Abstract:
The present invention relates to a vehicle periphery monitoring device. An object identification unit comprises: a first identifier requiring a relatively low computation volume for an object identification process; and a second identifier requiring a relatively high computation volume for the object identification process. A region to be identified determination unit determines at least one region to be identified which is presented in the identification process by the second identifier, by carrying out a clustering process relating to location and/or scale, with respect to a plurality of region candidates which are extracted by the first identifier as wherein objects are present.

Description:
TECHNICAL FIELD 
     The present invention relates to a vehicle periphery monitoring device, which detects objects existing around the periphery of a vehicle, based on a captured image acquired from an imaging unit that is mounted on the vehicle. 
     BACKGROUND ART 
     Conventionally, for the purpose of supporting vehicle driving, a technique has been known for detecting objects that exist outside of a vehicle, or for monitoring a relative positional relationship between such objects and a driver&#39;s own vehicle. In particular, various methods have been proposed to capture images using a camera (image capturing unit) that is mounted on the front of a vehicle, and to detect objects based on the captured images obtained therefrom. 
     For example, in “Nighttime Pedestrian Detection”, ViEW 2008, Proceedings of the Vision Engineering Workshop, pages 224-228, a technique is disclosed in which, after a number of area candidates have been extracted by performing a simple primary identification process, e.g., boosting, having a short computation time with respect to an entire captured image, a detailed secondary identification process, e.g., SVM (Support Vector Machine), having a long computation time is applied sequentially with respect to each of the area candidates. 
     SUMMARY OF INVENTION 
     Incidentally, although it is possible to capture the form of an object by performing the simple primary identification process, there is a tendency for it not to be possible to accurately detect the position of the object. More specifically, even for the same object, cases occur in which plural area candidates that differ slightly in position or the like are extracted. Therefore, concerning the secondary identification process, as a practical matter, it becomes necessary to carry out needless computations with respect to the same object, leading to a problem in that a sufficient effect of reducing the amount of computation cannot be obtained. 
     The present invention has been devised as a solution to the aforementioned problem, and has the object of providing a vehicle periphery monitoring device, which enables both a reduction in the amount of computation and an improvement in accuracy in relation to an object identification process. 
     A vehicle periphery monitoring device according to the present invention includes an image capturing unit mounted on a vehicle and configured to acquire a captured image around a periphery of the vehicle by performing image capturing while the vehicle is traveling, an identification target area determiner configured to determine identification target areas from within an image area in the captured image acquired by the image capturing unit, and an object identifier configured to identify whether or not an object exists within the identification target areas determined by the identification target area determiner. The object identifier includes a first identifying unit for which a computation amount thereof required by an identification process for identifying the object is relatively small, and a second identifying unit for which a computation amount thereof required by an identification process for identifying the object is relatively large. The identification target area determiner determines at least one of the identification target areas to be subjected to the identification process by the second identifying unit, by performing a clustering process in relation to position and/or scale with respect to plural area candidates extracted by the first identifying unit as areas in which the object exists. 
     As described above, the object identifier includes a first identifying unit for which a computation amount thereof required by an identification process for identifying the object is relatively small, and a second identifying unit for which a computation amount thereof is relatively large. In addition, the identification target area determiner determines at least one of the identification target areas to be subjected to the identification process by the second identifying unit, by performing the clustering process in relation to position and/or scale, with respect to plural area candidates extracted by the first identifying unit as areas in which the object exists. Consequently, with respect to identification target areas that have been suitably classified, the identification process can be executed by the second identifying unit for which the computation amount thereof is relatively large, whereby an improvement in accuracy and a reduction in the amount of computation in relation to the object identification process can both be achieved. 
     Further, preferably, the identification target area determiner may divide the image area into a plurality of sub-areas, and may determine the identification target areas by performing the clustering process in relation to position with respect to each of representative positions of the area candidates calculated in each of the sub-areas. By this feature, the data amount (total number of different positions) used for the clustering process can be reduced, so that the computation process can be realized at a faster speed. 
     The identification target area determiner preferably classifies the plural area candidates into two or more groups depending on scale, and determines the identification target areas, respectively, by performing the clustering process on each of the groups. In this manner, with respect to identification target areas that have been suitably classified by scale, the identification process is executed by the second identifying unit, whereby error factors due to variations in scale can be reduced, and identification accuracy can be further enhanced. 
