Patent Publication Number: US-2022230287-A1

Title: Information processing device, information processing system, information processing method, and non-transitory storage medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based on and claims priority under 35 USC 119 from Japanese Patent Application No. 2021-007022 filed on Jan. 20, 2021, the disclosure of which is incorporated by reference herein. 
     BACKGROUND 
     Technical Field 
     The present disclosure relates to an information processing device, an information processing system, and an information processing program that perform processing to gather and store captured images captured by vehicles so as to enable browsing of the captured images. 
     Related Art 
     Japanese Patent Application Laid-Open (JP-A) No. 2012-129961 proposes an image database construction system including an image database provided with a reception section that receives information transmitted from a terminal, an image determination section that determines whether or not to adopt an image as a most recent image for a particular image capture location based on the information received by the reception section, and an image storage section that stores an image transmitted from the terminal as the most recent image for the image capture location in a case in which determination has been made by the image determination section to adopt the image as the most recent image. 
     However, the technology disclosed in JP-A No. 2012-129961 is not capable of obtaining an image in which no moving bodies are present in a case in which a background image is hidden by a moving body such as a person or vehicle. There is room for improvement in this respect. 
     SUMMARY 
     The present disclosure provides an information processing device, an information processing system, and an information processing program that are capable of employing an image acquired from a vehicle to generate an image in which a moving body is not present. 
     An information processing device according to a first aspect includes an acquisition section configured to acquire captured images that have been captured by plural vehicles and that each of the captured images satisfies plural predetermined conditions including an image capture freshness condition, an image capture condition, and a moving body condition relating to a moving body in the captured image, and to also acquire vehicle information including position information corresponding to the respective captured images, a detection section configured to detect the moving body present in one of the captured images acquired by the acquisition section, a selection section configured to, based on the captured images and the vehicle information acquired by the acquisition section, from other of the captured images acquired by the acquisition section and corresponding to an image capture position of the one captured image in which the moving body has been detected by the detection section, select another of the captured images having a predetermined similarity level or higher to the one captured image, and a merging section configured to remove the moving body detected by the detection section from the one captured image, to extract an image corresponding to a removed region from the other captured image selected by the selection section, and to merge these images. 
     According to the first aspect, the acquisition section acquires the captured images that have been captured by plural vehicles and that each of the captured images satisfies the plural predetermined conditions including the image capture freshness condition, the image capture condition, and the moving body condition relating to the moving body in the captured image. The acquisition section also acquires the vehicle information including the position information corresponding to the respective captured images. 
     The detection section detects the moving body present in the one captured image acquired by the acquisition section. Based on the captured images and the vehicle information acquired by the acquisition section, from other of the captured images acquired by the acquisition section and corresponding to the image capture position of the one captured image in which the moving body has been detected by the detection section, the selection section selects another of the captured images having a predetermined similarity level or higher to the one captured image. 
     The merging section removes the moving body detected by the detection section from the one captured image, extracts an image corresponding to the removed region from the other captured image selected by the selection section, and merges these images. Merging the captured images in this manner enables an image in which no moving body is present to be generated using the captured images acquired by the respective vehicles. 
     Configuration may be made wherein the acquisition section gives a score for the image capture freshness condition, the image capture condition, and the moving body condition, and acquires any of the captured images for which the score is a predetermined threshold or higher. 
     This enables score-based evaluation of the image capture freshness condition, the image capture condition, and the moving body condition, enabling easy acquisition of captured images that have been recently captured under favorable image capture conditions, and in which any moving bodies in the captured image occupy a small number of pixels. 
     Configuration may be made wherein the score is computed such that a score for the image capture freshness condition becomes higher the more recent an image capture date and time are, a score for the image capture condition that becomes higher as a brightness level approaches a predetermined brightness level suited to conditions at the time of image capture and the slower a vehicle speed is, and a score for the moving body condition becomes higher the fewer pixels that are occupied by the moving body in the captured image. This approach enables the image capture freshness condition, the image capture condition, and the moving body condition to be evaluated based on a single score. 
     Configuration may be made wherein the acquisition section performs acquisition a predetermined number of times within a predetermined time period. This enables appropriate captured images to be obtained from plural vehicles that have traveled past a target point during the predetermined time period. 
     Configuration may be made wherein the acquisition section changes the threshold and acquires the captured images so as to perform acquisition a predetermined number of times within a predetermined time period. This enables acquisition of the requisite number of captured images over the course of the predetermined time period. 
