Abstract:
The purpose of the present invention is to provide an object detecting device which is capable of accurately detecting an object even far away, and of shortening processing time. Provided is an object detecting device ( 100 ), comprising: a disparity acquisition unit ( 116 ) which compares each image of two cameras ( 112, 113 ) and computes a disparity for each pixel; a near-far boundary setting unit ( 118 ) which, in a single image of one of the two cameras, sets a boundary (Rb) between a near region (R 1 ) which is close to a vehicle ( 110 ) and a far region (R 2 ) which is distant from the vehicle ( 110 ); a near object detecting unit ( 119 ) which detects objects ( 102,   104 ) of the near region (R 1 ) on the basis of the disparity; and a far object detecting unit ( 120 ) which detects objects ( 103,   104 ) of the far region (R 2 ) on the basis of the single image.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to an object detecting device that detects an object on the basis of an image. 
       BACKGROUND ART 
       [0002]    A driving support device for a vehicle using various sensors is developed worldwide. The driving support device can provide a function of automatic emergency braking (AEB) of applying automatic braking in an emergency or a function of adaptive cruise control (ACC) of automatically adjusting a speed according to a preceding vehicle. To provide the function of AEB or ACC, it is necessary to detect the preceding vehicle or an object such as an obstacle existing in front of an own vehicle. 
         [0003]    As an object detecting device used for detecting the object, a device that operates three-dimensional coordinate position data in a three-dimensional coordinate system for each pixel of a distance image, on the basis of two-dimensional coordinate position data for each pixel of the distance image and distance data from a reference position for each pixel, and generates a three-dimensional distribution of pixels corresponding to a surface and a detection target object is known (for example, refer to PTL 1 described below), 
         [0004]    In addition, a solid object detecting device that generates a grid map in which a three-dimensional distance data point group measured by a laser radar is accumulated, and determines a road surface and a solid object, is known (for example, refer to PTL 2 described below). The device divides three-dimensional distance information into solid object information to be three-dimensional information showing the solid object and plane information to be three-dimensional information showing a plane and extracts a grounding pointposition where the solid object contacts the plane, on the basis of the solid object information and plane information corresponding to a predetermined region around the solid object to which attention is paid. In addition, the device determines a search range of the solid object in an image, on the basis of the distance information and the grounding point position. 
       CITATION LIST 
     Patent Literature 
       [0005]    PTL 1: Japanese Unexamined Patent Publication No. 10-143659 
         [0006]    PTL 2: Japanese Unexamined Patent Publication No. 2013-140515 
       SUMMARY OF INVENTION 
     Technical Problem 
       [0007]    In the object detecting device, it is required to detect an object accurately and quickly from a near region to a fax-region to realize quick and reliable driving support. In addition, it is required to maximally shorten processing time necessary for detection to quickly execute an operation for detecting the object and executing control. 
         [0008]    However, in the object detecting device described in PTL 1, the surface is detected on the basis of the distance image and a pixel group of a predetermined height or more with the surface as a reference among pixel groups corresponding to the surface is detected as the detection target object. For this reason, erroneous detection or non-detection for a far object with small pixels may occur frequently. 
         [0009]    In addition, in the solid object detecting device described in PTL 2, erroneous detection of the solid object existing on the road surface can be reduced. However, it is necessary to divide the distance data into the solid object and the road surface in advance to detect the solid object existing on the road surface. The division of the distance data becomes difficult in a far region where the distance data decreases. 
         [0010]    The present invention has been made in view of the above problems and an object of the present invention is to provide an object detecting device that is capable of accurately detecting an object even far away and of shortening processing time. 
       Solution to Problem 
       [0011]    In order to solve the above issue, an object detecting device according to the present invention is an object detecting device for detecting objects in front of a vehicle, on the basis of images of two cameras, including: a disparity acquisition unit which compares individual images of the two cameras and calculates a disparity for each pixel; a near-far boundary setting unit which sets a boundary between a near region close to the vehicle and a far region distant from the vehicle, in a single image of one of the two cameras; a near object detecting unit which detects an object of the near region, on the basis of the disparity; and a far object detecting unit which detects an object of the far region, on the basis of the single image. 
