Patent Publication Number: US-8977006-B2

Title: Target recognition system and target recognition method executed by the target recognition system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This patent application is based on and claims priority pursuant to 35 U.S.C. §119 to Japanese Patent Application Nos. 2012-126416, filed on Jun. 1, 2012 and 2013-076963, filed on Apr. 2, 2013 in the Japan Patent Office, the entire disclosures of which are hereby incorporated by reference herein. 
     BACKGROUND 
     1. Technical Field 
     The present invention relates to a target recognition system to recognize a recognition target area indicating an oncoming car and pedestrian, and a target recognition method executed by the target recognition system. 
     2. Related Art 
     At present, recognition systems, installed in vehicles, which recognize obstacles based on a captured image of the area ahead of the vehicle are widely used for driver support systems such as adaptive cruise control (ACC), to reduce the burden on the driver. The driver support systems provide various functions, including a brake-control-and-alert function that alerts the driver to take corrective action to avoid a collision or reduce the impact of the collision, a driving speed adjustment function to maintain a safe minimum distance between vehicles, and a stray prevention function to prevent the vehicle from straying into another lane. 
     In JP-2008-146549-A, a driver support system that attempts to alleviate the burden on the driver of the vehicle by recognizing targets based on the image of scenery ahead of the vehicle captured by the imaging devices is disclosed. In order to recognize the targets shown in the captured image, the color and spatial frequency of the target are detected from the captured image. By integrating the detected spatial frequency for generating a distribution of the spatial frequency, the characteristics of the spatial frequency are detected. Then, the recognition target is recognized by comparing the detected target color and spatial frequency characteristics with predetermined target color and spatial frequency characteristics. 
     In order to implement the driver support system, it is necessary to recognize obstacles in the way of the vehicle accurately and immediately. However, in the above-proposed example, the driver support system also detects various targets other than the road, such as buildings, that are less likely to be obstacles. Since these buildings are not considered obstacles like pedestrians and vehicles, detecting targets other than those on the road comes to nothing. Accordingly, recognizing real obstacles on the road becomes slower. 
     In addition, the processing time required for detecting and integrating the spatial frequency depends on the memory processing ability and can be lengthy. As a result, the recognition processing to detect the real obstacle when the vehicle is traveling is delayed, which makes it difficult to implement the driver support system suitably. In addition, the above-described driver support system recognizes a false target that is less likely to function as the real obstacle of the recognition target in the background ahead. Therefore, the system may mistakenly recognize background targets as recognition targets. 
     SUMMARY 
     In one exemplary embodiment of the present disclosure, there is provided a target recognition system to recognize one or more recognition targets, operatively connected to a stereo imaging device to capture a stereo image of an area ahead of the target recognition system. The target recognition system includes a parallax calculator, a target candidate detector, and a target recognition processor. The parallax calculator calculates parallax of the stereo image including two captured images acquired by the stereo imaging device. The target candidate detector detects a candidate set of recognition target areas based on a luminance image of one of the captured images. The target recognition processor limits the candidate set of recognition target areas detected by the target candidate detector within a range of threshold values of characteristics in the candidate set of recognition target areas set in advance based on the parallax calculated by the parallax calculator to extract and output the one or more recognition targets. 
     In another aspect of the present disclosure, there is a provided a target recognition method executed by the target recognition system that calculates parallax of the stereo image including two captured images acquired by a stereo imaging device; detects a candidate set of recognition target areas based on a luminance image of one of the captured images; limits the candidate set of recognition target areas within a range of threshold values of characteristics in the candidate set of recognition target areas set in advance based on the parallax, and extracts and outputs the one or more recognition targets. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete appreciation of the disclosure and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein: 
         FIG. 1  is a schematic diagram illustrating an in-vehicle control system including a target recognition system, according to the present disclosure; 
         FIG. 2  is a diagram illustrating a configuration of an imaging unit and an image analysis unit shown in  FIG. 1 ; 
         FIG. 3  is a schematic expanded view illustrating optical filters shown in  FIG. 2  viewed from a direction orthogonal to an optical transmission direction; 
         FIG. 4  is a block diagram illustrating a configuration of the target recognition system; 
         FIG. 5  is a flow chart illustrating target recognition process executed by the target recognition system; 
         FIG. 6  is one example of a stereo image; 
         FIG. 7  is a fundamental view illustrating a range finding in the stereo camera shown in  FIG. 1 ; 
         FIG. 8  is the image including multiple rectangular blocks; 
         FIGS. 9A through 9D  illustrate examples of feature patterns in the rectangular block; 
         FIG. 10  is a diagram illustrating a configuration of the target recognition processor including hierarchies; 
         FIG. 11  is one example of a captured image including detected candidate set of recognition target areas; 
         FIG. 12  is one example of an image including detected candidate set of recognition target areas; 
         FIG. 13  is a table representing width minimum, width maxim, height minimum, and height maxim of the recognition target relative to parallax average value; 
         FIG. 14  is a flow chart illustrating another target recognition process executed by the target recognition system; and 
         FIG. 15  is a block diagram illustrating a hardware configuration of the stereo camera system. 
     
