Abstract:
In order to provide an object sensing device whereby, among limited computation resources, performance is improved in sensing an object when sense processing a plurality of objects to be sensed, an object sensing device includes image capture units which capture images external to a host vehicle, and a processing device which sense processes objects to be sensed from the images which are captured by the image capture units, said processing device further including: a scene analysis unit which analyzes a travel scene of the host vehicle; a process priority change unit which changes a sensing process priority of the object to be sensed on the basis of the travel scene which is analyzed by the scene analysis unit; and an object to be sensed sensing unit which carries out a sensing of the object to be sensed on the basis of the sensing process priority which is changed by the process priority change unit.

Description:
TECHNICAL FIELD 
     The present invention relates to an object sensing device that detects objects in a vehicle&#39;s surroundings from image information outside of the vehicle. 
     BACKGROUND ART 
     In order to realize safe travel of a vehicle, research and development is underway regarding devices that detect dangerous phenomena around a vehicle and automatically control the steering, accelerator, and brakes of the vehicle to avoid any dangerous phenomena that has been detected, and such devices have already been installed in some vehicles. Among such devices, a system that senses a pedestrian crossing in front of the vehicle with a sensor installed in the vehicle and warns the driver or automatically applies the brakes if there is a possibility of colliding with the pedestrian is effective in terms of enhancing the vehicle safety. 
     A camera or radar and a processing device that processes signals therefrom are used to sense a pedestrian in front of the vehicle with a sensor installed in the vehicle. In order to improve the sensing performance thereof, it is necessary to execute more detailed processes in the processing device. However, the computation resources of such a processing device are limited, and the processing device must simultaneously process other objects to be sensed in addition to the process for sensing a pedestrian. Thus, it is necessary to assign a priority to the processes and intensively execute the calculation processes. In order to achieve this, PTL 1 discloses one effective means for intensively executing processes in a scene in which there is a high possibility that a pedestrian exists, and PTL 1 further discloses an existence probability indicating the possibility that a pedestrian exists. 
     CITATION LIST 
     Patent Literature 
     PTL 1: JP 2010-3254 A 
     SUMMARY OF INVENTION 
     Technical Problem 
     PTL 1 discloses finding an existence probability that a pedestrian is likely to move after a pedestrian has been detected, but does not disclose improving the performance itself of detecting a pedestrian. Therefore, in order to improve the pedestrian sensing performance itself, the pedestrian existence probability must be calculated before sensing a pedestrian to determine whether to intensively process the pedestrian. 
     An object of the present invention is to provide an object sensing device that improves the sensing performance of an object when processing to sense a plurality of objects to be sensed given limited computation resources. 
     Solution to Problem 
     To achieve the above object, an object sensing device of the present invention includes: an image capture unit that captures surroundings of a host vehicle; and a processing device that executes a sensing process of an object to be sensed from an image captured by the image capture unit, wherein the processing device includes: a scene analysis unit that analyzes a travel scene of the host vehicle; a process priority change unit that changes a sensing process priority of the object to be sensed based on the travel scene analyzed by the scene analysis unit; and an object-to-be-sensed sensing unit that senses the object to be sensed based on the sensing process priority changed by the process priority change unit. 
     Advantageous Effects of Invention 
     According to the invention, an object sensing device that improves the sensing performance of an object when processing to sense a plurality of objects to be sensed given limited computation resources can be provided. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  illustrates an example of the constitution of an object sensing device according to the present invention. 
         FIG. 2  illustrates a processing flow in a scene analysis unit of the object sensing device of the present invention. 
         FIG. 3  schematically illustrates a road region extraction process of the object sensing device of the present invention. 
         FIG. 4  schematically illustrates a scene analysis diagram of the object sensing device of the present invention. 
         FIG. 5  illustrates an example of learned data for calculating an existence probability in the present invention. 
         FIG. 6  schematically explains the existence probability calculation in the present invention. 
         FIG. 7  schematically explains a process priority change unit of the object sensing device of the present invention. 
         FIG. 8  illustrates a processing flow in a parameter changing unit of the object sensing device of the present invention. 
         FIG. 9  illustrates a processing flow in a distance calculation unit of the object sensing device of the present invention. 
         FIG. 10  explains corresponding points of left and right images of the object sensing device of the present invention. 
