Patent Publication Number: US-2022230333-A1

Title: Information processing system, information processing method, and program

Description:
INCORPORATION BY REFERENCE RELATED APPLICATION 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2021-006183, filed Jan. 19, 2021, the entire contents of which are incorporated herein by reference. Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet as filed with the present application are hereby incorporated by reference under 37 CFR 1.57. 
     FIELD 
     Embodiments described herein relate generally to an information processing system, an information processing method and a program related to object detection and work content monitoring. 
     BACKGROUND 
     A technology for detecting objects and recognizing the work of an operator by using image data or the like that has been captured by a camera has been proposed. For example, a technology has been proposed in which a part of a person&#39;s body and another object are detected in image data, and then the work being performed by the person can be recognized by associating the person with the detected object. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an information processing system related to a first embodiment. 
         FIG. 2  is a flowchart of detection processing according to a first embodiment. 
         FIG. 3  is a flowchart of calculation processing according to a first embodiment. 
         FIG. 4  is a flowchart of determination processing according to a first embodiment. 
         FIG. 5  is a diagram showing an example of a display screen including a determination result. 
         FIG. 6  is a diagram showing an example of a setting screen. 
         FIG. 7  is a diagram showing an application example of a system which determines a transport status of a cargo. 
         FIG. 8  is a diagram showing an application example of a system which determines a passenger boarding status of a vehicle. 
         FIG. 9  is a block diagram of an information processing system related to a second embodiment. 
         FIG. 10  is a flowchart of detection processing according to a second embodiment. 
         FIG. 11  is a flowchart of determination processing according to a second embodiment. 
         FIG. 12  is a block diagram of an information processing system related to a modification example. 
         FIG. 13  is a hardware configuration diagram of a device related to at least one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     In related art, the accuracy of detection or recognition may be reduced when multiple objects must be detected within the same image. For example, when cargo being transported by a person is targeted as an object for detection rather than the person transporting the cargo, it may be difficult to detect the cargo object because its appearance and shape may be different for each scene (image). Furthermore, when a plurality of similar objects exist in the image data within the vicinity of the person, the attempted association between person and object based on the detection of the person (or a part of the person) and the object in the same scene may fail. 
     In general, according to one embodiment, an information processing system includes a processor configured to detect a first object in an image in time-series image data and output object information indicating a detection of the first object. The processor is further configured to calculate a movement status of one or more pixels included in the image by using a plurality of images in the time-series image data, and then output movement information indicating the movement status of the one or more pixels. The processor is configured to detect whether the second object moves in conjunction with the first object based on the object information and the movement information. 
     In the following, certain example embodiments of an information processing system related to the present disclosure will be described with reference to the accompanying drawings. 
     First Embodiment 
     The information processing system related to the first embodiment uses a movement area that is located around an object (first object) (such as a person) that is detected from image data, and then determines the presence or absence of another object (hereinafter referred to as a moving object or second object) that moves in conjunction with the first object. As a result, it is possible, with higher accuracy, to detect a moving object even though the moving object may have a different appearance or the like image to image or while moving. Furthermore, by using the movement area, it is also possible to prevent erroneous association between objects. 
       FIG. 1  is a block diagram showing an example of the configuration of an information processing device  100  as the information processing system related to the first embodiment. As shown in  FIG. 1 , the information processing device  100  includes an image capturing unit  151 , a display unit  152 , a storage unit  153 , a reception unit  101 , an object detection unit  110 , an area calculation unit  120 , and a determination unit  130 , an output control unit  102 , and a setting unit  103 . 
     The image capturing unit  151  can be an image capturing device such as a camera that captures or otherwise acquires image data. For example, the image capturing unit  151  records images of an imaging area set as a target region for detecting an object and a moving object, and sequentially outputs image data of the target region in time-series as captured at different imaging times. The time-series image data may be video image data or the like output at a constant frame rate. The information processing device  100  may include a plurality of image capturing units  151 . 
     The display unit  152  is a display device such as a liquid crystal display that displays various information for use and/or operation of the information processing device  100 . 