     Configuration may be made wherein the selection section prioritizes selection of the captured image for at least one case of a captured image captured by a same or a similar vehicle type, or a captured image captured at a same or a similar timing. This enables selection of a captured image having a higher similarity level than in a case in which captured images from different vehicle types or captured images taken at different timings are selected. 
     Configuration may be made wherein the selection section prioritizes selection of the captured image in a case in which a position of a vanishing point in the captured image is within a predetermined range. This enables selection of a captured image with a higher similarity level than in a case in which a captured image having a completely different vanishing point position is selected. 
     Configuration may be made wherein the selection section extracts a predetermined tracking region from the captured images, and selects as the other captured image a captured image having a feature value with a predetermined similarity level or higher to a feature value of the one captured image for the tracking region. This enables an appropriate captured image to be selected, while reducing the processing load. In such cases, the tracking regions may be configured by a region other than at least one region of a region in which an own-vehicle is captured in the captured image, or a region in which a vehicle traveling alongside is captured in the captured image. 
     An information processing system may be configured including the information processing device described above, and an onboard unit that is installed to a vehicle, and that includes an image capture section configured to capture a vehicle periphery to generate the captured images and a detection section configured to detect vehicle information including position information of the vehicle at a time of image capture. 
     Alternatively, an information processing program may be configured to cause a computer to function as the respective sections of the information processing device described above. 
     As described above, the present disclosure is capable of providing an information processing device, an information processing system, and an information processing program that are capable of employing an image acquired from a vehicle to generate an image in which a moving body is not present. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Exemplary embodiments of the present invention will be described in detail based on the following figures, wherein: 
         FIG. 1  is a diagram illustrating a schematic configuration of an information processing system according to an exemplary embodiment; 
         FIG. 2  is a block diagram illustrating configurations of an onboard unit and a central server of an information processing system according to an exemplary embodiment; 
         FIG. 3  is a block diagram illustrating configurations of a control section of an onboard unit and a central processing section of a central server in an information processing system according to an exemplary embodiment; 
         FIG. 4  is a diagram to explain a generation method for a common image by a common image generation section; 
         FIG. 5  is a flowchart illustrating an example of a flow of image capture processing performed by an onboard unit of an information processing system according to an exemplary embodiment; 
         FIG. 6  is a flowchart illustrating an example of a flow of processing to gather captured images from onboard units performed by a central server of an information processing system according to an exemplary embodiment; 
         FIG. 7  is a flowchart illustrating an example of a flow of processing performed by an onboard unit to transmit a captured image following a request from a central server in an information processing system according to an exemplary embodiment; 
         FIG. 8  is a flowchart illustrating an example of a flow of processing to generate a common image performed by a common image generation section of a central server in an information processing system according to an exemplary embodiment; 
         FIG. 9  is a flowchart illustrating a specific example of a flow of processing during video frame matching processing; and 
         FIG. 10  is a diagram to explain examples of a non-tracking region. 
     
    
    
     DETAILED DESCRIPTION 
     Detailed explanation follows regarding an example of an exemplary embodiment of the present disclosure, with reference to the drawings.  FIG. 1  is a diagram illustrating a schematic configuration of an information processing system according to the present exemplary embodiment. 
     An information processing system  10  according to the present exemplary embodiment includes onboard units  16  installed to respective vehicles  14  and a central server  12  serving as an information processing device, connected together over a communication network  18 . In the present exemplary embodiment, onboard units  16  installed to plural vehicles  14  are capable of communicating with the central server  12 . 
     In the information processing system  10  according to the present exemplary embodiment, the central server  12  performs processing to gather various data stored in the plural onboard units  16 . Examples of the various data stored in the onboard units  16  include image information expressing captured images obtained by image capture and vehicle information expressing states of the respective vehicles  14 . In the present exemplary embodiment, the central server  12  employs the captured images gathered from the onboard units  16  in processing to generate captured images in which moving bodies such as vehicles  14  and pedestrians do not appear. 
     Detailed explanation follows regarding the configuration of respective sections of the information processing system  10  according to the present exemplary embodiment.  FIG. 2  is a block diagram illustrating configuration of the onboard units  16  and the central server  12  of the information processing system  10  according to the present exemplary embodiment. 
     Each of the onboard units  16  includes a control section  20 , a vehicle information detection section  22 , an image capture section  24 , a communication section  26 , and a display section  28 . 