         FIG. 11  explains how to find the corresponding points of the left and right images of the object sensing device of the present invention. 
         FIG. 12  explains a method for distance calculation of the object sensing device of the present invention. 
         FIG. 13  illustrates another example of the constitution of the object sensing device according to the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Embodiments of the present invention are hereinafter described with reference to the drawings. 
     Embodiment 1 
     An embodiment of a stereo camera, which is an object sensing device, of the present invention will be explained below. Specifically, an embodiment of an object sensing device that senses a pedestrian using images of a stereo camera installed in a vehicle will be explained. 
     First, an overview of the object sensing device of the present invention will be explained using  FIG. 1 . 
       FIG. 1  is a block diagram realizing the object sensing device of the present invention. The object sensing device includes a stereo camera  100 , a left image capture unit  101  of the stereo camera, and a right image capture unit  102  of the stereo camera. The left image capture unit  101  and the right image capture unit  102  capture images of the front of the vehicle in which the stereo camera is installed. The captured images are processed in a processing device  111 . 
     The processing device  111  will now be explained in detail below. 
     An image from the right image capture unit  102  is input into a scene analysis unit  103 , and the scene analysis unit  103  analyzes the scene regarding what is captured in the image. The following explanation will focus on the processing of an image from the right image capture unit  102 , but an image from the left image capture unit  101  may also be processed in this way. 
     Next, in an external information acquisition unit  104 , information for calculating an existence probability of an object to be detected (pedestrian) is input from an external device such as a car navigation device installed in the vehicle. 
     Next, in an existence probability calculation unit  105 , an existence probability of an object to be detected (pedestrian) in the image captured by the right image capture unit  102  is calculated based on a scene of a subsequent image acquired in the scene analysis unit  103  and the information for calculating the existence probability acquired in the external information acquisition unit  104 . 
     Next, if the pedestrian existence probability is higher than a predetermined value, or for a portion in the image in which the pedestrian existence probability is higher than a predetermined value, a process priority change unit  106  changes a process priority so that the pedestrian sensing process is executed with priority over other objects to be detected (a preceding vehicle, a sign, a lane, etc.). 
     In a parameter changing unit  107 , the sensing process parameters are changed so that the pedestrian sensing process in a portion in which the pedestrian existence probability is high is executed in more detail. In a scene in which the pedestrian existence probability is high, the exposure control parameters of the camera (right image capture unit  102 ) are changed to make adjustments so as to acquire an image in which a pedestrian can be easily sensed. Further, the image processing parameters of a portion in which the pedestrian existence probability is high within the image acquired by the right image capture unit  102  are changed to produce an image in which a pedestrian can be easily detected. 
     In a vehicle speed determination unit  108 , in a scene in which the pedestrian existence probability is higher than a predetermined value, a command for executing speed control by suppressing acceleration of the vehicle is generated and output to a vehicle speed control device. 
     Meanwhile, in a distance calculation unit  103 , an image captured by the left image capture unit  101  and an image captured by the right image capture unit  102  are input, and a distance to an object is calculated from a deviation in the images between the same object captured by the left image capture unit  101  and the right image capture unit  102 . In an object-to-be-sensed sensing unit  110 , a process is executed to sense an object to be sensed (pedestrian) using the prior image from the right image capture unit  102  and the distance information to the object calculated in the distance calculation unit  109 . Therein, the sensing process is executed based on the priority that was previously changed by the process priority change unit  106 , and the sensing process is executed using the parameters changed in the parameter changing unit  107 . 
     Next, the processes executed in the scene analysis unit  103  of the stereo camera  100 , which is the object sensing device, will be explained. 
       FIG. 2  is a processing flow that is executed in the scene analysis unit  103  of the stereo camera  100 . 
     First, in a left-right image acquisition process  201 , images of the front of the vehicle captured by the left image capture unit  101  and the right image capture unit  102  of the stereo camera  100  are acquired. Next, in a distance data acquisition process  202 , data regarding the distance information of the images capturing the front of the vehicle that was calculated in the distance calculation unit  109  of the stereo camera  100  is acquired. The details of the distance calculation unit  109  will be explained later. 