     Furthermore, preferably, the identification target area determiner, after having classified the plural area candidates into at least one position group by performing a previous stage clustering process in relation to at least position, may classify the area candidates belonging to the at least one position group into the two or more groups depending on scale, and then may determine the identification target areas, respectively. By using the first identifying unit for which the computation amount thereof is relatively small, there is a tendency to extract area candidates of similar scale at substantially the same position. Thus, by classifying the plural area candidates into at least one position group, it is possible to exclude beforehand the existence of area candidates which are similar in scale but are separated in position, whereby identification accuracy can be further enhanced. 
     According to the vehicle periphery monitoring device of the present invention, the object identifier includes a first identifying unit for which a computation amount thereof required by an identification process for identifying the object is relatively small, and a second identifying unit for which a computation amount thereof is relatively large. In addition, the identification target area determiner determines at least one of the identification target areas to be subjected to the identification process by the second identifying unit, by performing a clustering process in relation to position and/or scale, with respect to plural area candidates extracted by the first identifying unit as areas in which the object exists. Consequently, with respect to identification target areas that have been suitably classified, the identification process can be executed by the second identifying unit for which the computation amount thereof is relatively large, whereby an improvement in accuracy and a reduction in the amount of computation in relation to the object identification process can both be achieved. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing a configuration of a vehicle periphery monitoring device according to an embodiment of the present invention; 
         FIG. 2  is a schematic perspective view of a vehicle in which the vehicle periphery monitoring device shown in  FIG. 1  is incorporated; 
         FIG. 3  is a flowchart used for describing operations of an ECU shown in  FIG. 1 ; 
         FIG. 4A  is a display image showing an example of a captured image acquired by way of image capturing using a camera; 
         FIG. 4B  is a display image showing extracted results of area candidates by a first identification process; 
         FIGS. 5A through 5C  are schematic explanatory views showing a detailed method of a clustering process in relation to position; 
         FIG. 6A  is a display image showing a determination result of identification target areas by a second identification process; 
         FIG. 6B  is a display image showing a detection result of monitored objects; 
         FIGS. 7A and 7B  are schematic explanatory views showing examples of results of a clustering process; 
         FIG. 8  is a flowchart used for describing a clustering process according to a first improvement; 
         FIG. 9  is a schematic explanatory view showing a detailed method of a scale division process performed in step S 74  of  FIG. 8 ; and 
         FIG. 10  is a schematic explanatory view showing a detailed method of a clustering process according to a second improvement. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     A preferred embodiment of a vehicle periphery monitoring device according to the present invention will be described below with reference to the accompanying drawings. 
     [Configuration of Vehicle Periphery Monitoring Device  10 ] 
       FIG. 1  is a block diagram showing a configuration of a vehicle periphery monitoring device  10  according to the present embodiment.  FIG. 2  is a schematic perspective view of a vehicle  12  in which the vehicle periphery monitoring device  10  shown in  FIG. 1  is incorporated. 
     As shown in  FIGS. 1 and 2 , the vehicle periphery monitoring device  10  is equipped with a color camera (hereinafter referred to as a “camera  14 ”) that captures a color image (hereinafter referred to as an “image Im”) made up from a plurality of color channels, a vehicle speed sensor  16  for detecting a velocity Vs of the vehicle  12 , a yaw rate sensor  18  for detecting a yaw rate Yr of the vehicle  12 , a brake sensor  20  for detecting an operating amount Br by which a brake pedal is operated by a driver, an electronic control unit (hereinafter referred to as an “ECU  22 ”) that controls the vehicle periphery monitoring device  10 , a speaker  24  for issuing an alarm or the like by voice, and a display unit  26  that displays a captured image that is output from the camera  14 . 
     The camera  14  is a camera using light having wavelengths primarily in the visible range, and functions as an image capturing means (image capturing unit) for capturing an image of the periphery of the vehicle  12 . The camera  14  has characteristics such that, the greater the amount of light reflected from the surface of a subject is, the higher the level of an output signal from the camera becomes, thereby resulting in an increase in brightness, e.g., RGB values, of the image. As shown in  FIG. 2 , the camera  14  is fixedly disposed (mounted) in a substantially central region of a front bumper of the vehicle  12 . 