     The vehicle information detection section  22  detects vehicle information relating to the corresponding vehicle  14 , including at least position information for the vehicle  14 . Examples of the vehicle information detected include the position information, a vehicle speed, acceleration, steering angle, accelerator pedal position, distances to obstacles peripheral to the vehicle, a route, and the like for the vehicle  14 . Specifically, the vehicle information detection section  22  may employ plural types of sensors and devices to acquire information expressing a situation in the vehicle  14  and its peripheral environment. Examples of such sensors and devices include sensors installed to the vehicle  14 , such as a vehicle speed sensor and an acceleration sensor, as well as a global navigation satellite system (GNSS) device, an onboard communication device, a navigation system, a radar system, and so on. A GNSS device measures the position of the own-vehicle  14  by receiving GNSS signals from plural GNSS satellites. An onboard communication device is a communication device that communicates through the communication section  26  using at least one of vehicle-to-vehicle communication with other vehicles  14  or road-to-vehicle communication with roadside equipment. A navigation system includes a map information storage section that stores map information, and performs processing to display the position of the own-vehicle  14  on a map and guide the own-vehicle  14  on a route to a destination based on the position information obtained by the GNSS device and the map information stored in the map information storage section. A radar system includes plural radar units with different detection ranges to one another, and is used to detect objects such as pedestrians and other vehicles  14  present peripheral to the vehicle  14  and also to acquire relative positions and relative speeds of such detected objects with respect to the vehicle  14 . Such a radar system includes a built-in processor to process detection results for peripheral objects. This processor eliminates noise and roadside objects such as guardrails from monitoring targets based on changes in the relative positions and relative speeds of the respective objects included in the several most recent detection results, and tracks and monitors pedestrians, other vehicles  14 , and the like as monitoring targets. The radar system also outputs information relating to the relative positions, relative speeds, and the like of the respective monitoring targets. 
     The image capture section  24  is installed to the vehicle  14 , and images the vehicle periphery, for example ahead of the vehicle  14 , in order to generate video image data as image data expressing captured images in video images. For example, a camera of a drive recorder or the like may be applied as the image capture section  24 . Note that the image capture section  24  may also image the vehicle periphery to at least one of the sides or the rear of the vehicle  14 . The image capture section  24  may also image a vehicle cabin interior. In the present exemplary embodiment, the image information generated by the image capture section  24  is initially saved in the control section  20 , although such image information may, for example, be uploaded to the central server  12  without being saved. 
     The communication section  26  establishes communication with the central server  12  over the communication network  18 , and exchanges various data such as the image information obtained through image capture by the image capture section  24  and the vehicle information detected by the vehicle information detection section  22  with the central server  12 . Note that the communication section  26  may also be configured capable of establishing inter-vehicle communication in order to perform vehicle-to-vehicle communication. 
     The display section  28  displays various information in order to provide the various information to an occupant. For example, the display section  28  may display information provided from the central server  12 . 
     As illustrated in  FIG. 3 , the control section  20  is configured by a generic microcomputer including a central processing unit (CPU)  20 A, read only memory (ROM)  20 B, random access memory (RAM)  20 C, storage  20 D, an interface (I/F)  20 E, a bus  20 F, and the like. 
     The CPU  20 A of the control section  20  serves as a second processor, and expands and executes a program held in the ROM  20 B, serving as a second memory, in the RAM  20 C in order to perform processing to upload the various information to the central server  12  and the like. Note that a program may be expanded into the RAM  20 C from the storage  20 D, serving as a second memory. 
     Meanwhile, as illustrated in  FIG. 2 , the central server  12  includes a central processing section  30 , a central communication section  36 , and a database (DB)  38 . 
     As illustrated in  FIG. 3 , the central processing section  30  is configured by a generic microcomputer including a CPU  30 A, ROM  30 B, RAM  30 C, storage  30 D, an interface (I/F)  30 E, a bus  30 F, and the like. Note that a graphics processing unit (GPU) may be applied as the CPU  30 A. 
     The CPU  30 A of the central processing section  30  serves as a first processor, and expands and executes a program held in the ROM  30 B or the storage  30 D, either of which may serve as a first memory, in the RAM  30 C in order to function as a captured image acquisition section  40 , an acquisition condition management section  50 , and a common image generation section  60 . Note that the captured image acquisition section  40  and the acquisition condition management section  50  both correspond to an acquisition section. The common image generation section  60  corresponds to a detection section, a selection section, and a merging section, and will be described in detail later. 
     From the captured images captured by the plural vehicles  14 , the captured image acquisition section  40  acquires and collects in the DB  38  captured images and vehicle information, including position information corresponding to the captured images, that conform to conditions set by the acquisition condition management section  50 . The captured image acquisition section  40  may perform acquisition a predetermined number of times within a predetermined time period. This enables appropriate captured images to be obtained from plural vehicles that have traveled past a target point during the predetermined time period. 