     Next, in a road region extraction process  203 , a road region in the image is extracted using the two images of the front of the vehicle captured by the left image capture unit  101  and the right image capture unit  102  that were acquired in the left-right image acquisition process  201 . A road region is the portion outlined by the dotted line (road region  301 ) in the image capturing the front of the vehicle (processing image  300  in  FIG. 3 ), excluding other vehicles (parked vehicle  302 ), structures outside the road (guard rail or shrubbery  303 ) or (sidewalk without guard rail  304 ), etc., and is a region in which the vehicle can travel. 
     The road region  301  can be extracted from the two images captured by the stereo camera  100  by the method disclosed in JP 2005-217883 A. 
     Next, in a parked vehicle detection process  204 , a parked vehicle  302  is detected from the processing image  300  capturing the front of the vehicle. In order to detect the parked vehicle  302  from the processing image  300 , first, the size of three-dimensional objects that exist is calculated in regions outside of the road region  301  previously extracted in the road region extraction process  203  using the distance information previously acquired in the distance data acquisition process  202 . 
     Herein, the distance information is the distance from the stereo camera  100  (vehicle) of objects captured in each pixel of the processing image  300 . From this distance information, for example, a vehicle height  305 , a vehicle width  306 , and a vehicle depth  307  of the parked vehicle  302  in  FIG. 3  can be calculated. 
     Next, among the three-dimensional objects whose size was calculated, those having height, width, and depth values near those of a vehicle are extracted. With regard to the height, width, and depth values of a vehicle, the value ranges of height, width, and depth of vehicles in the market are investigated in advance, and if the height, width, and depth values of a three-dimensional object are within these ranges, then the object is deemed to have a size equivalent to that of a vehicle. 
     Next, it is determined whether a side surface (vehicle side surface  308  in  FIG. 3 ) of the three-dimensional object equivalent to a vehicle extracted previously has a texture similar to that of a vehicle. In this determination method, the textures of side surfaces of vehicles in the market are learned in advance, and it is determined whether the vehicle side surface in the processing image  300  and this learned data are similar. If it is determined that the three-dimensional object is a vehicle as a result of the vehicle side surface determination, it is then determined whether the vehicle is stopped. 
     In order to determine whether the vehicle is stopped, the processes indicated in the processing flow of  FIG. 2  are similarly executed for the images of the previous frame and the frame before the previous frame, and a movement trajectory is calculated regarding where the same vehicle detected in the frame before the previous frame and the previous frame has moved in the image. 
     At this time, in determining whether the same vehicle exists in the frame before the previous frame, the previous frame, and the current frame, the vehicle side surface textures in each frame are compared using the vehicle side surface texture used when previously determining whether the three-dimensional object is a vehicle, and it is determined to be the same vehicle if the similarly of the side surface textures is high. Finally, the movement trajectory in the frame before the previous frame, the previous frame, and the current frame of the vehicle in the image calculated previously is compared to the speed of the host vehicle, and it is determined that the vehicle in the image is stopped if the movement of the background of the processing image  300  estimated from the speed of the host vehicle matches the movement of the trajectory of the vehicle in the image. 
     By the above-described processes, the parked vehicle  302  can be detected from the processing image  300 . 
     Next, in a road side condition determination process  205 , the attributes of the road shoulders outside of the road region  301  previously extracted in the road region extraction process  203  besides the portion of the parked vehicle  302  previously detected in the parked vehicle detection process  204  are determined. The attributes include the guard rail or shrubbery  303 , a building  309 , and the sidewalk without guard rail  304 . Herein, in determining whether an object is a guard rail or shrubbery  303 , the size of three-dimensional objects that exist is calculated using the distance information previously acquired in the distance data acquisition process  202  in regions outside of the road region  301  previously extracted in the road region extraction process  203  besides the portion of the parked vehicle  302  previously detected in the parked vehicle detection process  204 . 
     Herein, the distance information is the distance from the stereo camera  100  (vehicle) of objects captured in each pixel of the processing image  300 . From this distance information, the height of the three-dimensional objects is estimated. As a result, if the height of a three-dimensional object is within a certain fixed value, it is determined that the three-dimensional object is a guard rail or shrubbery. The certain fixed value is prepared as learned data by learning data regarding a typical guard rail and shrubbery in advance. 