     The image capturing means for capturing an image around the periphery of the vehicle  12  is not limited to the above structure (a so-called monocular camera), but may be a multiocular camera (stereo camera). Further, instead of a color camera, the image capturing means may be a monochromatic camera or an infrared camera, or one or both of such cameras may be used in combination with a color camera. Moreover, if the image capturing means is a monocular camera, then the monocular camera may be combined with a range finding means (radar apparatus). 
     Referring back to  FIG. 1 , the speaker  24  produces a warning sound or the like on the basis of a command from the ECU  22 . The speaker  24  is mounted on a non-illustrated dashboard of the vehicle  12 . Alternatively, a speech output function, which belongs to another apparatus (e.g., an audio system or a navigation system), may be used as the speaker  24 . 
     The display unit  26  (see  FIGS. 1 and 2 ) is a head-up display (HUD), which is positioned such that a display screen thereof is displayed on the front windshield of the vehicle  12 , at a position where the display screen does not obstruct the forward vision of the driver. The display unit  26  is not limited to a HUD, but may be a display unit for displaying a map, etc., of a navigation system incorporated in the vehicle  12 , or a display unit (multi-information display unit: MID) disposed in a meter unit for displaying mileage information, etc. 
     The ECU  22  basically includes an input/output unit  28 , a processor  30 , a display controller  32 , and a memory  34 . 
     Signals from the camera  14 , the vehicle speed sensor  16 , the yaw rate sensor  18 , and the brake sensor  20  are input to the ECU  22  through the input/output unit  28 . Further, signals from the ECU  22  are output through the input/output unit  28  to the speaker  24  and the display unit  26 . The input/output unit  28  includes a non-illustrated A/D conversion circuit for converting input analog signals into digital signals. 
     The processor  30  performs processing sequences on the signals from the camera  14 , the vehicle speed sensor  16 , the yaw rate sensor  18 , and the brake sensor  20 , and on the basis of results of the processing operations, generates signals that are supplied to the speaker  24  and the display unit  26 . The processor  30  functions as an identification target area determiner  40  (identification target area determining means), an object identifier  42  (object identifying means), and an object detector  44 . 
     The various functions of the components of the processor  30  are realized by reading and executing programs stored in the memory  34 . Alternatively, the programs may be supplied from an external source via a non-illustrated wireless communications unit (mobile phone, smartphone, or the like). 
     The display controller  32  is a control circuit for energizing and controlling the display unit  26 . By supplying a display control signal to the display unit  26  through the input/output unit  28 , the display controller  32  energizes the display unit  26 . Accordingly, the display unit  26  is capable of displaying various types of images (the captured image Im, marks, etc.). 
     The memory  34  is composed of a random access memory (RAM) for storing captured image signals that have been converted into digital signals, temporary data used in various processing operations, etc., and a read only memory (ROM) for storing executable programs, tables and maps, etc. 
     The vehicle periphery monitoring device  10  according to the present embodiment is basically configured as described above. An outline of operations of the vehicle periphery monitoring device  10  will be described below. 
     The ECU  22  converts an analog video signal output from the camera  14  into a digital signal, and temporarily stores the digital signal in the memory  34 , at prescribed frame clock interval or period (e.g., at thirty frames per second). 
     The ECU  22  then performs various processing operations on the captured image Im (i.e., an image in front of the vehicle  12 ) which is read from the memory  34 . The ECU  22 , and in particular the processor  30 , comprehensively evaluates the results of the processing sequences carried out on the captured image Im. Further, if necessary, the ECU  22  comprehensively evaluates respective signals (the vehicle speed Vs, the yaw rate Yr, and the operating amount Br) which indicate the traveling state of the vehicle  12 . Then, the ECU  22  detects a human body, an animal, or the like that exists in front of the vehicle  12  as an object to be monitored (hereinafter referred to as a “monitoring object” or simply an “object”). 
     If the processor  30  assesses that there is a high possibility for the vehicle  12  to come into contact with the monitoring object, the ECU  22  controls each of the output units of the vehicle periphery monitoring device  10  in order to call the attention of the driver. For example, the ECU  22  controls the speaker  24  to output a warning sound, e.g., a succession of blips, and highlights the monitoring object in the captured image Im, which is displayed visually on the display unit  26 . 