       Advantageous Effects of Invention 
       [0012]    According to an object detecting device according to the present invention, a far object detecting unit detects an object of a far region on the basis of a single image of one of two cameras, so that the far object detecting unit can accurately detect an object even far away. In addition, a near object detecting unit detects an object of a near region on the basis of a disparity of the two cameras, so that the near object detecting unit can accurately detect a near object. In addition, a data processing amount can be decreased and processing time can be shortened. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0013]      FIG. 1  is a system outline diagram illustrating an object detecting device according to an embodiment of the present invention, 
           [0014]      FIG. 2  is a block diagram illustrating a schematic configuration of a road surface estimation unit illustrated in  FIG. 1 . 
           [0015]      FIGS. 3( a ) and 3( b )  are image views illustrating processing by the road surface estimation unit. 
           [0016]      FIG. 4( a )  is an image view illustrating processing by the road surface estimation unit and  FIG. 4( b )  illustrates a V-Disparity image. 
           [0017]      FIG. 5( a )  is an image view illustrating processing by the road surface estimation unit and  FIG. 5( b )  illustrates a Y-Disparity image. 
           [0018]      FIG. 6( a )  is an image view illustrating processing by a far object detecting unit and  FIG. 6( b )  illustrates a V-Disparity image. 
           [0019]      FIGS. 7( a ) and 7( b )  are image views illustrating processing by a near-far object integrating unit. 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0020]    An embodiment of an object detecting device according to the present invention is hereinafter described with reference to the drawings. 
         [0021]      FIG. 1  is a system outline diagram illustrating a schematic configuration of an object detecting device according to this embodiment and a peripheral portion thereof. 
         [0022]    An object detecting device  100  according to this embodiment is a device that detects objects in front of a vehicle  110 , on the basis of images of two cameras, and is configured using a stereo camera device  111  mounted on the vehicle  110 , for example. The stereo camera device  111  is a device that photographs a front region of the vehicle  110 , detects a road surface  101  in front of the vehicle  110  by image processing, and detects a near preceding vehicle  102  and a far preceding vehicle  103  of the vehicle  110  and an obstacle other than the vehicles, for example, a guardrail  104 . The vehicle  110  includes the stereo camera device  111  and a travel control unit  122 . 
         [0023]    The stereo camera device  111  includes two cameras of a left camera  112  and a right camera  113 , a left image acquisition unit  114  and a right image acquisition unit  115 , a disparity image acquisition unit (disparity acquisition unit)  116 , a road surface height estimation unit (road surface estimation unit)  117 , a near-far boundary distance setting unit (near-far boundary setting unit)  118 , a near object detecting unit  119  and a far object detecting unit  120 , and a near-far object integrating unit  121 . 
         [0024]    The left camera  112  and the right camera  113  are a pair of imaging units that are disposed at positions separated from each other in a vehicle width direction of the vehicle  110  and images a front region of the vehicle  110  and each of the left camera  112  and the right camera  113  has an imaging element such as a CCD to convert light into a charge signal. The left image acquisition unit  114  acquires a charge signal from the left camera  112  every constant cycle and outputs a left image. The right image acquisition unit  115  acquires a charge signal from, the right camera  113  at timing synchronized with the constant cycle and outputs a right image. 
         [0025]    The disparity image acquisition unit  116  acquires the individual images of the two cameras, that is, the left image output from the left image acquisition unit  114  and the right image output from the right image acquisition unit  115 , compares the left image and the right image, calculates a disparity for each pixel of the right image, and outputs a disparity image in which the disparity is stored for each pixel. Here, the disparity represents a deviation of the left and right images based on a distance of the vehicle width direction between the left camera  112  and the right camera  113  and the disparity is an amount that can be converted into a distance by a principle of triangulation. Hereinafter, the disparity of each pixel stored in the disparity image is called distance data for simplification. 
         [0026]    The road surface height estimation unit  117  detects and estimates a position of the road surface  101 , on the basis of the disparity image output from the disparity image acquisition unit  116 , and outputs a height position of the road surface  101  and a farthest distance of the road surface  101 . Here, the height position of the road surface  101  is a height from a ground plane to be a reference and a height position of the road surface  101  corresponding to each distance of a depth direction is output, In addition, the farthest distance of the road surface  101  shows a farthest distance from the vehicle  110  among distances of the road surface  101  detected by the road surface height estimation unit  117 . 