     The storage unit  153  is a storage device that stores various information used in, or output by, the information processing device  100 . For example, the storage unit  153  stores the image data supplied by the image capturing unit  151  and the processing results. The storage unit  153  may be any commonly used storage medium such as a flash memory, a memory card, a random access memory (RAM), a hard disk drive (HDD), an optical disk, or the like. 
     The reception unit  101  receives input of time-series image data (a sequence of images, each image having different acquisition times) from the image capturing unit  151 . The reception unit  101  outputs the image data to the object detection unit  110  and the area calculation unit  120 . 
     By analyzing the image data, the object detection unit  110  detects a specific object (first object) depicted in the image data and outputs information corresponding to the detection result for the specific object. In general, the specific object may be detected in any way available. In the present example, the specific object is a person or a transport device or apparatus that can be used for transporting another object (second object). 
     For example, the object detection unit  110  detects a first object within the image data as a specific pixel group at certain coordinates, a rectangular region, or an area in the image. The object detection unit  110  evaluates the type of the detected first object and also the reliability of the detection result. The object detection method by the object detection unit  110  may be any method, and for example, one of the following methods may be applied:
         learning-based object detection using a neural network   object detection using feature data such as histograms of oriented gradients (HOG) feature analysis       

     As shown in  FIG. 1 , the object detection unit  110  may include a tracking unit  111 . The tracking unit  111  performs tracking processing for associating specific objects detected in different images. For example, the tracking unit  111  tracks an object between frames (frame-to-frame) by collating and associating an object detected in image data (a frame) captured at one time (e.g., most recent or current frame) with an object detected in image data captured at some previous time (a previous frame). The object detection unit  110  outputs object information including a tracking result from the tracking processing. 
     The association of objects between frames may be performed by any method. For example, the object detection unit  110  may employ a method of associating objects between frames based on one or more of the proximity of positions, the overlap ratio of object areas, the similarity of movement directions, the relationship between movement directions and displacement, shape, color, texture, and operation. When the tracking processing is not performed, the object detection unit  110  does need to not include the tracking unit  111 . 
     The area calculation unit  120  calculates a movement area indicating a movement status of one or more pixels included in the image data by analyzing a plurality of received images, and outputs the movement area information according to the calculation result. For example, the movement area information indicates the presence and absence of movement, the movement direction, and/or the magnitude of movement with respect to an area with each pixel or a set of a plurality of pixels in the image data. The area calculation unit  120  calculates the movement area by, for example, background subtraction and/or optical flow. 
     The determination unit  130  detects the presence or absence of a moving body that moves in conjunction with another object (e.g., the first object) by using the object information and the movement area information. The determination unit  130  includes an area setting unit  131 . 
     The area setting unit  131  sets a determination area according to the object information. The determination area is area inside which the presence or absence of a moving object is detected. For example, the area setting unit  131  sets the determination area according to the one of the following setting methods:
         The larger the size of the object indicated by the object information, the larger the size of the determination area. ⋅ Based on the position of the object indicated by the object information, a determination area is set as some surrounding area around the position of the object.       

     The position of the determination area with respect to the object is set based on the position of the object indicated by the object information and the movement direction of the object indicated by the tracking result or the like. 
     The determination unit  130  detects the presence or absence of a moving object inside the determination area. For example, the determination unit  130  detects the presence or absence of a moving object by using the movement area located within the determination area from among all the detected movement areas indicated by the movement area information. 
     The determination unit  130  can detect the presence or absence of a moving object by using the movement area information to calculate a score indicating the certainty of movement in conjunction with another object (e.g., a first object). Next, the determination unit  130  detects whether a moving object (a second object) that is moving in conjunction with another object (a first object) is present or absent according to the comparison result between the score and the threshold value. 
     When a score is used such that the larger the score value is, the higher the certainty is, the determination unit  130  determines that there is an object moving in conjunction with another object when the score is larger than the threshold value. When a score is used in which the smaller the score value is, the higher the certainty is, the determination unit  130  determines that there is an object moving in conjunction with another object when the score is equal to or less than the threshold value. 