     In determining whether an object is a building  309 , the size of three-dimensional objects that exist is calculated using the distance information previously acquired in the distance data acquisition process  202  in regions outside of the road region  301  previously extracted in the road region extraction process  203  besides the portion of the parked vehicle  302  previously detected in the parked vehicle detection process  204  and the portion determined to be a guard rail or shrubbery  303  in the road side condition determination process  205 . As a result, if the height of a three-dimensional object is equal to or greater than a certain fixed value, it is determined that the three-dimensional object is a building. The certain fixed value is prepared as learned data by learning data regarding the height of a typical building in advance. 
     In determining whether there is a sidewalk with no guard rail  304 , first image processing is executed outside of the road region  301  to extract a road border line  310  (solid white line). The road border line can be detected by the method disclosed in JP 2012-155399 A. If no stationary three-dimensional objects exist between the road border line  310  that was detected and the portion that was determined to be the building  309  in the road side condition determination process  205 , then it is determined that the sidewalk with no guard rail  304  exists. In determining whether a three-dimensional object is a stationary three-dimensional object, the trajectory of the target three-dimensional object in the frame before the previous frame, the previous frame, and the current frame is calculated, and if this trajectory matches the movement of the background of the processing image  300  estimated from the speed of the host vehicle, the three-dimensional object is determined to be a stationary three-dimensional object. 
     Next, in a crosswalk detection process  206 , it is determined whether there are road surface markings of a crosswalk within the road region  301  previously extracted in the road region extraction process  203 . A crosswalk can be detected from within the road region  301  by the method disclosed in JP 2011-192071 A, etc. 
     Finally, in a scene analysis diagram production process  207 , a scene analysis diagram of the road region  301  as shown in  FIG. 3  is produced. A scene analysis diagram is a diagram that describes what kind of objects exist in which regions within an image as shown in  FIG. 4  based on the results extracted in the parked vehicle detection process  204 , the road side condition determination process  205 , and the crosswalk detection process  206  described above. 
       FIG. 4  illustrates guard rail or shrubbery regions  401  and  402 , a gap region  403  between a guard rail or shrubbery, a crosswalk region  404 , sidewalk regions without a guard rail  405  and  406 , parked vehicle regions  407  and  408 , and a gap region  409  between parked vehicles. 
     Next, the processes executed in the external information acquisition unit  104  of the stereo camera  100  will be explained. 
     Herein, external information is a car navigation device installed in the vehicle or a device outside the vehicle such as a sensor or other vehicle. A device outside the vehicle acquires information by a road-to-vehicle communication device called DSRC (Dedicated Short Range Communication), a mobile telephone, or a wireless LAN. 
     Herein, an example of acquiring information from a car navigation device will be explained. Attributes of a place where the host vehicle is traveling are delivered to the stereo camera  100  from the car navigation device. 
     Herein, the attributes of a place where the host vehicle is traveling are the attributes of urban area, residential area, commercial facility, school, road with few vehicles, and place where the density of intersection is high, which are places where the probability that a pedestrian exists is high, and conversely, the attributes of highway, elevated road, road with many vehicles, place with few buildings, mountainous area, and road with few intersections, which are places where the probability that a pedestrian exists is low. 
     The car navigation device specifies the location of the host vehicle on map data within the car navigation device based on GPS (Global Positioning System) position data, and transmits the above-described place attributes regarding the probability of excessive pedestrians around the host vehicle to the stereo camera  100 . 
     Next, the processing in the existence probability calculation unit  105  of the stereo camera  100  will be explained in detail. 
     In the existence probability calculation unit  105 , an existence probability regarding whether the possibility that a pedestrian exists in the image captured by the right image capture unit  102  is high or low is calculated based on the image scene acquired in the scene analysis unit  103  and the information regarding the attributes of the place where the host vehicle is traveling acquired in the external information acquisition unit  104  as described above. 
     Herein, in calculating the existence probability, learned data as shown in  FIG. 5  is prepared based on the results of investigations conducted in advance, and the pedestrian existence probability is calculated by referring to this learned data. In the table of learned data shown in  FIG. 5 , image scene types  501  are given on the vertical axis, including guard rail or shrubbery, gaps between guard rail or shrubbery, crosswalks, sidewalks without guard rail, parked vehicles, and gaps between parked vehicles, which are elements of the scene captured by the stereo camera  100  in the scene analysis diagram production process  207  among the processes executed in the scene analysis unit  103  of the stereo camera  100 . 