     [Detailed Operations of Vehicle Periphery Monitoring Device  10 ] 
     Next, operations of the vehicle periphery monitoring device  10  will be described below with reference to the flowchart shown in  FIG. 3 . The sequence of operations or process flow is carried out for each of respective captured frames while the vehicle  12  is traveling. 
     In step S 1 , for each frame, the ECU  22  acquires a captured image Im of an area that lies within a given angle of view in front of the vehicle  12 , which is represented by an output signal from the camera  14 . 
     As shown in  FIG. 4A , it is assumed that the ECU  22  acquires a captured image Im in one frame at a given time from the camera  14 . The captured image Im represents an image area  60  having a horizontally elongate rectangular shape made up of horizontal rows of 1200 pixels and vertical columns of 600 pixels, for example. Within the image area  60 , there exist, starting from the left, an image region of a postal submission box (hereinafter referred to simply as a “post box  62 ”), an image region of a utility pole (hereinafter referred to simply as a “utility pole  64 ”), an image region of a road surface (hereinafter referred to simply as a “road surface  65 ”), an image region of a first pedestrian (hereinafter referred to simply as a “first pedestrian  66 ”), and an image region of a second pedestrian (hereinafter referred to simply as a “second pedestrian  68 ”). 
     In step S 2 , the processor  30  initiates a raster scanning process on the captured image Im. The raster scanning process refers to a process for sequentially identifying whether or not objects exist in the captured image Im, while a scanning region  70  in the captured image Im is moved along a prescribed scanning direction. The scanning region  70  corresponds to a region of interest for identifying whether or not an object including the first pedestrian  66  exists, as well as for identifying the type of object. 
     Thereafter, the identification target area determiner  40  sequentially determines the position and scale (size) of the currently specified scanning region  70 . In the example shown in  FIG. 4A , a rectangular scanning region  70  (the area framed by the dashed line) is set to include substantially the entirety of the first pedestrian  66 . In addition, the position of the scanning region  70  can be specified by a position (hereinafter referred to as a “contact position  72 ”) corresponding to a point of contact between an actual road surface and the first pedestrian. 
     In step S 3 , the object identifier  42  (first identifying unit  50 ) identifies whether at least one type of object exists within the determined scanning region  70  or not. The term “first identification process” as used herein implies an identification process for which an amount of computation needed by the process to identify an object is relatively small, and will hereinafter be described in detail. In addition, in the case that an object is identified as existing, an area candidate extraction unit  46  extracts the scanning region  70  as an area candidate  74  (refer to  FIG. 4B ). 
     In step S 4 , the identification target area determiner  40  assesses whether or not the scanning process has been completed entirely within the designated area. If the identification target area determiner  40  assesses that the scanning process has not been completed (step S 4 : NO), then control proceeds to the next step (step S 5 ). The designated area, which is the object that is scanned, may be the entire image area  60  or a portion of the area. 
     In step S 5 , the identification target area determiner  40  changes the position or scale of the scanning region  70 . More specifically, the identification target area determiner  40  moves the current scanning region  70  to a position where scanning has not yet been performed, and more specifically, moves the current scanning region by a predetermined amount (e.g., by one pixel) in a predetermined scanning direction (e.g., a rightward direction). 
     Thereafter, control returns to step S 3 , and the processor  30  repeats steps S 3  through S 5  until the scanning process has been completed within all of the designated areas. If it is judged that the scanning process is completed (step S 4 : YES), then in step S 6 , the processor  30  terminates the raster scanning process on the captured image Im. 
     As shown in  FIG. 4B , it is assumed that from within the image area  60 , plural area candidates  74  are extracted. In this regard, an area candidate group  75  is an aggregate of area candidates  74  including at least a portion of the post box  62 . Further, an area candidate group  76  is an aggregate of area candidates  74  including at least a portion of the utility pole  64 . Furthermore, an area candidate group  77  is an aggregate of area candidates  74  including at least a portion of the first pedestrian  66 . Furthermore, an area candidate group  78  is an aggregate of area candidates  74  including at least a portion of the second pedestrian  68 . 