     In general, the score may be calculated in any way, and for example, one of the following index values may be used as the score:
     (S1) The square measure of the movement area indicated by the movement area information in the determination area;   (S2) The difference between the movement direction indicated by the movement area information in the determination area, and the movement direction of a specific object indicated by the object information (tracking result);   (S3) The difference between the movement amount indicated by the movement area information in the determination area, and the movement amount of a specific object indicated by the object information (tracking result); or   (S4) The value obtained by combining a plurality of indexes (for example, the square measure of (S1) and the inverse of the difference of (S2), or the like)   

     The method for determining the presence or absence of a moving object is not limited to the method using the score as described above. For example, the determination unit  130  may perform a determination by using a learning device (learning model) that receives object information and movement area information from within the determination area and outputs information indicating the presence or absence of a moving object. The learning device is, for example, a neural network and/or an AdaBoost type meta-algorithm. 
     In some examples, the determination unit  130  may determine the presence or absence of a moving object without first setting the determination area. In this case, the determination unit  130  may not need to include the area setting unit  131 . For example, when a learning device is used that has learned not to detect that an object far from the position of an object indicated by the object information is an object moving in conjunction with the specific object, it is possible to determine the presence or absence of the object moving in conjunction with the specific object even if the determination area is not set around the position of the other object. 
     The output control unit  102  controls the output of various information. For example, the output control unit  102  controls processing for outputting the determination result provided by the determination unit  130  to at least one of the display unit  152  and the storage unit  153 . 
     The setting unit  103  sets parameters that determine the processing by each of the above units. For example, according to an instruction from the user, the setting unit  103  sets at least some of the parameters that determine aspects of the detection processing performed by the object detection unit  110 , parameters that determine the calculation processing performed by the area calculation unit  120 , and parameters that determine the determination processing performed by the determination unit  130 . These parameters do not have to be set according to an instruction from a user, and may, in some examples, be set by using, for example, a machine learning model. 
     Settable parameters that may be set by setting unit  103  or otherwise are, for example, the following:
     (P1) Parameters that determine detection processing
       A threshold value for detecting an object (threshold value for comparison with reliability, or the like)   An object detection method to be applied   Applicability of tracking processing   
       (P2) Parameters that determine calculation processing
       The method to be applied (e.g., whether to use background subtraction or optical flow)   
       (P3) Parameters that determine determination processing
       A setting method of the determination area   A threshold value to be compared with the score   The method to be applied (e.g., a method comparing a score to the threshold value or a method using a learning device)   
       

     Each of reception unit  101 , the object detection unit  110 , the area calculation unit  120 , the determination unit  130 , the output control unit  102 , and the setting unit  103  can be implemented by one or a plurality of processors. For example, these various units may be implemented in software by causing a processor, such as a central processing unit (CPU), to execute a program. Each of these units may also or instead be implemented in hardware as a special-purpose processor such as a dedicated integrated circuit (IC). Likewise, these units may be implemented by using software and hardware in combination. When a plurality of processors are used, each processor may implement a single one of the units or may implement two or more of the units. 
     Next, the information processing by the information processing device  100  related to the first embodiment configured in this way will be described with reference to  FIGS. 2 to 4 . The information processing includes detection processing by the object detection unit  110  ( FIG. 2 ), calculation processing by the area calculation unit  120  ( FIG. 3 ), and determination processing by the determination unit  130  ( FIG. 4 ). 
     The detection processing and the calculation processing may be executed in parallel, or one may be executed first and the other may be executed later. The determination processing is executed after the detection processing and the calculation processing have been executed. 
       FIG. 2  is a flowchart showing an example of the detection processing according to the first embodiment. 
     The object detection unit  110  receives image data from the reception unit  101  (step S101). By analyzing the image data, the object detection unit  110  detects a specific object (predetermined first object) in the image data and outputs object information indicating the detection result (step S102). When the tracking unit  111  is provided, the tracking unit  111  executes tracking processing for collating and associating an object detected in the current frame to the detection result of a past frame (step S103). The object detection unit  110  outputs object information including the tracking result (step S104), and ends the detection processing. The object information is sent to the determination unit  130 . 