    
    
     DETAILED DESCRIPTION 
     In describing preferred embodiments illustrated in the drawings, specific terminology is employed for the sake of clarity. However, the disclosure of this patent specification is not intended to be limited to the specific terminology so selected, and it is to be understood that each specific element includes all technical equivalents that have the same function, operate in a similar manner, and achieve a similar result. Referring now to the drawings, wherein like reference numerals designate identical or corresponding parts throughout the several views thereof, and particularly to  FIGS. 1 through 15 , a target recognition system according to illustrative embodiments of the present disclosure is described. 
     Initially, a vehicle-mounted control system includes a target recognition system as in-vehicle system. It is to be noted that the target recognition system according to the present disclosure is not limited to an in-vehicle control system, and thus, for example, the target recognition system may be used for an object detection device that detects the object based on captured images. 
       FIG. 1  is a schematic diagram illustrating an in-vehicle control system  106  including a target recognition system  200  according to the present disclosure. The in-vehicle control system  106  controls the various devices in a vehicle  100  such a car in accordance with recognition of targets using the captured image of the road in front of the vehicle acquired by an imaging unit  101  installed in the vehicle  100 . 
     In  FIG. 1 , the in-vehicle control system  106  includes the imaging unit  101 , an image analysis unit  102 , and a vehicle drive control unit  104 . The imaging unit  101  is provided as a capture device to capture an image of the area in front of the vehicle  100  in the direction of travel. For example, the imaging unit  101  is provided near a rearview mirror near a windscreen  103  of the vehicle  100 . The various data, such as, captured data acquired by the imaging unit  101  is input to the image analysis unit  102  as an image processor. The image analysis unit  102  analyzes the data transmitted from the imaging unit  101 , calculates the position, the direction, and the distance of another vehicle in front of the vehicle  100 , and detects dividing lines as the lane borders. When another vehicle (leading vehicle, oncoming vehicle) is detected, the another vehicle is detected as the recognition target on the road based on the luminance image 
     In addition, the calculation result of the image analysis unit  102  is transmitted to the vehicle drive control unit  104 . The vehicle drive control unit  104  performs driving support control to report the alert and control the steering and brakes of the vehicle  100 , based on the detection of the recognition target such as another vehicle and pedestrian. The vehicle drive control unit  104  realizes various functions having a brake-control-and-alert function that the driver is alerted to take corrective action to avoid a collision or reduce the impact of the collision, and a driving speed adjustment function to maintain a safe minimum distance between vehicles by engaging a control device such as the brakes and the steering. 
       FIG. 2  is a diagram illustrating a configuration of the imaging unit  101  and the image analysis unit  102 . The imaging unit  101  is a stereo camera system that includes two cameras  110 A and  110 B, and the two cameras  110 A and  110 B have similar configuration. Respective cameras  110 A and  110 B include capturing lenses  111 A and  111 B, optical filters  112 A and  112 B, and image sensors  113 A and  113 B on which image pickup elements are two-dimensionally arranged. The imaging unit  101  outputs luminance data. 
     In addition, the imaging unit  101  includes a process hardware unit  120  constituted by a field programmable-gate array (FPGA). The process hardware unit  120  includes a parallax calculator  121  to calculate parallaxes in the corresponding portions between the captured images, for acquiring the parallax data based on the luminance image data output from the respective imaging units  110 A and  110 B. Herein, when one captured image acquired by one of the imaging devices  110 A and  110 B is a reference image and the other captured image acquired by the other of the imaging devices  110 A and  110 B is a comparison image, the parallax for a certain area is calculated as position deviation in the certain image area in the comparison image correlated to the certain image area in the reference image. Using fundamental of triangulation, the distance from the stereo camera system to the same object corresponding to the certain image area in the captured image areas can be calculated based on the calculated parallax. 
     The image analysis unit  102  includes a memory  130  and a micro processing unit (MPU)  140 . The memory  130  stores luminance image data and parallax image data output from the imaging unit  101 . The MPU  140  performs recognition processing to recognize targets and controls the parallax calculation, using the luminance image data, and parallax image data stored in the memory  130 . 
       FIG. 3  is a schematic expanded view illustrating the optical filters  112 A and  112 B and the image sensors  113 A and  113 B viewed from a direction orthogonal to an optical transmission direction. Each of the image sensors  113 A and  113 B is constituted by, such as, charge coupled device (CCD) and Complementary Metal Oxide Semiconductor (CMOS), and the imaging element (light-receiving element) is formed by photodiodes  113   a . The photodiodes  113   a  are two dimensionally arranged for each of the imaging pixels in the image sensors  113 A and  113 B. In order to improve the focus efficiency, a micro lens  113   b  is provided on the incoming side of the photodiodes  113   a . By connecting the image sensors  113 A and  113 B by printed wiring board (PWB) bonded by wire boding method, sensor substrates  114 A and  114 B are formed. 