     The acquisition condition management section  50  manages acquisition conditions for the captured images acquired from the plural vehicles  14 . Specifically, the acquisition condition management section  50  sets conditions for acquisition of captured images from the vehicles  14  so as to acquire captured images that satisfy plural predetermined conditions, including an image capture freshness condition, image capture conditions, and a moving body condition relating to a moving body present in the captured image. For example, the acquisition condition management section  50  manages so as to acquire captured images that are recent captured images conforming to the image capture freshness condition, are also captured images captured under favorable image capture conditions (for example during the daytime, in good weather, and at low speed) conforming to the image capture conditions, and are also captured images in which moving bodies such as pedestrians or vehicles  14  occupy a small number of pixels conforming to the moving body condition. The acquisition condition management section  50  scores for the plural conditions including the image capture freshness condition, the image capture conditions, and the moving body condition, and the captured image acquisition section  40  performs management so as to acquire captured images having a score that meets a predetermined threshold or higher. This enables score-based evaluation of the plural conditions, enabling easy acquisition of captured images that have been recently captured under favorable image capture conditions, and in which any moving bodies in the captured image occupy a small number of pixels. For example, the score for the image capture freshness condition computed to give a higher score the more recent the image capture date and time are, the image capture condition score is computed to give a higher score the closer a brightness level to a predetermined brightness level suited to the conditions at the time of image capture and the slower the vehicle speed, and the moving body condition score is computed to give a higher score the fewer pixels occupied by a moving body in the captured image. This approach enables the image capture freshness condition, the image capture condition, and the moving body condition to be evaluated based on a single score. Note that the scores for the plural conditions including the image capture freshness condition, the image capture condition, and the moving body condition may, for example, be scores computed using the vehicle information acquired together with the captured images from the onboard units  16 . 
     The common image generation section  60  detects for moving bodies present in one captured image. Then, based on the captured images and vehicle information collected in the DB  38 , the common image generation section  60  selects another captured image having a predetermined similarity level or higher to the one captured image from other captured images collected in the DB  38  having an image capture position corresponding to that of the captured image in which a moving body has been detected. Namely, the common image generation section  60  selects a captured image that is similar to the one captured image in its own right and that also has the same or a similar image capture position. Specifically, the common image generation section  60  selects captured images using video frame matching processing. The video frame matching processing is used to extract captured images captured within a specific range (for example 10 m toward the front and rear) of a comparison target captured image from captured images captured by a vehicle  14  traveling past the same point according to the position information. Feature values (specifically, local feature values of plural locations, configured by a collection of plural local feature value vectors) are calculated, and matches for the feature values are ascertained in a predetermined tracking region in order to select a matching result with a high similarity level. Note that the predetermined tracking region is, for example, a region other than a region in which the bonnet or the like of the own-vehicle  14  appears. In a case in which the other captured image with an image capture position corresponding to that of the one captured image in which a moving body has been detected is selected from captured images from other vehicles  14 , selection of a captured image that is at least one of a captured image captured by a same or a similar vehicle type or a captured image captured at a same or a similar timing may be prioritized. This enables selection of a captured image having a higher similarity level than in a case in which captured images from different vehicle types or captured images taken at different timings are selected. Note that when selecting a captured image having a predetermined similarity level or higher, the vanishing point may be used to prioritize selection of images having a vanishing point position within a specific range (for example when positional misalignment of the vanishing point is within a predetermined range, for example 10 to 20 pixels, with respect to the captured image). This enables selection of a captured image with a higher similarity level than in a case in which a captured image having a completely different vanishing point position is selected. Alternatively, when performing video frame matching processing, matching may be performed after correcting misalignment between captured images using the vanishing point position. Alternatively, in the case of captured images that cannot be obtained from the same viewpoint, for example the same traffic lane, lateral correction may be carried out before performing matching. 