         [0027]    The near-far boundary distance setting unit  118  acquires the farthest distance of the road surface  101  output from the road surface height estimation unit  117 , sets the farthest distance as a near-far boundary distance to be a boundary between a near region and a far region of the vehicle  110 , and outputs the farthest distance. That is, the near-far boundary distance setting unit  118  sets a boundary Rb between a near region R 1  close to the vehicle  110  and a far region R 2  distant from the vehicle  110 , in a single image of one of the two cameras, for example, the right image of the right camera  113 . 
         [0028]    The near object detecting unit  119  detects an object of the near region R 1 , on the basis of the disparity calculated by the disparity image acquisition unit  116 . More specifically, the near object detecting unit  119  extracts a group of distance data in which the distance data to be the disparity of each pixel of the disparity image is continuous in a depth direction and a transverse direction, from the disparity image, using the disparity image in which the disparity calculated for each pixel of the near region R 1  of the right image is stored for each pixel, and detects an object of the near region R 1 . 
         [0029]    In other words, the near object detecting unit  119  extracts a group of distance data continuous in the depth direction and the transverse direction from the disparity image, in the near region R 1  of the vehicle  110 , on the basis of the disparity image output from the disparity image acquisition unit  116  and the near-far boundary distance output from the near-far boundary distance setting unit  118 , detects the preceding vehicle  102  of the near region R 1  and the obstacle such as the guardrail  104 , and outputs a detection result. 
         [0030]    The far object detecting unit  120  detects an object of the far region R 2 , on the basis of the right image of the right camera  113 , for example. More specifically, the far object detecting unit  120  executes pattern matching using an image, in the far region distant from the vehicle  110 , on the basis of the right image output from the right camera  113  and the boundary Rb to be the near-far boundary distance output from the near-far boundary distance setting unit  118 . As a result, the far object detecting unit  120  detects the far preceding vehicle  103  and outputs a detection result. 
         [0031]    The near-far object integrating unit  121  outputs detection results of the. objects of the near region and the far region of the vehicle  110 , on the basis of the detection result output from the near object detecting unit  119  and the detection result output from the far object detecting unit  120 . Specifically, the near-far object integrating unit  121  integrates the detection results of the object of the near region R 1  and the object of the far region R 2  to prevent contradiction in a geometrical position relation, on the basis of the detection result of the preceding vehicle  102  of the near region R 1  close to the vehicle  110  and the obstacle and the detection result of the preceding vehicle  103  of the far region R 2  distant from the vehicle  110 , and outputs a result. 
         [0032]    The travel control unit  122  controls an accelerator and a brake, on the basis of the detection result of the preceding vehicles  102  and  103  output from the near-far object integrating unit  121  of the stereo camera device  111  or the obstacle such as the guardrail  104 . As a result, the travel control unit  122  performs travel support of the vehicle  110  to cause the vehicle  110  to avoid a collision with the preceding vehicles  102  and  103  or the guardrail  104  or cause the vehicle  110  to automatically track the preceding vehicles  102  and  103 . 
         [0033]      FIG. 2  is a block diagram illustrating a schematic configuration of the road surface height estimation unit  117 . 
         [0034]    The road surface height estimation unit  117  includes a road shape setting unit  201 , a virtual plane setting unit  202 , a straight line detecting unit  203 , and a farthest distance detecting unit {boundary pixel position detecting unit}  204 , for example. 
         [0035]    The road shape setting unit  201  sets a shape of a road on which the vehicle  110  is travelling, calculates an image region of the road obtained by projecting the set road shape onto an image, and outputs the image region. The virtual plane setting unit  202  projects distance data entering the image region of the road output from the road shape setting unit  201 , that is, data of the disparity corresponding to each pixel and a coordinate position of each pixel as a histogram onto a two-dimensional virtual plane and outputs a V-Disparity image. Here, the virtual plane onto which the virtual plane setting unit  202  projects the distance data is a two-dimensional space in which a longitudinal axis shows a coordinate position V of a pixel of a longitudinal direction and a transverse axis shows a disparity. In addition, the V-Disparity image is an image in which a histogram frequency showing a total number of data points is stored for each pixel of a grid virtual plane. 