     Next, a recognition process according to a feature of the present disclosure is described below.  FIG. 4  is a block diagram illustrating a configuration of a target recognition system  200 . In  FIG. 4 , the target recognition system  200  includes a stereo image input unit  201 , a luminance image input unit  202 , a parallax image calculator  203 , a recognition target candidate detector  204 , a recognition candidate dictionary  205 , a target recognition processor  206 , and a target recognition result output unit  207 . The stereo image input unit  201  receives the stereo image from the stereo camera including imaging units  110 A and  110 B containing imaging lenses and image sensors positioned on both sides of the stereo camera. 
     The luminance image input unit  202  receives the luminance image of right image or left image of the stereo image from the stereo image input unit  201 . The input stereo image and the luminance image are stored in the memory  130  in the stereo camera system. The parallax image calculator  203  calculates the parallax (parallax image) of the captured targets that is the difference of the focusing position between the right image and left image. The recognition target candidate area detector  204  detects a candidate set of recognition target areas presenting forward corners of the captured image showing the area ahead of the vehicle  100 . The recognition candidate dictionary  205  is generated by a machine learning method, such as Support Vector Machine (SVM) method, using the image sample learning data of the target recognition in advance. The recognition candidate dictionary  205  is formed for each of recognition targets. The target recognition processor  206  performs the recognition processing. The target recognition processor  206  uses the recognition candidate dictionary  205  to recognize the recognition targets. The target detection result output unit  207  outputs the recognition result of the recognition targets. 
     Herein, the recognition process in the target recognition processor  206  is described below. Initially, the target recognition processor  206  calculates a parallax average of the candidate set of recognition target areas. The parallax average is the value obtained by adding the parallaxes at certain pixels and then dividing the sum, or obtained by using a central value (median) filter in the candidate set of recognition target areas. Alternatively, the parallax may be set to the highest frequency value within the recognition target candidate areas. The target recognition processor  206  calculates the distance between the candidate set of recognition target areas and the stereo camera system  101  based on the parallax average. The target recognition processor  206  calculates the size of the real candidate set of recognition target areas using the distance from the stereo camera system  101  and sizes of the recognition target candidate areas on the captured image. 
     When horizontal, vertical sizes of the candidate set of recognition target areas exceed a size threshold range setting as the predetermined recognition targets, the recognition is considered as falsely recognition, and the excess candidate set of recognition target areas are eliminated. Then, the recognition target contained within the size threshold range of the candidate set of recognition target areas is output as the target recognition result. 
     With these processes, the accuracy of the candidate set of recognition target areas can be improved. Herein, the size threshold range is saved in the memory  130  of the stereo camera  101 . 
     Next, operation flow of the target recognition system  200  is described below with reference to  FIG. 5 .  FIG. 5  is a flow chart illustrating target recognition process executed by the target recognition system  200 . At step S 101 , the stereo image is input to the stereo image input unit  201  (see  FIG. 4 ). More specifically, the stereo image is input from stereo camera  201 .  FIG. 6  is one example of a stereo image. The same subject in the stereo image is positioned at different imaging positions in the right image and the left image. 
     Then, the luminance image input unit  202  outputs luminance image of left image or right image at step S 102 . The input stereo image and the luminance image are saved in the memory  516  in the stereo camera  102 . 
     The parallax image calculator  203  calculates the parallax that is a difference between the image forming positions of the left image and right image in the object of the stereo image, using the stereo image input from the stereo image input unit  201  at step S 103 . More specifically, the parallax image where the parallax is treated as the pixel value is calculated based on the same areas between the left image and the right image formed by the left image lens  111 A and right image lens  111 B, using block matching method. 
     The block matching method is the method to divide the left image and the right image into multiple blocks and calculate the parallax based on the blocks where the degree of similarity between the left image and the right image is largest in the blocks and blocks between the left image and the right image is matched. For example, the image having 1280×960 pixels is divided into 5×5 pixel-size blocks. Then, the parallax is calculated using the respective blocks. The optimum values of the block sizes are set by adjusting through the experiment. 
     In one example illustrated in  FIG. 7 , Δ 1  and Δ 2  represent distances from image center positions to actual image positions in the right image and the left image showing a point O in the capturing target. Accordingly, the parallax Δ can be obtained by adding the distance Δ 1  and the distance Δ 2  (Δ=Δ 1 +Δ 2 ). The parallax image has parallaxes in the corresponding pixel positions. 