     In addition to human bodies (e.g., the first pedestrian  66  shown in  FIG. 4A ), examples of types of objects that can be identified may include various animals (specifically, mammals such as deer, horses, sheep, dogs, cats, etc., birds, etc.) and artificial structures (specifically, vehicles, markings, utility poles, guardrails, walls, etc.). In the present embodiment, the object identifier  42  serves to detect a living body (a human body or an animal). In this case, by way of the first identification process, the first pedestrian  66  and the second pedestrian  68  are properly identified, whereas the post box  62  and the utility pole  64 , which are artificial structures, are mistakenly identified. 
     Further, although it is possible for the object identifier  42  to capture the form of an object by performing the simple first identification process, there is a tendency for it not to be possible to accurately detect the position of the object. More specifically, even for the same object, cases may occur in which plural area candidates  74  that differ slightly in position or the like are extracted. In greater detail, as shown in  FIG. 4B , in the vicinity of one post box  62 , plural area candidates  74 , which differ in position or scale, are extracted, and the area candidate group  75  is formed thereby. 
     In step S 7 , an area classifier  48  performs a clustering process with respect to each of the area candidates  74  that were extracted in step S 3 , thereby determining at least one of identification target areas  95  to  98  (see  FIG. 6A ). In this regard, the clustering process is defined by a process in which data sets are classified and divided into subsets (clusters), and is broadly divided into a hierarchical technique by which classification of data is done hierarchically, and a non-hierarchical technique in which the classification is done according to a particular evaluation function. 
     For example, using a mean shift method or a K-means clustering method, which are known non-hierarchical techniques, the area classifier  48  performs the clustering process in terms of the position and/or scale of the plural area candidates  74 . However, according to such techniques, the amount of computation increases substantially in proportion to the square of the number of data, and therefore, as the total number of area candidates  74  increases, significantly more computing time is required for this process. Thus, the identification target area determiner  40  (area classifier  48 ) divides the image area  60  into a plurality of sub-areas  80 , and the clustering process in relation to position is performed with respect to each of representative positions of the area candidates  74  that are calculated in the respective sub-areas  80 . 
     As shown in  FIG. 5A , the image area  60  is divided into a grid pattern, thereby defining a plurality of respective sub-areas  80 . In  FIG. 5A , the rectangular image area  60  is divided into a two-dimensional pattern made up of five rows and ten columns of equal blocks. Further, as shown in the figure, candidate positions  82 , which specify the respective positions of the plural area candidates  74 , are plotted on the image area  60 . The candidate positions  82 , which are indicated by circles, correspond to the respective contact positions  72  of the area candidates  74 , for example. 
     As shown in  FIG. 5B , a representative position is determined for each of the previously divided sub-areas  80 . For example, in the case that three candidate positions  82  exist within one of the sub-areas  80 , a statistical value (average value) therefor is calculated, and the statistical value is stored as the representative position in the one sub-area  80  together with the number of (three) candidate positions  82 . In addition, using a mean shift method, a maximum of fifty representative positions in the sub-areas  80  are classified in at least one group. When this calculation is made, evaluation values are weighted, respectively, corresponding to the number of candidate positions  82 , i.e., depending on the numbers that are indicated in the rectangular cells of  FIG. 5B . By this feature, the data amount (total number of different positions) used for the clustering process can be reduced, so that the computation process can be realized at a faster speed. 
     As results of the clustering process, as shown in  FIG. 5C , candidate position groups  85 ,  86 ,  87 ,  88  are extracted, respectively, corresponding to the four area candidate groups  75  to  78  (see  FIG. 4B ). In addition, reference positions  90 ,  91 ,  92 ,  93  are determined, respectively, corresponding to the candidate position groups  85  to  88 . 
     In this manner, as shown in  FIG. 6A , in the image area  60 , the identification target area determiner  40  (area classifier  48 ) determines four identification target areas  95 ,  96 ,  97 ,  98  specified from the reference positions  90  to  93 . Further, in relation to  FIGS. 5A through 5C , although a description has been given centering on a clustering process in relation to the (horizontal and vertical) positions of the plural area candidates  74 , a clustering process in relation to scale may also be applied independently or in addition thereto. Moreover, the space coordinate system to which the clustering process is applied may be a three-dimensional space (real space) or a two-dimensional space (e.g., the captured image surface). 
     In step S 8 , the object identifier  42  (second identifying unit  52 ) identifies whether or not at least one type of object exists within the identification target areas  95  to  98  that were determined in step S 7 . The term “second identification process” as used herein implies an identification process for which an amount of computation needed by the process to identify an object is relatively large. Below, differences between the first and second identification processes will be explained. 