         [0036]    The straight line detecting unit  203  acquires a V-Disparity image output from the virtual plane setting unit  202 , detects a straight line transmitting a pixel in which a histogram frequency of the V-Disparity image is high, and calculates a road surface height position on the basis of the straight line. The farthest distance detecting unit  204  detects a distance of the farthest road surface  101  in which a road surface estimation result is reliable, on the basis of a road surface estimation result by the straight line detecting unit  203 , and outputs the distance. 
         [0037]      FIGS. 3( a ) and 3( b )  are image views illustrating processing by the road shape setting unit  201 . 
         [0038]    As illustrated in  FIG. 3( a )  , the road shape setting unit  201  sets a region that is more likely to be a road in the right image  301 , for example, a region such as a trapezoidal region  302  as a road shape. In the case in which a road surface extending in a straight direction and having a constant width and a constant gradient is assumed, the trapezoidal region  302  can be obtained by calculating a projection position of the road surface on an image. As such, in the case in which a shape of the road extending in the straight direction is assumed, when the road shape is actually curved, there is a problem in that a deviation occurs in the set road shape and the actual road shape. 
         [0039]    In this case, the road shape setting unit  201  preferably estimates a curve direction of a successive road, estimates a road surface extending in a curve direction and having a constant width and a constant gradient, and calculates a projection position of the estimated road surface on an image, as illustrated in  FIG. 3( b ) . Thereby, a curved region such as a curve region  303  can be set as the road shape. The curve direction of the road can be estimated by detecting a white line on the basis of image information or detecting a road shoulder on the basis of distance information. 
         [0040]      FIG. 4( a )  illustrates the right image  301  of the right camera  113  illustrating processing by the virtual plane setting unit  202 .  FIG. 4( b )  illustrates a V-Disparity image obtained by projecting each distance data of a disparity image corresponding to the right image  301  onto the virtual plane. 
         [0041]    A flat road surface  400  in a near region of the vehicle  110  and an ascending road surface  401  in a far region of the vehicle  110 , which are illustrated in  FIG. 4( a )  , have properties of being projected linearly in oblique directions having different gradients, like a first direction  402  and a second direction  403 , on the V-Disparity image illustrated in  FIG. 4( b )  . In addition,, the obstacles such as the preceding vehicles  102  and  103  have properties of being projected linearly in a vertical direction, like data of regions  404  and  405  surrounded by solid lines. 
         [0042]    In the road surface height estimation unit  117 , distance data of the obstacles of the straight line shape of the vertical direction illustrated in the regions  404  and  405  becomes noise. Therefore, the road surface height estimation unit  117  calculates a relational expression representing a straight line of distance data of the road surface  400  along the first direction  402  and the second direction  403 , without being affected by the noise. 
         [0043]      FIG. 5( a )  illustrates the right image  301  of the right camera  113  illustrating processing by the straight line detecting unit  203  and the farthest distance detecting unit  204  .  FIG. 5( b )  illustrates a V-Disparity image illustrating processing of the straight line detecting unit  203  and the farthest distance detecting unit  204 . 
         [0044]    For example, the straight line detecting unit  203  first converts the V-Disparity image into an image binarized with a constant threshold value and detects a straight line by Hough transformation, so that the straight line detecting unit  203  detects a most dominant straight line  501  in the V-Disparity image, as illustrated in  FIG. 5( b ) . The straight line  501  shows a relation of a projected longitudinal position of an estimated road surface on an image and a disparity. The straight line detecting unit  203  converts the relation of the longitudinal position and the disparity into a relation of a distance of a depth direction of a depth direction of a three-dimensional space and a road surface height position and outputs a road surface height position for each distance. 
         [0045]    In addition, in an example illustrated in  FIG. 5( a ) , a gradient of a road surface  503  in the far region of the vehicle  110  changes with respect to a road surface  502  in the near region of the vehicle  110 . For this reason, the straight line  501  detected OR the V-Disparity image illustrated in  FIG. 5( b )  fits with distance data of a road surface well in a near region  504  of the vehicle  110 . However, it is known that the straight line  501  generates a deviation with a position of distance data of an actual road surface in a far region  505  of the vehicle  110  and reliability of a road surface estimation result is low. 