     The recognition target candidate area detector  204  detects a candidate set of recognition target areas such as vehicle and pedestrian, using the luminance image input from the luminance image input unit  202  at step S 104 . The target recognition processor  206  calculates the parallax average of the candidate set of recognition target areas recognized from the luminance image at step S 105 . The target recognition processor  206  calculates the distance between the candidate set of recognition target areas and the stereo camera from the parallax average at step S 106 . The target recognition processor  206  calculates the actual size of the candidate set of recognition target areas r using the distance from the stereo camera and the size of the candidate set of recognition target areas on the image at step S 107 . 
     The target recognition processor  206  compares the actual size of the candidate set of recognition target areas with the size threshold range corresponding to the candidate set of recognition target areas at step S 108 . When the actual size of the candidate set of recognition target areas is positioned within the size threshold range (Yes at step S 108 ), the target recognition processor  206  executes the recognition processing using the luminance image, and output the target recognition results at step S 109 . When the actual size of the candidate set of recognition target areas positioned outside of the size threshold range (No at step S 108 ), the target recognition processor  206  executes the recognition processing to eliminate the excess image, and recognize the excess candidate set of recognition target areas as the false detection at step S 110 . 
     Herein, the detail recognition process to recognize the recognition target according to the present disclosure is described below. Initially, in order to recognize the recognition target, as illustrated in  FIG. 8 , rectangular or square blocks are set associated with target images in the captured image. The position and the size of the rectangular block in the captured image are determined by an upper left coordinate (Xs, Ys) and a lower right coordinate (Xe, Ye) of the rectangular. Then, using a large rectangular block  1 , the captured images is scanned, and the target image is extracted so that the size of the rectangular block  1  almost matches the size of the target, and the rectangular blocks  1  is set for the extracted target images. 
     After setting the rectangular block  1 , using a small rectangular block  2 , the captured images is scanned, and then, the target image is extracted so that the size of the rectangular block  2  almost matches the size of the small target, and the rectangular block  2  is set for the extracted small target image. Accordingly, the rectangular blocks are set for the corresponding target images. The rectangular block is associated to the candidate set of recognition target areas. 
     Then, the target recognition processor  206  recognizes the recognition targets, using the recognition candidate dictionary  205 . Herein, the recognition candidate dictionary  205  for recognizing the aimed recognition target is described below. 
     As illustrated in  FIGS. 9A through 9D , the target recognition processor  206  calculates feature amount in a rectangular block of an evaluation target, based on a rectangular range  401  constituted by only white pixels and a rectangular range  402  constituted by only black pixels, represented by a shaded portion contained in a rectangular block  300  shown in  FIG. 9A . The target recognition processor  206  calculates a difference between the pixels in the evaluation target rectangular block and the white pixels of the rectangular range  401  and between the pixels in the evaluation target rectangular block and the black pixel value of the rectangular block  402 , and therefore, the difference in the sums are set as the feature amount h(x) in the rectangular block  300 . 
     The feature patterns A, B, C, and D shown in  FIGS. 9A through 9D  almost fully cover features of ant targets. In the feature pattern A, the rectangular range  301  and the rectangular range  302  are positioned adjacent from side to side, and both ranges are positioned upper left from the center of the rectangular block  300 . In the feature pattern B, the rectangular range  301  and the rectangular range  302  are positioned adjacent up and down, and both ranges are positioned upper left from the center of the rectangular block  300 . In the feature pattern C, the rectangular range  302  is sandwiched between two rectangular ranges  301 , and both ranges are positioned upper from the center of the rectangular block  300 . In the feature pattern D, the rectangular range  301  and the rectangular range  302  are positioned diagonally, and both ranges are positioned left side from the center of the rectangular block  300 . 
     Then, using the evaluation function like that shown in the formula 1, evaluation weight values f(x) in the candidate sets of recognition target areas are calculated based on an evaluation function like that shown in the formula 1. By calculating the feature amount h t (x) in the entire pixels T in the rectangular blocks t ( 1  to T) (T; the number of patterns used for evaluation), the weight evaluation value f(x) is calculated by integrating weight coefficients α t  attached to each of the rectangular blocks. 
     Herein, the feature amount h t (x) and the weight coefficient α t  are obtained by collecting learning data for the image of the recognition target and by leveraging the learning data. By calculating the weight evaluation values for the above-described feature patterns A through D, the recognition candidate dictionary  205  saves pairs of the feature pattern and weight coefficient based on the calculated weight evaluation values 
     