       FIG. 3  is a flowchart showing an example of the calculation processing according to the first embodiment. 
     The area calculation unit  120  receives a plurality of images (time-series image data) from the reception unit  101  (step S201). The area calculation unit  120  analyzes the image data and calculates a movement area indicating a movement area (portions/regions of the images in which movement is apparent) within the image data (step S202). The area calculation unit  120  outputs the movement area information, which is this calculation result (step S203), and ends the calculation processing. The movement area information is sent to the determination unit  130 . 
       FIG. 4  is a flowchart showing an example of the determination processing according to the first embodiment. 
     The determination unit  130  receives the object information from the object detection unit  110  and the movement area information from the area calculation unit  120  (step S301). The area setting unit  131  sets a determination area based on the object information (step S302). The determination unit  130  detects the presence or absence of an object moving in conjunction with the detected object based on the object information in the determination area and the movement area information (step S303). 
     The determination unit  130  may modify the movement area information according to the relationship between the area of the object and the movement area, and then perform a determination by using the modified movement area information. For example, when a moving object cannot exist inside the area of the object, the determination unit  130  modifies the movement area so as to exclude the movement area overlapping the area of the object or modifies to reduce the movement amount corresponding to the movement area overlapping the area of the object. The amount to be reduced may be, for example, the movement amount of the object. 
     The output control unit  102  outputs the determination result by the determination unit  130  to at least one of the display unit  152  and the storage unit  153  (step S304).  FIG. 5  is a diagram showing an example of a display screen including a determination result output to the display unit  152 . The display screen of  FIG. 5  is an example of a screen when a person is detected as the first object (specific object) and the presence or absence of a piece of cargo being transported by the person is detected as the second object. 
     As shown in  FIG. 5 , the display screen includes image data  501  (which reflects the input image data) and a determination result  502  based on evaluation/analysis of the image data  501 . With respect to the vicinity of each person (person A, person B, person C) detected by the object detection unit  110 , the image data  501  displays determination areas  511 ,  512 ,  513  set by the area setting unit  131 , and an image  521  indicating a moving object (second object) that is determined to be present by the determination unit  130  in an overlapping manner with a detected person (person B). The determination result  502  includes the result of the determination processing for the detected persons A and B. The determination result  502  may include and display the object information or the movement area information, or both the object information and the movement area information, together with the result of the determination processing, on the display screen. The output control unit  102  may display the determination result  502  and the image data  501  in an overlapping manner. 
     In the determination result  502  of the display screen of  FIG. 5 , although the abbreviated result of the determination processing of the person A and the person B is displayed, the result of the determination processing of the person C may be displayed by a screen operation (e.g., scrolling) or the like, and not limited thereto, the results of the determination processing of three or more persons may be displayed all at once. 
     In some examples, only a person who is determined to be transporting cargo (like the person B in  FIG. 5 ) and corresponding information might be displayed as the determination result  502  by the determination unit  130 . Further, the determination result  502  may display the result of the determination processing of the entire image data  501  as summary such as, for example, “three persons are detected, of which one person is transporting cargo” on the display screen. 
     When the determination result can be output to the storage unit  153 , the output control unit  102  stores, for example, the determination result from the determination unit  130  in the storage unit  153  for each detected object. The output control unit  102  may further store one or more of the detected time period, the input data in that time period, and the output result of each unit, in the storage unit  153 . 
     As described above, in the first embodiment, a setting unit  103  for setting various parameters for each unit as necessary is provided.  FIG. 6  is a diagram showing an example of a setting screen used for setting parameters via the setting unit  103 . 
     The setting screen of  FIG. 6  includes the same image data  501  as that of  FIG. 5  and a parameter setting field  602 .  FIG. 6  shows an example in which the reliability (0.7, 0.6, and 0.5) calculated at the time of detection is displayed in association with each person. In the parameter setting field  602 , the parameters used in each processing may be set by the operation of the user or the like. 
     For example, after executing processing by using the parameters previously set, the user changes a portion or all of the parameters via the setting screen as shown in  FIG. 6 . The processing on the image data  501  is then re-executed using the changed (new) parameters. The output control unit  102  displays an updated setting screen to reflect the result of re-execution. 