     The common image generation section  60  also performs removal processing to identify a moving body in the one captured image and remove the moving body from the one captured image, and merge processing to extract from the other captured image selected using the video frame matching processing an image corresponding to a region from which the moving body has been removed, and merging the respective images together. An image generated as a result of the removal processing and the merge processing is then held in the DB  38  as a common image. For example, as illustrated in  FIG. 4 , in a case in which a single leading vehicle  14  and a single pedestrian  64  are present in a given captured image  62  among captured images that have been uploaded, a post-removal captured image  66  is generated in which the pedestrian  64  and the vehicle  14  have been removed from the given captured image  62 . In a case in which a single pedestrian  64  is present in a selected captured image  68  that has been selected by the video frame matching processing, a post-removal selected captured image  70  is generated in which the pedestrian  64  has been removed from the selected captured image  68 . Images corresponding to locations in the post-removal captured image  66  from which the pedestrian  64  and the vehicle  14  have been removed are then extracted from the post-removal selected captured image  70  and merged with the post-removal captured image  66  so as to generate a common image  72 . When extracting from the post-removal selected captured image  70  and merging with the post-removal captured image  66 , abstract features are extracted and conditions relating to the brightness level of the image, such as lighting conditions, are adjusted when merging. This enables the common image  72 , in which neither the vehicle  14  nor the pedestrian  64  are present, to be generated and collected in the DB  38 . Note that  FIG. 4  is a diagram to explain a generation method of a common image by the common image generation section  60 . Moreover, regarding the removal of moving bodies and merging, although explanation follows regarding a case in which removal of moving bodies and merging are performed using shapes corresponding to those of the moving bodies, configuration may be made such that bounding boxes containing the identified moving bodies are removed and regions corresponding to the bounding boxes are then extracted from the captured image from another vehicle  14  and merged. Moreover, when removing a shape corresponding to a moving body, although the present exemplary embodiment envisages removal of a region in which a margin is provided around the moving body to give a shape larger than that of the moving body, alternatively a shape fitted to the outline of the moving body may be removed instead. 
     The central communication section  36  establishes communication with the onboard units  16  over the communication network  18 , and exchanges information such as image information and vehicle information with the onboard units  16 . 
     The DB  38  requests information transmission from the respective vehicles  14  and collects the resulting data acquired from the respective vehicles  14 . The DB  38  also collects the common images  72  generated by the common image generation section  60 . Examples of the data acquired from the vehicles  14  and collected include image capture information expressing the captured images captured by the image capture sections  24  of the respective vehicles  14 , vehicle information detected by the vehicle information detection sections  22  of the respective vehicles  14 , and the like. 
     Note that imagery employed in map generation and the like preferably employs captured images captured under predetermined favorable image capture conditions, for example recent imagery that was captured during the day, in good weather, and at a low travel speed. 
     In the present exemplary embodiment, in order to generate the common images  72  from which moving bodies such as leading vehicles  14  and pedestrians  64  have been removed, it is desirable to acquire captured images in which moving bodies are not present wherever possible. Accordingly, in addition to the image capture conditions listed above, acquisition conditions may also be managed to avoid, as far as possible, uploading captured images when conditions such as the following apply. Note that since the aim is only to “avoid as far as possible”, if for example the only captured images available do not satisfy a condition of having being captured within the past month, such captured images may still be employed in common image generation despite not meeting this condition. 
     As an example of an acquisition condition, captured images may be avoided in a case in which a pedestrian  64  has been detected based on pedestrian detection information detected using the functionality of an advanced driver-assistance system (ADAS) that includes functionality to detect and avoid collisions with such pedestrians  64 . 
     As another example of an acquisition condition, similarly, captured images captured when traveling behind a leading vehicle may be avoided, in particular when an inter-vehicle distance is short or when captured in heavy traffic. This condition may take into account not only an own-vehicle traffic lane but also neighboring traffic lanes. 
     As another example of an acquisition condition, density information regarding pedestrians  64  and vehicles  14  may be acquired from a separate database (for example a mobile spatial statistics database) in order to avoid captured images from regions with a high density of pedestrians  64  and/or vehicles  14 . 
     As another example of an acquisition condition, captured images may be avoided before and after slips observed based on vehicle information relating to anti-lock brake system (ABS) actuation, in order to avoid captured images captured following rain or in icy conditions. 
     As another example of an acquisition condition, captured images captured when traveling in a right hand side traffic lane on left hand drive roads, when changing lane, or the like may also be avoided. In other words, captured images captured when traveling in the traffic lane closest to the sidewalk and not changing lanes are uploaded. 
     As an example, upload determination by the acquisition condition management section  50  employing acquisition conditions such as those described above may be made offline. In such cases, upload determination is performed using combined scores for the plural conditions described above. The combined scores for the plural conditions may, for example, be computed using weighted summing or the like. 
     Specifically, initially only vehicle information, this having a smaller data size than the captured images, is uploaded. Appropriate captured images are then extracted and uploaded from plural vehicles that have traveled past a target point during a given time period. 