         [0046]    The farthest distance detecting unit  204  detects and outputs a farthest distance of the road surface  502  where a road surface estimation result is reliable, in the near region of the vehicle  110 . For this reason, the farthest distance detecting unit  204  confirms a histogram frequency of each pixel which the straight line  501  detected by the straight line detecting unit  203  passes through sequentially from a near side of the vehicle  110  and detects a first pixel position when pixels where histogram frequencies become a constant value or less are continuous by a constant number or more as a boundary pixel position  506 . 
         [0047]    That is, the farthest distance detecting unit  204  to be a boundary pixel position detecting unit detects the boundary pixel position  506 , on the basis of a deviation of the straight line  501  in the V-Disparity image and a position of distance data. The farthest distance detecting unit  204  converts a disparity in the boundary pixel position  506  into a distance and calculates a farthest distance of the road surface  502  in the near region R 1  of the vehicle  110 . 
         [0048]    As such, a range of the road surface  502  closer to the vehicle  110  than the boundary pixel position  506  to be a farthest distance can be regarded as the near region R 1  of the vehicle  110  where reliability of a road surface estimation result, that is, a distance to the road surface  502  is high. In addition, a range of the road surface  503  more distant from the vehicle  110  than the boundary pixel position  506  to be the farthest distance can be regarded as the far region R 2  of the vehicle  110  where reliability of a road surface estimation result, that is, a distance to the road surface  503  is low. 
         [0049]    In this way, the road surface height estimation unit  117  calculates the distances to the road surfaces  502  and  503  in front of the vehicle  110 , on the basis of the disparity calculated by the disparity image acquisition unit  116 , and detects the boundary pixel position  506  between the region  504  where reliability of the distance to the road surface  502  is high and the region  505  where reliability of the distance to the road surface  503  is low. In addition, the near-far boundary distance setting unit  118  sets the boundary Rb between the near region R 1  and the far region R 2 , on the basis of the boundary pixel position  506 . 
         [0050]      FIG. 6( a )  illustrates the right image of the right camera  113  illustrating processing by the far object detecting unit  120 .  FIG. 6( b )  illustrates a V-Disparity image illustrating processing by the far object detecting unit  120 . 
         [0051]    The far object detecting unit  120  acquires the right image output from the right camera  113  and illustrated in  FIG. 6( a ) , the boundary pixel position  506  showing the near-far boundary distance output from the farthest distance detecting unit  204 , and the trapezoidal region  302  to be an image region of the road shape output from, the road shape setting unit  201 . In addition, the far object detecting unit  120  sets a search range  601  of a preceding vehicle to the far region R 2  on the right image. 
         [0052]    Using an estimation error a between a coordinate position of a longitudinal direction of a pixel at the boundary Rb of the right image of the right camera  113  and the boundary pixel position  506  of the V-Disparity image, the far object detecting unit  120  sets one end of a longitudinal direction of the search range  601  to a position to be a sum of the boundary pixel position  506  and the estimation error α. That is, the far object detecting unit  120  performs setting such that a lower end position of the search range  601  becomes a sum Ve+α of an image longitudinal position Ve at the boundary pixel position  506  of the straight line  501  of the road surface estimation result illustrated in  FIG. 6( b )  and the estimation error α. 
         [0053]    Here, the estimation error α is a previously set value of an estimation error of an image longitudinal position of a road surface assumed at the boundary pixel position  506  of the road surface estimation result. The estimation error a tends to become relatively small on an expressway where the road surface  502  is relatively flat and tends to become relatively large on a general road where a gradient of the road surface  502  is large. Therefore, the estimation error a may be calculated sequentially and changed, according to a travel situation of the vehicle  110 . 
         [0054]    Using an image longitudinal width H of an object of a maximum height assumed at the boundary Rb of the right image of the right camera  113 , the far object detecting unit  120  sets the other end of the longitudinal direction of the search range  601  to a position obtained by subtracting the image longitudinal width H and the estimation error a from the boundary pixel position  506 . That is, the far object detecting unit  120  performs setting such that an upper end position of the search range  601  becomes Ve−H−α obtained by subtracting the value a and the image longitudinal width H from the image longitudinal position Ve at the boundary pixel position  506 . Here, the image longitudinal width H is an image longitudinal width when a vehicle having a maximum vehicle height assumed at the boundary pixel position  506  is projected onto an image. 