     The parameters may be changed in real time in parallel with the processing for the image data to be sequentially captured. For example, when a parameter is changed, processing using the updated parameter is used with the image data received thereafter. 
     Next, a specific example of a system to which the first embodiment can be applied will be described. As described above, the information processing system of the first embodiment detects a specific object in the image data, calculates a movement area (moving portions within the image data), and detects the presence or absence of an object moving in conjunction with the specific object by using the movement area information. 
     The information processing system of the first embodiment can be applied to a system for determining a transport status of cargo being moved by a person or a transport device within a warehouse or a cargo loading facility.  FIG. 7  is a diagram showing such a system. The system of  FIG. 7  detects a person  701  as a specific object (first object) and determines the presence or absence of a cargo  702  (piece of cargo; second object) as an object moving in conjunction with the person  701 . The system of  FIG. 7  can also detect a transport device  703  as a specific object (a first object instead of person  701 ) and determine the presence or absence of a cargo  704  (piece of cargo; second object) as an object moving in conjunction with the transport device  703 . 
     The information processing system of the first embodiment can also be applied to a system for determining the boarding status of a person getting on a vehicle (or other moving body that is capable of moving with a person on board).  FIG. 8  is a diagram showing an application example to such a system. The system of  FIG. 8  detects a person  801  and then detects the presence or absence of a vehicle  802  as an object moving in conjunction with the person  801 . 
     As described above, in the information processing system related to the first embodiment, it is possible to detect an object that moves in conjunction with a specific object, such as a person, with higher accuracy. 
     Second Embodiment 
     In the first embodiment, for example, it may be possible to detect that an object is a person but it may not be possible to individually (specifically) identify the person (to identify an individual). Similarly, in the first embodiment, for example, it may be possible to detect that the object is a transport device but it may not be possible to identify an individual type, model, or instance of a transport device. However, the information processing system of the second embodiment further individually identifies the detected first object, and then detects the presence or absence of an object moving in conjunction with the first object by using the specific identification result as well. 
       FIG. 9  is a block diagram showing an example of the configuration of the information processing device  100 - 2  as an information processing system according to the second embodiment. 
     As shown in  FIG. 9 , the information processing device  100 - 2  includes an image capturing unit  151 , a display unit  152 , a storage unit  153 , a reception unit  101 , an object detection unit  110 , an area calculation unit  120 , and a determination unit  130 - 2 , an identification unit  140 - 2 , an output control unit  102 , and a setting unit  103 . 
     In the second embodiment, the identification unit  140 - 2  is added, and the function of the determination unit  130 - 2  (area setting unit  131 - 2 ) is different from that in the first embodiment. Other configurations and functions are the same as those described in conjunction with  FIG. 1 , which is a block diagram of the information processing device  100  related to the first embodiment, and thus the same reference numerals are given, and the description of corresponding aspects will be omitted here. 
     The identification unit  140 - 2  uniquely (specifically) identifies an individual detected by the object detection unit  110  as a first object, and outputs the identification result. For example, the identification unit  140 - 2  extracts individual information with respect to an object detected by the object detection unit  110  based on one or more of appearance information such as shape, color, texture, and operation. 
     The identification unit  140 - 2  uniquely identifies the object by collating the individual information with the previously known (acquired) individual information that is registered in advance or the previously known individual information obtained with respect to the object captured in a past frame. 
     For example, when the specific object is a person such as an employee or worker, individual information for each individual can be stored in advance in the storage unit  153  or the like, and the identification unit  140 - 2  identifies the detected object by collating the specific object with the stored individual information. The identification result by the identification unit  140 - 2  is output to the determination unit  130 - 2 . 
     The determination unit  130 - 2  detects the presence or absence of an object that moves in conjunction with the detected individual by using the identification result from the identification unit  140 - 2  in addition to the object information and the movement area information. For example, the area setting unit  131 - 2  in the determination unit  130 - 2  changes the position that sets the determination area with respect to the object and the size of the determination area according to the identification result. The determination unit  130 - 2  may change the parameters of the determination processing according to the identification result. 