       
         
           
             
               
                 
                   
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     Herein, the target recognition processor  206  includes multiple hierarchies  400 - 1  through  400 - n  (n; natural integer number). In the respective hierarchies  400 - 1  through  400 - n , the target is evaluated using the weight evaluation values of the evaluation function represented by the formula (1). 
     In the respective hierarchies  400 - 1  through  400 - n , using a unique feature pattern for each of recognition targets or multiple feature patterns for each of recognition targets and weight coefficients associated to the feature pattern(s), the evaluation is performed based on the formula (1). In general, the huaraches vary from  400 - 1  to  400 - n , the number of used patterns is gradually increased. Accordingly, recognition speed can becomes faster. For example, when the rectangular block that is smaller than the threshold range set in advance, in the hierarchy  400 - 1 , the aimed recognition target is not present in the rectangular block is determined. Then, without evaluating the rectangular block, the block is handled as a non-target block  400 - 1 . Similar determination is performed for respective hierarchies  400 - 2  to  400 - n  (n: natural integer). The rectangular block, in which the recognition target in the final hierarchy  400 - n  is not the recognition target, is not determined as the rectangular block in which the image of the aimed recognition target is present. 
     When the feature amount is calculated, the recognition weight is additionally multiplied. Therefore, when the recognition weight in the road area is set to 1 and the recognition weight in the other area is set to 0; that is, when the weight coefficient of the entire area other than the road area is 0, the recognition process is performed for the road area and is not performed for the areas other than the road area. Alternatively, the weight coefficient corresponding to the recognition weight is set at decimal, and the different weight coefficients may be set for the respective areas. In this case, even when the aimed recognition target is present in the respective area whose weight coefficient is set at a small value and the area whose weight coefficient is set at a larger value, the weight evaluation value in the area whose weight coefficient is small is small, that is not the image of the recognition target is determined. 
     Accordingly, the recognition results of the recognition target in the area whose weight coefficient is small is bad, and the image of the target similar to the image of the recognition target can be eliminated, thereby reducing the generation of false recognition. 
     Herein, the feature amount, the weight coefficient, and the evaluation threshold value for calculating the evaluation values of the respective hierarchies in the target recognition processor  206  are acquired by using a leaned image that classifies recognition targets and un-recognition targets.  FIGS. 11 and 12  illustrate the detected candidate set of recognition target areas in the image. In  FIGS. 11 and 12 , the areas enclosed by bold lines represent the candidate set of recognition target areas 
     Then, the target recognition processor  206  eliminates the falsely detected candidate set of recognition target areas from the candidate set of recognition target areas detected by the recognition target candidate area detector  204 , using the parallax image containing the parallaxes calculated by the parallax image calculator  203 . At this time, the target recognition processor  206  calculates the parallax average of the candidate set of recognition target areas recognized from the luminance image. 
     Then, the target recognition processor  206  calculates the distance of the candidate set of recognition target areas from the stereo camera based on the parallax average. More specifically, the relation between a parallax average Δ and a distance Z of the candidate set of recognition target areas from the stereo camera are represented by the following formula 2 (see  FIG. 7 ).
 