         [0055]    The far object detecting unit  120  sets positions of left and right ends of the search range  601  to positions of left and right ends of the trapezoidal region  302  to be the image region of the road shape at the boundary pixel position  506 . In this way, the far object detecting unit  120  sets the search range  601  to an image range where there may be a vehicle after the boundary pixel position  506 . 
         [0056]    Next, the far object detecting unit  120  scans a template  602  in the search range  601 , executes pattern matching, and detects the preceding vehicle  103  in the far region R 2  distant from the vehicle  110 . The far object detecting unit  120  previously learns a feature of an image of the preceding vehicle  103  in the pattern matching and compares a learned feature amount and a feature amount of an image of the template  602 . In addition, when a difference of the feature amounts is a constant amount or less, the far object detecting unit  120  assumes a target object as the vehicle and detects the preceding vehicle  103  at a position of the template. 
         [0057]    A size of an image onto which the preceding vehicle  103  is projected is different according to a distance where the targeted preceding vehicle  103  exists. For this reason, using a plurality of templates  602  having different sizes, the far object detecting unit  120  repetitively executes detection processing of the preceding vehicle by the search by the number of templates  602  having the different sizes. As such, the far object detecting unit  120  executes object detection processing using the camera image output from the right camera  113 , in only the far region R 2  distant from the vehicle  110 . 
         [0058]      FIGS. 7( a ) and 7( b )  illustrate right images of the right camera  113  illustrating processing by the near-far object integrating unit  121 , 
         [0059]    As illustrated in  FIG. 7( a ) , when a detection result  701  of a near object such as the preceding vehicle  102  is obtained by the near object detecting unit  119  and detection results  702  and  703  of far objects such as the preceding vehicle  103  are obtained by the far object detecting unit  120 , the near-far object integrating unit  121  checks a position relation of the near object and the far object. In addition, the near-far object integrating unit  121  erases an object of a long distance for detection objects overlapping each other on an image, like the detection results  702  and  703 . 
         [0060]    In an example illustrated in  FIG. 7( a )  , the near-far object integrating unit  121  erases the detection result  703  of the long distance in the detection results  702  and  703  overlapping each other on the image. As a result, the near-far object integrating unit  121  outputs the detection results  701  and  702  as near-far objects, as illustrated in  FIG. 7( b ) . Even when a detection result in the near region of the vehicle  110  and a detection result in the far region of the vehicle  110  overlap each other on the image, the near-far object integrating unit  121  erases the detection result of the long distance. As such, the reason why the object of the long distance is erased when the object regions to be the detection results overlap each other on the image is that the object of the long distance is shielded by the object of the short distance and is not viewed and the possibility of erroneous detection is high. 
         [0061]    Hereinafter, functions of the object detecting device  100  according to this embodiment will be described. 
         [0062]    As described above, for example, in the object detecting device according to the related art described in PTL 1, a surface is detected on the basis of a distance image and a pixel group of a predetermined height or more with the surface as a reference among pixel groups corresponding to the surface is detected as a detection target object. For this reason, erroneous detection or non-detection for a far object with small pixels may occur frequently. This is because an information amount obtained from a sensor in the far region is smaller than an information mount in the near region, the number of distance data to be obtained is small, and precision of the distance obtained from the sensor is lowered. 
         [0063]    As such, if the number of road surface data to be obtained is small and the precision of the distance is lowered, separation of distance data of a far object and distance data of a road surface becomes difficult, a position error of the road surface to be estimated in the far region increases, and error detection or non-detection of the object is generated. For example, the position of the road surface is estimated at a position below an actual position, so that the region of the road surface may be detected as the object (erroneous detection), or the position of the road surface is estimated at a position on the actual position, so that the object existing on the road surface may not be detected (non-detection), 
         [0064]    In addition, in the solid object detecting device according to the related art described in PTL 2, erroneous detection of a solid object existing on the road surface can be reduced. However, it is necessary to divide the distance data into the solid object and the road surface in advance to detect the solid object existing on the road surface. The division of the distance data becomes difficult in the far region where the distance data decreases. When the solid object cannot be divided as a solid object correctly in the far region, there is concern that non-detection of the object may not be prevented. 