     Since each of the vehicles  14  has finite storage, it is necessary to perform upload determination within a certain time period (threshold update: for example one week) that is shorter than an update frequency of the common images in the DB  38  (for example one month). Accordingly, a threshold is decided based on recent results such that upload instructions are given in a manner that will achieve an appropriate number of uploads. Namely, the threshold may be changed and upload instructions given such that acquisition is performed a predetermined number of times over the course of a predetermined time period. This enables acquisition of the requisite number of captured images over the course of the predetermined time period. When changing the threshold, the threshold is changed based on results of past travel. For example, in a case in which acquisition of a single captured image over the course of one month is desired, if it is known based on scores from a preceding time period that four captured images having scores of 1, 2, 4, and 8 are likely be acquirable during this time period, the threshold for upload determination may be set to around 6. Moreover, if an upload exceeding the threshold has already been acquired within a given month, the threshold may be raised for the time period of the next threshold update (for example the next one week), whereas the threshold may be lowered if it does not seem likely that the requisite uploads will be achieved within the time period based on the current threshold. Note that the threshold update may set different thresholds for each street or each district, since the level of congestion of vehicles  14  and pedestrians will differ between streets and districts. 
     Next, explanation follows regarding specific processing performed by the respective sections of the information processing system  10  in the present exemplary embodiment configured as described above. 
     First, explanation follows regarding specific processing performed in order to capture the vehicle periphery with the image capture section  24  of the onboard unit  16 .  FIG. 5  is a flowchart illustrating an example of a flow of image capture processing performed by the onboard units  16  of the information processing system  10  according to the present exemplary embodiment. Note that as an example, the processing illustrated in  FIG. 5  is initiated when the onboard unit  16  is started up when a non-illustrated ignition switch or the like of the corresponding vehicle  14  is switched ON. 
     At step  100 , the CPU  20 A starts vehicle periphery image capture, and processing transitions to step  102 . Namely, the image capture section  24  starts image capture of the vehicle periphery. 
     At step  102 , the CPU  20 A acquires the required vehicle information as a captured image profile, and processing transitions to step  104 . This acquisition of vehicle information is performed by acquiring detection results of the vehicle information detection section  22 . Information regarding the weather at the time of image capture as well as image capture conditions and congestion information may also be acquired from an external server. 
     At step  104 , the CPU  20 A appends the acquired profile information to the captured image, and processing transitions to step  106 . 
     At step  106 , the CPU  20 A saves the profiled captured image in the storage  20 D, and processing transitions to step  108 . The profiled captured image is saved such that the captured image is saved in association with the profile information, with the profile information being saved so as to be capable of being read independently of the corresponding captured image. 
     At step  108 , the CPU  20 A determines whether or not to end image capture. This determination is, for example, determination as to whether or not an instruction has been given to switch the non-illustrated ignition switch OFF. In a case in which this determination is negative, processing returns to step  102  to continue image capture and repeat the processing described above. In a case in which determination is affirmative the image capture processing routine is ended. 
     Next, explanation follows regarding specific processing performed by the central server  12  when gathering captured images from the onboard units  16 .  FIG. 6  is a flowchart illustrating an example of a flow of processing performed by the central server  12  of the information processing system  10  according to the present exemplary embodiment in order to gather captured images from the onboard units  16 . Note that as described above, the processing in  FIG. 6  is initiated according to a regular cycle with a shorter time period (for example one week) than the predetermined update frequency of the common images  72  (for example one month). 
     At step  200 , the CPU  30 A issues a transmission request for profile information corresponding to a predetermined time period (for example one week) to the respective onboard units  16 , and processing transitions to step  202 . Namely, the acquisition condition management section  50  issues an acquisition request for profile information corresponding to the predetermined time period from the profile information saved in the storage  20 D of the respective onboard units  16 . 
     At step  202 , the CPU  30 A determines whether or not profile information has been received. This determination is determination as to whether or not the requested profile information has been received, and the CPU  30 A stands by until determination is affirmative before processing transitions to step  204 . 
     At step  204 , the CPU  30 A computes a score by scoring the plural acquisition conditions relating to captured image acquisition, and processing transitions to step  206 . For example, as described above, weighted averages or the like are employed to compute a score for each captured image corresponding to the profile information. 
     At step  206 , the CPU  30 A chooses captured images as upload targets based on their scores, and processing transitions to step  208 . For example, captured images having a score that meets a predetermined threshold or higher are chosen as the upload targets. 
     At step  208 , the CPU  30 A issues a transmission request for the upload target captured images to the onboard units  16 , and processing transitions to step  210 . For example, the acquisition condition management section  50  outputs to the onboard units  16  a transmission request for captured images having a computed score meeting the predetermined threshold or higher. 