Δ: f=D:Z   (2)
 
     In the formula 2, f represents a focal length between the two imaging lenses  111 A and  111 B in the stereo camera system, D represent a difference between the two camases. Therefore, the distance Z of the candidate set of recognition target areas from the stereo camera can be calculated by the following formula 3.
 
 Z=D×f/Δ   (3)
 
     After the distance Z of the candidate set of recognition target areas from the stereo camera is calculated, an actual size of the candidate set of recognition target areas can be calculated. Herein, the relation between a virtual size s of the candidate set of recognition target areas in the captured image and the actual size S of the candidate set of recognition target areas is represented by the following formula 4.
 
 S:Z=s:f   (4)
 
Therefore, the actual size S of the candidate set of recognition target areas can be calculated by the following formula 5.
 
 S=s×Z/f   (5)
 
     By comparing the size recognition range corresponding to the candidate set of recognition target areas, and the actual size S of the candidate set of recognition target areas acquired by the formula 5, the falsely detected candidate set of recognition target areas can be eliminated from list of the candidate set of recognition target areas. 
     For example, width minimum Wmin, width maxim Wmax, height minimum Hmin, and height maxim Hmax of the candidate set of recognition target areas can be set as following values. When the recognition target is pedestrian, the size threshold range can be set at a range whose height minimum Hmin is 0.5 m, height maxim Hmax is 2.5 m, width minimum Wmin is 0.1 m, and width maxim Wmax is 1.0 m. When the recognition target is the car an, the size threshold range can be set at a range whose height minimum Hmin is 1.0 m, height maxim Hmax is 5.0 m, width minimum Wmin is 4.0 m, and width maxim Wmax is 3.0 m. Then, whether or not the candidate set of recognition target areas is falsely detected or not is determined based on the range of the Wmin, Wmax, Hmin, and Hmax. That is, the width Ws and the height Hs of the candidate set of recognition target areas can be set within the range represented by the formulas 6 and 7.
 
 W min&lt; Ws&lt;W max  (6)
 
 H min&lt; Hs&lt;H max  (7)
 