     Meanwhile, the horizontal axis  502  shows attributes of the places where the vehicle is traveling acquired in the external information acquisition unit  104  of the stereo camera  100 , including urban area, residential area, commercial facility, school, highway, elevated road, mountainous area, and road with few intersections. 
     The numbers listed in the table as the values  503  of the pedestrian existence probability indicate the pedestrian existence probability. For example, if the image scene is a guard rail/shrubbery and the place attribute is an urban area, the probability that a pedestrian exists is 10%. 
     Herein, in calculating the probability of the values  503  of the pedestrian existence probability, pre-acquired images are investigated to actually check the probability that a pedestrian exists, and thereby probability values are prepared as empirical values. 
     Next, a pedestrian existence probability is assigned to the scene analysis diagram of  FIG. 4  produced in the scene analysis diagram production process  207  executed in the scene analysis unit  103  of the stereo camera  100  based on the learned data regarding the pedestrian existence probability as shown in  FIG. 5 . Considering an example when the place attribute of the scene acquired in the external information acquisition unit  104  of the stereo camera  100  is a commercial facility, for example, referring to the table in  FIG. 5 , the pedestrian existence probability in the gap region  403  between a guard rail or shrubbery is 90% based on a value  504  of the pedestrian existence probability in  FIG. 5 . Similarly, with regard to the other guard rail or shrubbery regions  401  and  402 , the crosswalk region  404 , the sidewalk regions without a guard rail  405  and  406 , the parked vehicle regions  407  and  408 , and the gap region  409  between parked vehicles, a pedestrian existence probability is assigned to each of the above in the scene analysis diagram of  FIG. 4  as shown in  FIG. 6  referring to the existence probabilities from the table in  FIG. 5 . 
     In  FIG. 6 , the regions  601  indicated with a thick solid line frame are regions with a pedestrian existence probability of 90% (the regions  403 ,  404 ,  405 ,  406 , and  409  in  FIG. 6 ), the regions  602  indicated with a thin solid line frame are regions with a pedestrian existence probability of 60% (the regions  407  and  408 ), and the regions  603  indicated with a thin dotted line frame are regions with a pedestrian existence probability of 30% (the regions  401  and  402 ). 
     Next, the processes executed in the process priority change unit  106  of the stereo camera  100  will be explained in detail. 
     In the process priority change unit  106 , if the pedestrian existence probability is higher than a predetermined value, or for a portion in the image in which the pedestrian existence probability is higher than a predetermined value, the process priority is changed so that the pedestrian sensing process is executed with priority over other objects to be detected (a preceding vehicle, a sign, a lane, etc.). 
       FIG. 7  shows an overview of the process priority changing. As a result of calculating the pedestrian existence probability of the current scene in the existence probability calculation unit  105  of the stereo camera  100 , if there is a region in the scene in which the probability is at or above a certain fixed value, the process priority is changed as shown in  FIG. 7 .  FIG. 7  illustrates a process schedule  704  before the priority change and a process schedule  705  before the priority change. 
     In the process schedule  704  before the priority change, considering an example in which a pedestrian sensing process, a vehicle sensing process, and a sign sensing process are executed in the stereo camera  100 , a pedestrian sensing process  701 , a vehicle sensing process  702 , and a sign sensing process  703  are all executed sequentially in a period of 90 ms, such that the pedestrian sensing process  701  is executed first at 0 ms, the vehicle sensing process  702  is executed next, the sign sensing process  703  is executed last, and then a pedestrian sensing process  706  is executed again at 90 ms. 
     As a result of calculating the pedestrian existence probability of the current scene, if there is a region in the scene in which the probability is at or above a certain fixed value, the process priority is changed to the process schedule  705  before the priority change of  FIG. 7 . In other words, the process priority is changed such that a pedestrian sensing process  707  is executed first at 0 ms, a vehicle sensing process  708  is executed next, then a pedestrian sensing process  709  is executed again, a sign sensing process  710  is executed last, and then a pedestrian sensing process  711  is executed again at time 120 ms. 
     Thereby, the pedestrian sensing process is executed in a 60 ms period, and the vehicle sensing process and the sign sensing process are executed in a period of 120 ms. By repeatedly executing the pedestrian sensing process with priority, the pedestrian sensing performance can be improved. 
     Next, the processes executed in the parameter changing unit  107  of the stereo camera  100  will be explained in detail. 
