to a corresponding point of the image using correction technology of the camera  20 . The image output unit  80  computes an amount of displacement based on real measurement information. The image output unit  80  may convert each piece of angle and distance information into x, y, and z of a 3D Cartesian coordinate system in  FIG. 9  with respect to the laser instrument  40 . 
         [0076]    The image output unit  80  may measure a volume through a difference between the initial slope model and the precise scan result based on an XY plane of the Cartesian coordinate system.  FIG. 10A  shows the result of precise scan on a block in which the optical flow occurs.  FIG. 10B  shows computation of an amount of displacement according to the initial scan result and the precise scan result. A real block may be divided into a square pillar and a prismatoid as illustrated in  FIG. 11 . A volume of the block that has been precisely scanned due to occurrence of the optical flow is a sum of volumes of the prismatoid and the prismatoid as illustrated in  FIG. 11 . 
         [0077]      FIG. 12  is a control flowchart illustrating a system for recognizing a slope condition according to an embodiment of the present invention. 
         [0078]    The camera  20  captures and obtains an image of a slope. Among the slope obtained by the camera  20 , a part of the region may be set as a region of interest according to the user&#39;s manipulation or a predetermined rule ( 200  and  210 ). 
         [0079]    When distance information of the slope of the region of interest is measured by the laser instrument  40 , the initial model generating unit  62  may calculate the initial slope model using corresponding information. The initial slope model may be derived as 3D geometric information by reflecting each piece of distance information of a plurality of blocks within the region of interest ( 220 ). 
         [0080]    The optical flow calculating unit  63  may calculate optical flow information of the region of interest. The optical flow information may be calculated by the known plurality of methods ( 230 ). 
         [0081]    The first determining unit  64  may perform primary determination of a possibility of slope failure according to the optical flow information calculated by the optical flow calculating unit  63 . The first determining unit  64  may perform primary determination for efficient scanning of the laser instrument by calculating an optical flow vector (V_r) of a unit feature vector (X_k) and a direction vector (V_g) in a slope direction. The first determining unit  64  calculates a first determination value that is obtained by an inner product of the optical flow vector (V_r) and the slope direction vector (V_g). When the first determination value is greater than a predefined threshold value T 1 , it is determined that there is a possibility of slope failure ( 240 ,  250 , and  260 ). 
         [0082]      FIG. 13  is a control flowchart illustrating a method of recognizing a slope condition according to an embodiment of the present invention. 
         [0083]    When the first determination value is greater than the threshold value, the second determining unit  65  performs a precise scanning operation on a block having a high possibility of slope failure by controlling the laser instrument  40  in order to secondarily determine a possibility of slope failure, and may perform secondary determination based on 3D information obtained by the precise scanning operation ( 300  and  310 ). 
         [0084]    The second determining unit  65  performs precise scan on blocks including corner features having high occurrence of the optical flow, compares the result with the initial slope model, and may verify whether there is a slope failure using a displacement in the region of interest ( 320 ). 
         [0085]    The second determining unit  65  compares distance information obtained by partial scan on candidate blocks having a high possibility of slope failure by the first determining unit  64  with the initial slope model, and computes the number of changed corner feature points N c . Secondary determination may be determined as a ratio of the number of blocks in the region of interest (N) of the initial slope model and the number of blocks whose distance information is changed (N c ), and the ratio serves as a second determination value ( 330  and  340 ). 
         [0086]    The second determining unit  65  compares the second determination value with a size of the predetermined threshold value, and secondarily determines that there is a high possibility of slope failure when the second determination value is greater than the predetermined threshold value ( 350  and  360 ). 
         [0087]    When it is determined through the above operation that there is a high possibility of slope failure, the image output unit  80  may perform a rendering operation to generate a texture of a real image using each scan point of the region of interest as a starting point in order to realistically express a 3D stereoscopic image. The image output unit  80  performs the above operation, outputs a 3D screen, and enables the user to easily observe a place having a high possibility of slope failure in a stereoscopic manner ( 370 ). 
         [0088]    In this manner, technology for determining a possibility of slope failure by capturing a slope may be implemented in an application or a form of program instructions that may be executed through various computer components, and may be recorded in computer readable recording media. The computer readable recording media may include a program instruction, a data file, a data structure, and the like, or combinations thereof. 
         [0089]    The program instruction recorded in the computer readable recording media may be specially designed and prepared for the invention or may be an available well-known instruction for those skilled in the field of computer software. 
         [0090]    Examples of the computer readable recording media include, for example, magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a CD-ROM and a DVD, magneto-optical media such as a floptical disk, and a hardware device, such as a ROM, a RAM, and a flash memory, that is specially configured to store and perform the program instruction. 
         [0091]    Examples of the program instruction may include a machine code generated by a compiler and a high-level language code that can be executed in a computer using an interpreter. Such a hardware device may be configured as at least one software module in order to perform operations of the invention and vice versa. 
         [0092]    According to the embodiment of the present invention, since a possibility of slope failure is primarily determined according to optical flow information and a possibility of slope failure is secondarily determined by performing partial scan on only a region having a high displacement among the region of interest, it is possible to determine a possibility of slope failure more rapidly and accurately. 
         [0093]    While the present invention has been described above with reference to the embodiments, it may be understood by those skilled in the art that various modifications and alternations may be may be made without departing from the spirit and scope of the present invention described in the appended claims. 
         [0094]    While the present invention have been described above with reference to the embodiments, it may be understood by those skilled in the art that various modifications and alternations may be may be made without departing from the spirit and scope of the present invention described in the appended claims.