     The values of the Wmin, Wmax, Hmin, and Hmax of the respective candidate set of recognition target areas can be determined by actually collected experiment data of the recognition target. 
     In this configuration, when the detected candidate set of recognition target areas is positioned either excess width area (6) or excess height area (7), the recognition processor  206  detects that the candidate set of recognition target areas is falsely detected. Alternatively, when the detected candidate set of recognition target areas is positioned the excess width area (6) and the excess height area (7), the recognition processor  206  detects that the candidate set of recognition target areas is falsely detected. 
     Using theses calculation, the rectangular blocks B, D, and F shown in  FIG. 12  is detected as the false result of the candidate set of recognition target areas to be eliminated. 
       FIG. 14  is a flow chart illustrating another target recognition process executed by the target recognition system  200 . The steps S 201  through S 207  in the target recognition operation shown in  FIG. 14  are similar to the steps S 101  through S 107  shown in  FIG. 5 . 
     What is different from  FIG. 5  is described below. At step S 208 , the target recognition processor  206  compares the size threshold range corresponding to the candidate set of recognition target areas with the actual size of the candidate set of recognition target areas. When the actual size of the candidate set of recognition target areas is equal to or smaller than the size threshold range (Yes at step S 208 ), the target recognition processor  206  performs the recognition process using the luminance image, and outputs the target recognition results at step S 209  and S 210 . 
     Conversely, when the actual size of the candidate set of recognition target areas is larger than the size threshold range (No at step S 208 ), the target recognition processor  206  determines that the detected target is not the recognition target without performing the recognition process and process is revisited to the parallax calculation process step S 204 , and detects the candidate set of recognition target areas again at the following steps. With these processing, the false recognition can be further reduced. 
     A hardware configuration of the in-vehicle stereo camera imaging device to recognize the recognition target is described below with reference to  FIG. 15 .  FIG. 15  is a block diagram illustrating one example of a hardware configuration of the stereo camera. In  FIG. 15 , a light reflected from the object is input to Complementary Metal Oxide Semiconductors (CMOS)  503  and  504  corresponding to the image sensors  112 A and  112 B (see  FIG. 4 ) through the both imaging lenses  501  and  502  ( 112 A and  112 B) in the stereo camera. The CMOS  503  and  504  convert the optical image formed on the captured image into electrical signals for outputting as analog image data. Then, signal processor  114 A and  114 B include correlated double sampling (CDS) circuits  505  and  506 , analog-digital (A/D) converters  507  and  508 , and image processing circuits  509  and  510 . Each of the process hardware unit  120  and the MPU  140  includes a central processing unit (CPU)  511 , a synchronous dynamic random access memory (DRAM)  512 , a compand (compress-expand) circuit  513 , a read only memory (ROM)  516 , random access memory (RAM)  517 , and a timing signal generator circuit  518 . Noise of the analog image data output from the CMOS  503  and  504  is removed by the CDS circuits  505  and  506 , and the noise-removed image data is converted into digital signal, by the A/D converters  507  and  508 , for outputting to the image processing circuits  509  and  510 . 
     Using the SDRAM  512  that temporarily saves the image data, the image processing circuits  509  and  510  performs various image processing, such as luminance-hue (YcrCb) conversion, white balance control processing, contrast correction processing, and edge emphasis processing. In the processes of image processing, shade of the image information is adjusted in the white balance processing, contrast of the image information is adjusted in the contrast correction processing, sharpness of the image information is adjusted in the edge emphasis processing, and color of the image information is adjusted in the color conversion processing. 
     In addition, the image information in which the signal process and image process is executed is memorized in the memory card  514  via the compand circuit  513 . The compand circuit  513  compresses the image information output from the image processing circuits  509  and  510  and expands the image information read from the memory card  514 , to output the compressed and expanded information to the image processing circuit. The timings of the CMOS  503  and  504 , the CDS circuits  505  and  506 , and the A/D converters  507  and  508  are controlled by the CPU  511  via the timing signal generator  518  that generates the timing signal. Furthermore, the CPU  511  further controls the image processing circuits  509  and  510 , the compand circuit  513 , and the memory card  514 . 
     In the stereo camera system, the CPU  511  performs various calculations depending on the target recognition program. The CPU  511  installs ROM  516  dedicated for storing the image processing program and RAM  517  that is a readably memory including a work area used for the various processes and various data storage area. The ROM  516  and RAM  517  are connected by a bus line  519 . With this configuration, the stereo camera is constituted by module configuration including the process function to perform parallax calculation executed in the in-vehicle stereo camera system, to detect the candidate set of recognition target areas using the luminance image, perform the target recognition function to eliminate the false detection of the candidate set of recognition target areas As the actual hardware configuration, the CPU  511  reads out the image processing program from the ROM  516 , and the respective process are road on the main memory and the target recognition result is output. 
     Herein, the present invention of the present disclosure can provide, in addition to the target recognition system and the target recognition method described above, a computer readable program for executing the method. The computer program to be executed by the target recognition system according to the present embodiment can be provided by being recorded in a computer-readable recording medium such as a CD-ROM, an FD, a CD-R, and a DVD as a file in an installable format or an executable format. 
     Alternatively, the computer program to be executed by the target recognition system according to the present embodiment can be provided by being downloaded by a predetermined transmission device over a transmission medium, such as telephone line, dedicated network, Internet, and other communication systems. Herein, while transmitting the program, at least a part of computer program should be transmitted through the transmission medium. That is, not all of data constituting the computer program should be present in the communication medium (signal) at one time. The communication medium (signal) is implemented by a carrier wave of a computer data signal containing the computer program. A communication method to transmit the computer program from the predetermined transmission device may contain a continuous transmission process to transmit the data constituting the computer program and an intermittent transmission process. 
     Numerous additional modifications and variations are possible in light of the above teachings. It is therefore to be understood that, within the scope of the appended claims, the disclosure of this patent specification may be practiced otherwise than as specifically described herein.