       FIG. 8  illustrates a processing flow of the processes that are executed in the parameter changing unit  107 . First, in a region of high probability of pedestrians extraction process  801 , the regions having an existence probability at or above a certain fixed value are extracted from the pedestrian existence probabilities ( FIG. 6 ) calculated in the existence probability calculation unit  105  of the stereo camera  100 . If the certain fixed value is 80%, the regions  601  indicated with a thick solid line frame in  FIG. 6  (the regions  403 ,  404 ,  405 ,  406 , and  409 ) will be extracted. 
     Next, in a pedestrian sensing logic changing process  802 , if there are existence probability values that are at or above the certain fixed value among the pedestrian existence probabilities ( FIG. 6 ) calculated in the existence probability calculation unit  105  of the stereo camera  100 , a logic of the pedestrian sensing process is changed so that the pedestrian sensing process in the regions with a high pedestrian existence probability extracted previously in the region of high probability of pedestrians extraction process  801  is executed in more detail. Herein, in the pedestrian sensing process, pedestrians can be detected by making a determination in comparison with data resulting from learning many pedestrian images using an image feature quantity called HOG (Histograms of Oriented Gradients), which is described in the following Non-Patent Literature: “N. Dalal and B. Triggs, ‘Histograms of Oriented Gradients for Human Detection’, IEEE Symposium on Intelligent Vehicle, pp. 206-212, June, 2006”. In this process, the determination process can be made more detailed by adding another sensing process using a second feature quantity other than HOG, thereby improving the detection performance. 
     Further, in this process, the pedestrian sensing performance can be improved by, when making a determination in comparison to data that has been learned in advance using image feature quantities, lowering the determination threshold almost to the point of oversensitivity, and then adding a detailed determination regarding whether the movement of each part of the pedestrian, i.e. the head, shoulders, and legs of the pedestrian, resemble that of a pedestrian. 
     Next, in a pedestrian sensing region changing process  803 , the regions having an existence probability at or above the certain fixed value among the pedestrian existence probabilities ( FIG. 6 ) calculated in the existence probability calculation unit  105  of the stereo camera  100  are extracted and set as processing regions in which a pedestrian viewpoint is executed with priority. For example, in  FIG. 6 , settings are implemented so that the pedestrian sensing process is executed at a high frequency of a period of 60 ms for the regions including the regions  601  and  602  in which the pedestrian existence probability is 60% or greater, whereas the pedestrian sensing process is not executed or executed at a low frequency in the other regions. 
     Next, in an image preprocessing parameter changing process  804 , the regions having an existence probability at or above the certain fixed value among the pedestrian existence probabilities ( FIG. 6 ) calculated in the existence probability calculation unit  105  of the stereo camera  100  are extracted, and preprocessing parameters for these regions in which the pedestrian existence probability is high are changed to produce images in which a pedestrian can be easily sensed. Herein, in a case in which the image is overexposed in white or darkened in portions where the pedestrian existence probability is high such that it is difficult to detect a pedestrian, the entire image is subjected to gradation correction so that pedestrians are displayed with good contrast. Alternatively, gradation correction is conducted only in portions including the regions in which the pedestrian existence probability is at or above the certain fixed value that were previously extracted so that pedestrians are displayed with good contrast. 
     Finally, in an exposure control parameter changing process  805 , in a scene in which the pedestrian existence probability is high, the exposure control parameters of the left image capture unit  101  and the right image capture unit  102  of the stereo camera  100  are changed and adjusted so as to acquire an image in which a pedestrian can be easily sensed. Herein, the regions having an existence probability at or above the certain fixed value among the pedestrian existence probabilities ( FIG. 6 ) calculated in the existence probability calculation unit  105  of the stereo camera  100  are extracted and the brightness in these portions of the image is extracted, and then the exposure control parameters are changed so that the exposure time is shortened if the portions are bright and the exposure time is lengthened if the portions are dark. 
     Next, the processes executed in the vehicle speed determination unit  108  of the stereo camera  100  will be explained. 
     In the vehicle speed determination unit  108 , in a scene in which the pedestrian existence probability is high, a command for executing speed control by suppressing acceleration of the vehicle is generated and output to a vehicle speed control device. 