     The first identification process (step S 3 ) is a simple process having a relatively short computing time, which enables the form of objects to be detected. On the other hand, the second identification process (step S 8 ) is a detailed process having a relatively long computing time, which enables the form and position of objects to be detected. The amount of computation is relatively large (the computing time is relatively long) due to factors such as, for example, [1] the target area on which computation is effected is large, [2] the image resolution is high, [3] the calculation method for the image feature quantities is complex, [4] there are many types of image feature quantities, [5] identification of the types of objects is further performed, [6] the method encompasses the entire content of the first recognition process, or [7] the amount of memory used is large, etc. 
     The first identifying unit  50  (or the second identifying unit  52 ), for example, is an identifying device, which is generated using a machine learning process, with an image feature quantity in the scanning region  70  (or the identification target areas  95  to  98 ) being input thereto, and information as to whether or not an object exists being output therefrom. The machine learning process may be based on any of various algorithms, including a supervised learning algorithm, an unsupervised learning algorithm, and a reinforcement learning process. Examples of learning architecture may include a boosting process including AdaBoost, a support vector machine (SVM), a neural network, and an expectation maximization (EM) algorithm. 
     As shown in  FIG. 6B , the object identifier  42  (second identifying unit  52 ) excludes the post box  62  and the utility pole  64 , which are artificial structures, and identifies each of the first pedestrian  66  and the second pedestrian  68 . In this manner, the object identifier  42  may perform the second identification process with respect to the identification target areas  95  to  98  that have been suitably classified, so that, compared to the case of regarding all of the area candidates  74  (see  FIG. 4B ) as targets to be processed, the amount of computation can be significantly reduced. 
     In step S 9 , the object detector  44  detects objects that exist within the captured image Im, and in particular, detects the first pedestrian  66  and the second pedestrian  68  that were identified in step S 8 . The object detector  44  may use an identification result in a single frame, or may take into account identification results in a plurality of frames, so that a motion vector of one object can be calculated. 
     In step S 10 , the ECU  22  stores in the memory  34  data that are needed for the next cycle of processing operations. For example, the data may include attributes of the objects (the first pedestrian  66  and the second pedestrian  68  in  FIG. 4A ) that were obtained in step S 9 , the positions of the identification target areas  97 ,  98 , or the like. 
     By successively carrying out the above operation sequence, the vehicle periphery monitoring device  10  is capable of monitoring objects that exist in front of the vehicle  12  at prescribed time intervals. 
     As described above, the object identifier  42  includes the first identifying unit  50  for which the computation amount thereof required by the identification process for identifying objects is relatively small, and the second identifying unit  52  for which the computation amount thereof is relatively large. In addition, the identification target area determiner  40  determines at least one of the identification target areas  95  to  98  to be subjected to the identification process by the second identifying unit  52 , by performing the clustering process in relation to position and/or scale, with respect to plural area candidates  74  extracted by the first identifying unit  50  as areas in which objects exist. Consequently, with respect to the identification target areas  95  to  98  that have been suitably classified, the identification process can be executed by the second identifying unit  52  for which the computation amount thereof is relatively large, whereby an improvement in accuracy and a reduction in the amount of computation in relation to the object identification process can both be achieved. 
     [Improvements in the Clustering Process] 
     Next, examples of improvements in the clustering process (step S 7  of  FIG. 3 ) according to the present embodiment will be described. 
     As shown in  FIG. 7A , with respect to the same object (second pedestrian  68 ), an area candidate group  78  is extracted in which scales of multiple levels are mixed. In this case, as a result of carrying out the clustering process simultaneously with respect to the horizontal direction, the vertical direction, and the scale direction, an identification target area  99  is set having a scale of an intermediate value within the aforementioned multiple levels (see  FIG. 7B ). Stated otherwise, by handling the multiple level scales without distinction, cases occur in which an identification target area having an appropriate scale cannot be set. Thus, the area classifier  48  may classify the plural area candidates  74  into two or more groups depending on scale, and then perform the clustering process on each of such groups. In this manner, the second identification process is executed with respect to the identification target areas  105 , etc. (see  FIG. 9 ), that have been suitably classified by scale, whereby error factors due to variations in scale can be reduced, and identification accuracy can be further enhanced. 