         [0065]    In the case in which an image is searched and an image feature amount is compared and evaluated, when an image size cannot be reduced sufficiently and a search range of the image cannot be narrowed sufficiently, processing time tends to increase as compared with the case of detecting an object from, the distance data as in PTL 1. Particularly, because the image size of the object increases in the near region and the image search range when the image is searched is widened, there is concern that the processing time may increase greatly as compared with the method described in PTL 1. 
         [0066]    Meanwhile, the object detecting device  100  according to this embodiment includes not only the disparity image acquisition unit (disparity acquisition unit)  116  comparing the individual images of the left and right two cameras  112  and  113  and calculating the disparity for each pixel but also the near-far boundary distance setting unit (near-far boundary setting unit)  118 . In addition, the boundary Rb between the near region R 1  close to the vehicle  110  and the far region R 2  distant from the vehicle  110  in the right image to be the single image of the right camera  113  of the left and right two cameras is set by the near-far boundary distance setting unit  118 . In addition, the object of the near region R 1  is detected by the near object detecting unit  119  on the basis of the disparity and the object of the far region R 2  is detected by the far object detecting unit  120  on the basis of the right image. 
         [0067]    As a result, the object detecting device  100  can detect the object of the far region R 2  with the small pixels in which the detection of the object by the distance data is difficult can be accurately detected on the basis of the right image of the right camera  113 , without depending on the distance data. Therefore, according to the object detecting device  100  according to this embodiment, occurrence of erroneous detection or non-detection of the object in the far region R 2  can be suppressed and the object can be detected accurately even in the far region of the vehicle  110 . In addition, the processing target when the object is detected from the distance data based on the disparity can be narrowed to the near region R 1 , the data processing amount can be decreased, and the processing time can be shortened. 
         [0068]    The object detecting device  100  according to this embodiment further includes the road surface height estimation unit (road surface estimation unit)  117  that calculates the distance to the road surface in front of the vehicle  110  on the basis of the disparity and detects the boundary pixel position  506  between the region where the reliability of the distance is high and the region where the reliability of the distance is low. In addition, in the object detecting device  100  according to this embodiment, the near-far boundary distance setting unit (near-far boundary setting unit)  118  sets the boundary Rb on the basis of the boundary pixel position  506 . As a result, the reliability of the distance data in the near region R 1  can be increased and the detection precision of the object in the near region R 1  can be improved. 
         [0069]    In addition, the road surface height estimation unit  117  includes the virtual plane setting unit  202 , the straight line detecting unit  203 , and the farthest distance detecting unit (boundary pixel position detecting unit)  204 . The virtual plane setting unit  202  outputs the V-Disparity image and the straight line detecting unit  203  detects the most dominant straight line  501  in the V-Disparity image. In addition, the farthest distance detecting unit  204  detects the boundary pixel position  506  on the basis of the deviation of the straight line  501  in the V-Disparity image and the position of the distance data. Thereby, the boundary pixel position  506  between the region  504  where the reliability of the distance to the road surface is high and the region  505  where the reliability of the distance is low can be detected by the road surface height estimation unit  117 . 
         [0070]    In addition, the far object detecting unit  120  sets the search range  601  to the far region R 2  on the right image, scans the template  602  in the search range  601 , and detects the object of the far region R 2  by the pattern matching. As a result, the object can be detected accurately on the basis of the right image, without depending on the distance data, in the tar region R 2 . 
         [0071]    In addition, the far object detecting unit  120  sets the lower end of the longitudinal direction of the search range  601  to the position to be the sum of the boundary pixel position  506  and the estimation error a, using the estimation error a between the coordinate position of the longitudinal direction of the pixel at the boundary Rb of the right image and the boundary pixel position  506  of the V-Disparity image. As a result, the search range  601  can be set in a more appropriate range having considered the estimation error α. 