     In other words, if there are existence probability values that are at or above the certain fixed value among the pedestrian existence probabilities ( FIG. 6 ) calculated in the existence probability calculation unit  105  of the stereo camera  100 , even if the vehicle speed is lower than a user set speed of an ACC (Adaptive Cruise Control) of the vehicle, control is performed to suppress the vehicle speed without allowing the vehicle to accelerate to the set speed. 
     Further, the speed limit of the road where the vehicle is currently traveling is acquired from the car navigation device in the external information acquisition unit  104  of the stereo camera  100 , and if the vehicle speed is higher than the speed limit, deceleration control is performed to decelerate the vehicle to the speed limit. 
     Next, the processes executed in the distance calculation unit  109  of the stereo camera  100  will be explained in detail using the flowchart of  FIG. 9 . 
     In the flowchart of  FIG. 9 , first, in a left image input process  901 , image data captured by the left image capture unit  101  is received. Next, in a right image input process  902 , image data captured by the right image capture unit  102  is received. Herein, the left image input process  901  and the right image input process  902  can be simultaneously executed as parallel processes. 
     Next, in a corresponding point calculation process  903 , the two left and right images acquired in the left image input process  901  and the right image input process  902  are compared, and portions where the same object is captured are specified. As shown in  FIG. 10 , when an object  1001 , which is an object on the travel path, is captured by the stereo camera  100 , the images captured by the left image capture unit  101  and the right image capture unit  102  appear as the left image  1002  and the right image  1003 . Herein, the identical object  1001  is captured at an object position  1004  in the left image  1002  and is captured at an object position  1005  in the right image  1003 , and thus a deviation d 1  in the horizontal direction occurs between the images. Therefore, it is necessary to specify where the object captured at the object position  1004  of the left image  1002  is captured in the right image  1003 . 
     A method for specifying where the specific object captured in the left image  1002  is captured in the right image  1003  will now be explained using  FIG. 11 . 
     In  FIG. 11 , in the coordinate system of the left image  1002  and the right image  1003 , the horizontal axis is a u-axis  1101  and the vertical axis is a v-axis  1102 . First, in the left image  1002 , a rectangular region  1103  defined by (u1, v1), (u1, v2), (u2, v1), (u2, v2) in the uv coordinate system is set. 
     Next, in the right image  1003 , the U value is increased from u=0 to u=u3 so that a region defined by (U, v1), (U, v2), (U+(u2−u1), v1), (U+(u2−u1), v2) is scanned up to a rectangular region  1104  in the rightward direction of the image. When scanning, the correlation values of the image within the rectangular region  1103  and the image within the rectangular region  1104  are compared, and it is determined that an object which is identical to the object captured in the rectangular region  1103  is captured at a position (u4, v1), (u4, v2), (u4+(u2−u1), v1), (u4+(u2−u1), v2) of a rectangular region  1105  in the right image  1003  where the correlation with the rectangular region  1103  of the left image  1002  is the highest. Herein, the pixels within the rectangular region  1103  are regarded as corresponding to the pixels within the rectangular region  1105 . Herein, when scanning the rectangular region  1104  of the right image  1003 , if there are no rectangles in which the correlation value is at or above a certain fixed value, it is determined that there are no corresponding points in the right image  1003  that correspond to the rectangular region  1103  of the left image  1002 . 
     Next, the rectangular region  1103  of the left image  1002  is shifted to the position of a rectangular region  1106 , and the same process is executed. 
     In this way, rectangular regions in the left image  1002  are scanned throughout the entire left image  1002 , and corresponding points within the right image  1003  are found for all of the pixels in the left image  1002 . If no corresponding points are found, then it is determined that no corresponding points exist. 
     Next, a distance calculation process  904  in the flowchart of  FIG. 9  is executed. 
     In the distance calculation process  904 , with regard to the corresponding points of the left image  1002  and the right image  1003  capturing the same object found in the corresponding point calculation process  903  described above, the distance from the stereo camera  100  of the corresponding points is calculated. 
     First, using  FIG. 12 , a method for calculating a distance from the camera of an object point  1201  in the left image  1002  and the right image  1003  will be explained. 