     At step S 210 , the CPU  30 A determines whether or not a target captured image has been received. The CPU  30 A stands by until determination is affirmative before processing transitions to step  212 . 
     At step  212 , the CPU  30 A sequentially collects the received captured images in the DB  38 , and then ends the captured image gathering processing routine. 
     Next, explanation follows regarding specific processing performed by the onboard unit  16  when transmitting captured images following a request from the central server  12 .  FIG. 7  is a flowchart illustrating an example of a flow of processing performed by the onboard unit  16  of the information processing system  10  according to the present exemplary embodiment in order to transmit captured images following a request from the central server  12 . Note that the processing in  FIG. 7  is initiated on receipt of a profile information transmission request from the central server  12 . 
     At step  300 , the CPU  20 A extracts from the storage  20 D the profile information of captured images captured over the course of the predetermined time period, and processing transitions to step  302 . 
     At step  302 , the CPU  20 A transmits the extracted profile information to the central server  12 , and processing transitions to step  304 . 
     At step  304 , the CPU  20 A determines whether or not a transmission request for captured images has been issued from the central server  12 . This determination is determination as to whether or not a transmission request for captured images has been issued at step  208  described above. The CPU  20 A stands by until determination is affirmative before processing transitions to step  306 . 
     At step  306 , the CPU  20 A extracts from the storage  20 D any captured images subject to the request, and processing transitions to step  308 . 
     At step  308 , the CPU  20 A transmits the captured images subject to the request to the central server  12 , and the captured image transmission processing routine is ended. 
     Next, explanation follows regarding specific processing performed by the central server  12  in order to generate the common image  72 .  FIG. 8  is a flowchart illustrating an example of a flow of processing performed by the common image generation section  60  of the central server  12  of the information processing system  10  according to the present exemplary embodiment in order to generate a common image. The processing of  FIG. 8  is initiated according to a regular cycle based on the predetermined update frequency of the common images  72 . 
     At step  400 , the CPU  30 A reads a given captured image  62  from the captured images collected in the DB  38  over a predetermined time period, and processing transitions to step  402 . 
     At step  402 , the CPU  30 A performs video frame matching processing, and processing transitions to step  404 . In the video frame matching processing, for example captured images from a specific range (for example 10 m toward the front and rear) of a comparison target captured image of the captured images captured by vehicles  14  traveling past the same point are extracted, and respective local feature values are calculated to ascertain matches between the local feature values in the tracking regions in order to select a captured image having a high similarity level in the matching results as the selected captured image  68 . Ascertaining matches between the local feature values in the respective tracking regions in this manner enables an appropriate captured image to be selected, while reducing the processing load. Note that the video frame matching processing corresponds to that of a selection section, and this processing will be described in detail later. Moreover, as described above, the tracking region is a region other than a region in which the bonnet or the like of the own-vehicle  14  appears, although there is no limitation thereto. For example, a region other than a region in which at least one of the own-vehicle  14  or a peripheral moving body appears in the captured image may be adopted as a predetermined tracking region. 
     At step  404 , the CPU  30 A identifies moving bodies in the captured images, and processing transitions to step S 406 . For example, deep learning using technology such as semantic segmentation, YOLOv4, or the like is employed to identify moving bodies such as pedestrians  64  and vehicles  14 . Moving bodies are identified in both the given captured image  62  and the selected captured image  68  extracted by the video frame matching processing. Note that the processing of step  404  corresponds to that of a detection section. 
     At step  406 , the CPU  30 A removes the moving bodies in the captured images from the given captured image  62  and the selected captured image  68  respectively, and processing transitions to step  408 . 
     At step  408 , the CPU  30 A extracts from the selected captured image  68  selected by the video frame matching processing a region corresponding to a removal target, and processing transitions to step  410 . Note that although explanation has been simplified, it is assumed that the selected captured image  68  selected either contains no moving body, or a moving body that is present is at a different position to the moving body in the given captured image  62 , and it is also assumed that the region of the given captured image  62  from which the moving body has been removed may be supplemented using the selected captured image  68 . 
     At step  410 , the CPU  30 A merges the region extracted from the selected captured image  68  with the region of the given captured image  62  from which the moving body has been removed, and processing transitions to step  412 . Note that the processing of steps  406  to  410  corresponds to that of a merging section. 
     At step  412 , the CPU  30 A saves the merged image in the DB  38  as a common image  72 , and processing transitions to step  414 . 