         [0072]    In addition, the far object detecting unit  120  sets the upper end of the longitudinal direction of the search range  601  to a position obtained by subtracting the image longitudinal width H and the estimation error α from the boundary pixel position  506 , using the image longitudinal width H of the object of the maximum height assumed at the boundary Rb of the right image. As a result, the search range  601  can be set in a more appropriate range having considered the estimation error a and the image longitudinal width H. 
         [0073]    The object detecting device  100  according to this embodiment further includes the road shape setting unit  201  that sets the road shape in front of the vehicle  110  using the right image. In addition, the far object detecting unit  120  sets the positions of the left and right ends of the search range  601  to the positions of the left and right ends of the image region of the road shape. As a result, the search range  601  can be further narrowed and the processing time can be shortened. 
         [0074]    In addition, the far object detecting unit  120  scans the search range  601  repetitively using the plurality of templates  602  having the different sizes. As a result, detection from the preceding vehicle close to the vehicle  110  to the preceding vehicle distant from the vehicle  110  in the far region R 2  can be performed surely. 
         [0075]    The object detecting device  100  according to this embodiment further includes the near-far object integrating unit  121  that erases the detection result  703  of the object of the long distance in the detection results  702  and  703  of the objects displayed on the right image and overlapping each other. As a result, the detection result  703  that is more likely to be detected by the erroneous detection can be erased and the erroneous detection can be reduced. 
         [0076]    In addition, the near object detecting unit  119  extracts a group of distance data in which distance data to be the disparity of each pixel of the disparity image is continuous in the depth direction and the transverse direction, from the disparity image, using the disparity image in which the disparity calculated for each pixel of the near region R 1  of the right image is stored for each pixel, and detects the object of the near region R 1 . As a result, the near object detecting unit  119  can detect the object of the near region R 1  accurately on the basis of the disparity. 
         [0077]    The embodiment of the present invention has been described in detail using the drawings. However, the specific configuration is not limited to the embodiment and a design change made without departing from the scope of the present invention is included in the present invention. 
         [0078]    For example, in the embodiment, the example of the case in which the far preceding vehicle is detected by the object detecting device has been described. However, the object detecting device according to the present invention can be applied to even when a pedestrian or other obstacle is detected, in addition to detection of the preceding vehicle. 
         [0079]    In addition, in the embodiment, the example of the case in which the stereo camera device is used has been described. However, the present invention can be applied to a sensor configuration in which image information and distance information are obtained as sensor output values, such as a monocular camera and a laser radar and the monocular camera and a millimeter wave radar, in addition to the stereo camera. 
         [0080]    In addition, in the embodiment, the example of the driving support device for the vehicle that detects the preceding vehicle existing on the road surface by the object detecting device and performs travel control has been described. However, the present invention can be applied to a peripheral monitoring device and a driving support device for a ship that detect a marine obstacle and a peripheral monitoring device and a driving support device for an airplane that detect an obstacle on a ground plane, in addition to the driving support device for the vehicle. 
         [0081]    In addition, in the embodiment, the object is detected using only the right image of the camera in the far region distant from the vehicle. However, the object may be detected using the right image or the left image and the disparity image together. 
       REFERENCE SIGNS LIST 
       [0000]    
       
           100  object detecting device 
           101  road surface 
           102 ,  103  preceding vehicle (object) 
           104  guardrail (object) 
           110  vehicle 
           112  left camera (camera) 
           113  right camera (camera) 
           116  disparity image acquisition unit (disparity acquisition unit) 
           117  road surface height estimation unit (road surface estimation unit) 
           118  near-far boundary distance setting unit (near-far boundary setting unit) 
           119  near object detecting unit 
           120  far object detecting unit 
           121  near-far object integrating unit 
           122  travel control unit 
           201  road shape setting unit 
           202  virtual plane setting unit 
           203  straight line detecting unit 
           204  farthest distance detecting unit (boundary pixel position detecting unit) 
           302  trapezoidal region (road shape) 
           303  curve region (road shape) 
           400 ,  401  road surface 
           502 ,  503  road surface 
           504  region where reliability of distance is high 
           505  region where reliability of distance is low 
           506  boundary pixel position 
           601  search range 
           602  template 
         H image longitudinal width 
         R 1  near region 
         R 2  far region 
         Rb boundary 
         α estimation error