     In  FIG. 12 , the left image capture unit  101  is a camera having focal length f and optical axis  1208  of the left image capture unit, and consisting of a lens  1202  and an image capture surface  1203 , and the right image capture unit  102  is a camera having focal length f and optical axis  1209  of the right image capture unit, and consisting of a lens  1204  and an image capture surface  1205 . The object point  1201  in front of the cameras is captured at a point  1206  (a distance d 2  from the optical axis  1208 ) on the image capture surface  1203  of the left image capture unit  101 , and becomes the point  1206  (a position of d 4  pixels from the optical axis  1208 ) in the left image  1002 . Similarly, the object point  1201  in front of the cameras is captured at a point  1207  (a distance d 3  from the optical axis  1209 ) on the image capture surface  1205  of the right image capture unit  102 , and becomes the point  1207  (a position of d 5  pixels from the optical axis  1209 ) in the right image  1003 . 
     In this way, the object point  1201  of the same object is captured at a position of d 4  pixels toward the left from the optical axis  1208  in the left image  1002 , and is captured at a position of d 5  toward the right from the optical axis  1209  in the right image  1003 . Thus, a parallax of d 4 +d 5  pixels is generated. 
     Therefore, if the distance between the optical axis  1208  of the left image capture unit  101  and the object point  1201  is x, a distance D from the stereo camera  100  to the object point  1201  can be calculated by the following formulas. 
     From the relationship between the object point  1201  and the left image capture unit  101  d 2 :f=x:D 
     From the relationship between the object point  1201  and the right image capture unit  102  d 3 :f=(d−x):D 
     Thus, D=f×d/(d 2 +d 3 )=f×d/{(d 4 +d 5 )×a}. Herein, a is the size of the image capture elements of the image capture surface  1203  and the image capture surface  1205 . 
     The distance calculation described above is carried out for all of the corresponding points calculated in the corresponding point calculation process  903  described above. As a result, a distance image expressing the distance from the stereo camera  100  to the object can be found. 
     In a distance information output process  905  of the flowchart of  FIG. 9 , this distance image is output and saved. 
     Finally, at a branch  906  of the flowchart of  FIG. 9 , if there are image input signals from the left image capture unit  101  and the right image capture unit  102 , the process returns to the left image input process  901 . At the branch  906 , if there are no image input signals from the left image capture unit  101  and the right image capture unit  102 , the process enters standby until image input signals are input into the distance calculation unit  109 . 
     Finally, the processes executed in the object-to-be-sensed sensing unit  110  of the stereo camera  100  will be explained. In the object-to-be-sensed sensing unit  110 , each sensing process is initiated following the process schedule shown in the process schedule  705  before the priority change of  FIG. 7  determined in the process priority change unit  106  of the stereo camera  100  as described above. In the example of  FIG. 7 , a pedestrian sensing process, a vehicle sensing process, and a sign sensing process are executed sequentially. The results of sensing are output from the stereo camera  100  to an external device. 
     Embodiment 2 
     Here, another embodiment in which the present invention is applied to a system for sensing a pedestrian using images of a stereo camera installed in a vehicle is shown in  FIG. 1 . 
     A stereo camera  1300 , which is an image capture device, has a left image capture unit  1301  and a right image capture unit  1302 . A processing device  1311  has the scene analysis unit  103 , the external information acquisition unit  104 , the existence probability calculation unit  105 , the process priority change unit  106 , the parameter changing unit  107 , the vehicle speed determination unit  108 , the distance calculation unit  109 , and the object-to-be-sensed sensing unit  110 . The processing content in each unit from the scene analysis unit  103  to the object-to-be-sensed sensing unit  110  is the same as that described in Embodiment 1. 
     In the present embodiment, the stereo camera  1300  and the processing device  1311  can be in separate housings. The stereo camera  1300  and the processing device  1311  are connected by a single or a plurality of signal lines, and the image captured by the left image capture unit  1301  and the image captured by the right image capture unit  1302  are sent to the processing device  1311 . 
     REFERENCE SIGNS LIST 
     
         
           100  stereo camera 
           101  left image capture unit 
           102  right image capture unit 
           103  scene analysis unit 
           104  external information acquisition unit 
           105  existence probability calculation unit 
           106  process priority change unit 
           107  parameter changing unit 
           108  vehicle speed determination unit 
           109  distance calculation unit 
           110  object-to-be-sensed sensing unit 
           111  processing device 
           201  left-right image acquisition process 
           202  distance data acquisition process 
           203  road region extraction process 
           204  parked vehicle detection process 
           205  road side condition determination process 
           206  crosswalk detection process 
           207  scene analysis diagram production process