     At step  414 , the CPU  30 A determines whether or not to end generation of the common images  72 . This determination is determination as to whether or not the above processing regarding captured images captured within the predetermined time period has been completed. Processing returns to step  400  in a case in which determination is negative, and the processing described above is repeated with another captured image as the given captured image  62 . The processing routine of the common image generation section  60  is ended in a case in which determination is affirmative at step  414 . 
     Generating the common images  72  in this manner enables captured images in which no moving bodies are present to be generated using the captured images acquired from the vehicles  14 . 
     Next, explanation follows regarding the video frame matching processing mentioned above.  FIG. 9  is a flowchart illustrating an example of a specific flow of processing of the video frame matching processing. 
     At step  500 , the CPU  30 A extracts vehicles  14  that have traveled through the same region, and processing transitions to step  502 . For example, vehicles  14  that have traveled through the same region are extracted based on the position information included in the vehicle information. 
     At step  502 , the CPU  30 A extracts captured images from nearby vehicles configuring comparative vehicles, and processing transitions to step  504 . Namely, the CPU  30 A extracts captured images captured by vehicles  14  near to the vehicle  14  that captured a given captured image as a candidate pool for the selected captured image  68 . 
     At step  504 , the CPU  30 A computes feature values of the captured images from the comparative vehicles, and processing transitions to step  506 . Note that the feature values are a collection of plural local feature value vectors, and such local feature values are computed for plural locations. 
     At step  506 , the CPU  30 A computes feature values for an extracted image pool, and processing transitions to step  508 . Namely, respective local feature values are computed for each captured image in the candidate pool for the selected captured image  68 . 
     At step  508 , the CPU  30 A chooses a non-tracking region, and processing transitions to step  510 . Examples of the non-tracking region include at least one region of an own-vehicle region  74  in which the hood or the like of the own-vehicle  14  appears in the captured image, or a neighboring vehicle region  76  in which a vehicle  14  is traveling alongside the own-vehicle  14 , as illustrated in  FIG. 10 . The non-tracking region is, for example, ascertained using semantic segmentation. 
     At step  510 , the CPU  30 A finds feature value matches for the tracking region outside the non-tracking region, and processing transitions to step  512 . Setting the non-tracking region and finding matches using feature values (specifically, local feature values for plural locations, configured by a collection of plural local feature value vectors) enables the processing load to be reduced in comparison to cases in which a non-tracking region is not set. Note that configuration may be made such that step  508  is omitted and matches are found for local feature values without setting non-tracking regions. 
     At step  512 , the CPU  30 A selects an image with a high similarity level based on the feature value matching results as the selected captured image  68 , and the processing routine is ended. 
     By performing video frame matching processing in this manner, the requisite selected captured image  68  is selected in order to supplement the region from which a moving body has been removed from the given captured image  62 , thereby enabling generation of a common image  72  in which no moving body is present. 
     Note that in the exemplary embodiment described above, explanation has been given in which the respective functionality of the captured image acquisition section  40 , the acquisition condition management section  50 , and the common image generation section  60  is implemented by functionality of the single central server  12 . However, there is no limitation thereto, and the respective functionality may be distributed between plural servers. 
     In the exemplary embodiment described above, the central server  12  requests and acquires upload target images. However, there is no limitation thereto, and configuration may be made such that scores are computed on the onboard unit  16  side and captured images that meet a threshold or higher are then uploaded to the central server  12 . 
     Moreover, in the exemplary embodiment described above, an example has been described in which a removal target region is extracted from a single captured image and merged when a moving body is removed from a captured image and the region from which the moving body has been removed is then supplemented using another captured image. However, there is no limitation thereto. For example, configuration may be made such that plural captured images are employed to generate an image corresponding to a removal target region, and this generated image is then merged with the region from which the moving body has been removed from the captured image. 
     Although explanation has been given in the above exemplary embodiment in which the respective processing executed by the central server  12  and the onboard unit  16  is software processing implemented by executing a program, there is no limitation thereto. For example, the respective processing may be implemented by hardware processing employing an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or the like. Alternatively, the respective processing may be implemented by a combination of both software processing and hardware processing. In a case in which software processing is employed, a program may be circulated stored on various non-transitory storage media. Although explanation has been given regarding an example in which the CPU  20 A and the CPU  30 A serve as processors that execute software processing, there is no limitation thereto. For example, a GPU, ASIC, FPGA, or programmable logic device (PLD) may be applied as such processors. 
     The present disclosure is not limited to the foregoing description, and obviously various other modifications may be implemented within a range not departing from the spirit of the present disclosure.