     &lt;First Improvement&gt; 
     First, a clustering process (step S 7 A of  FIG. 3 ) according to a first improvement will be described with reference to  FIGS. 8 and 9 .  FIG. 8  is a flowchart used for describing a clustering process according to the first improvement. 
     In step S 71 , the area classifier  48  performs a clustering process (hereinafter referred to as a first clustering process) in relation at least to position, thereby classifying each of the area candidates  74  into at least one position group. As a result, it is assumed that the respective area candidates  74  are classified into an area candidate group  78  (see  FIG. 7A ), which is one position group. 
     In step S 72 , based on the classification result of step S 71 , the area classifier  48  initiates a secondary clustering process. Below, the secondary clustering process will be explained. 
     The area classifier  48  selects one position group that has not yet been selected, i.e., one area candidate group  78  (step S 73 ), and thereafter carries out a scale division of each of the area candidates  74  that belong to the position group (step S 74 ). 
     As shown in  FIG. 9 , the area classifier  48  classifies plots of multidimensional coordinates, which are defined by a scale on the horizontal axis and coordinates (positions) on the vertical axis, into two or more levels depending on scale. As a result, the respective area candidates  74  are classified into, for example, three groups  101 ,  102 , and  103 . 
     Thereafter, in step S 75 , the area classifier  48  executes the clustering process in relation to position and/or scale for each of the groups that were classified in step S 73 . The clustering process can be executed in the same manner as in step S 7 , and thus explanations thereof are omitted. 
     Next, the area classifier  48  returns to step S 73  (step S 76 : NO), and thereafter, steps S 73  through S 75  are repeated sequentially. Additionally, in the case that all of the position groups (area candidate groups  78 ) have been selected and the clustering process thereon is completed (step S 76 : YES), then in step S 77 , the secondary clustering process is brought to an end. 
     As a result, the area classifier  48  determines the identification target areas  105 ,  106 ,  107  respectively for the groups  101  to  103  that were classified by the clustering process. With respect to the same second pedestrian  68 , the area classifier  48  is capable of determining, respectively, an identification target area  105  of a small scale, an identification target area  106  of an intermediate scale, and an identification target area  107  of a large scale. 
     In this manner, the identification target area determiner  40 , after having classified the plural area candidates  74  into at least one position group by performing the previous stage clustering process in relation to at least position, classifies the area candidates  74  belonging to the at least one position group into two or more groups depending on scale, and then determines the identification target areas  105  to  107  for the respective area candidates. 
     By using the first identifying unit  50  for which the computation amount thereof is relatively small, there is a tendency to extract area candidates  74  of similar scale at substantially the same position. Thus, by classifying the plural area candidates  74  into at least one position group, it is possible to exclude beforehand the existence of area candidates  74  which are similar in scale but are separated in position, whereby identification accuracy can be further enhanced. 
     &lt;Second Improvement&gt; 
     Next, a second improvement will be described with reference to  FIG. 10 . 
     As shown in  FIG. 10 , the area classifier  48  classifies the plural area candidates  74  into two or more groups depending on scale. In  FIG. 10 , the plural area candidates  74  are classified into an area candidate group  110 , which is an aggregate of the area candidates  74  having a scale that is greater than a predetermined threshold value, and an area candidate group  111 , which is an aggregate of the area candidates  74  having a scale that is equal to or less than the predetermined threshold value. Thereafter, the area classifier  48  determines identification target areas  112 ,  113  by performing the clustering process on each of the groups. More specifically, with respect to the same second pedestrian  68 , the area classifier  48  is capable of determining, respectively, an identification target area  113  of a small scale, and an identification target area  112  of a large scale. 
     In this manner, according to the second improvement, the clustering process may be performed only on the number of classified groups, whereby identification accuracy can be enhanced together with realizing a reduction in the amount of computation. 
     [Supplementary Features] 
     The present invention is not limited to the embodiment described above. The embodiment may be changed or modified without departing from the scope of the present invention. 
     For example, in the present embodiment, the aforementioned image processing sequence is carried out on the captured image Im, which is produced by the monocular camera (camera  14 ). However, the same advantages as those described above can be obtained by performing the image processing sequence on a captured image that is produced by a multiocular camera (stereo camera).