     For example, when the specific object is a person, such additional information such as movement speed (average walking speed, or the like) and the preferred cargo transport method (e.g., in which direction the cargo is placed with respect to the body, or the like) may differ for each individual. The area setting unit  131 - 2  sets, for example, a determination area that is larger with respect to an individual having a relatively fast movement speed than for an individual having a relatively slow movement speed. 
     Furthermore, when the specific object is a transport device, the movement speed and the cargo transport method (cargo loading position, or the like) may differ for each individual (model or type) of transport device. The area setting unit  131 - 2  sets, for example, a determination area larger with respect to a transport device having a relatively fast movement speed than for a transport device having a relatively slow movement speed. 
     Information such as the movement speed may be stored in the storage unit  153 , for example. As described above, according to the second embodiment, since the determination processing by the determination unit  130 - 2  can be executed according to the individual identification result, more accurate determination is possible. 
     Next, the detection processing by the information processing device  100 - 2  related to the second embodiment will be described.  FIG. 10  is a flowchart showing an example of the detection processing according to the second embodiment. 
     The step S401, step S402, and step S403 are substantially the same processing as described for step S101 to step S103 in the information processing device  100  related to the first embodiment, additional description of steps S401 to S403 will be omitted. 
     The identification unit  140 - 2  executes the identification processing on the object detected by the object detection unit  110  and outputs the identification result (step S404). The object detection unit  110  outputs the object information, and the identification unit  140 - 2  outputs the identification result (step S405). 
     Next, the determination processing by the information processing device  100 - 2  related to the second embodiment will be described.  FIG. 11  is a flowchart showing an example of the determination processing in the second embodiment. 
     The determination unit  130 - 2  receives the object information from the object detection unit  110 , the identification result from the identification unit  140 - 2 , and the movement area information from the area calculation unit  120  (step S501). The area setting unit  131 - 2  sets the determination area based on the object information and also the identification information (step S502). The determination unit  130 - 2  detects the presence or absence of an object moving in conjunction with the detected object based on the object information in the determination area, the identification result, and the movement area information (step S503). The output control unit  102  outputs the determination result from the determination unit  130 - 2  to at least one of the display unit  152  and the storage unit  153  (step S504). 
     As described above, in the second embodiment, particular individuals can be detected, and the determination processing can be executed according to the identification result. As a result, the determination of the moving object can be executed with higher accuracy. 
     Modification Example 
     The information processing devices of the first and second embodiments can be, for example, an example of an information processing system implemented by one logical or physical device. However, the described functions of the information processing system may be implemented by a plurality of different logical or physical devices. 
       FIG. 12  is a block diagram showing an example of the configuration of the information processing system related to this modification example that is implemented on a plurality of different devices. As shown in  FIG. 12 , the information processing system of the modification example has a configuration in which an image capturing device  300 - 3 , a client device  200 - 3 , and a server device  100 - 3  are connected by a network  400 - 3 . The aspects that are the same as those described in conjunction with  FIG. 1  are designated by the same reference numerals, and a detailed description thereof will be omitted. 
     The network  400 - 3  may be, for example, the Internet, but may be any other form of network. The network  400 - 3  may be any of a wired network, a wireless network, and a network in which a wired network and a wireless network are mixed. 
     The image capturing device  300 - 3  includes an image capturing unit  151  and a communication control unit  301 - 3 . 
     The communication control unit  301 - 3  controls the communication of information to an external device such as the server device  100 - 3 . For example, the communication control unit  301 - 3  transmits the image data captured by the image capturing unit  151  to the server device  100 - 3 . 
     The client device  200 - 3  includes a communication control unit  201 - 3 , an output control unit  102 , a display unit  152 , and a storage unit  153 . 
     The communication control unit  201 - 3  controls the communication of information to an external device such as the server device  100 - 3 . For example, the communication control unit  201 - 3  receives the determination result of the determination unit  130  from the server device  100 - 3 . The output control unit  102  controls, for example, the processing of outputting the determination result received by the communication control unit  201 - 3  to at least one of the display unit  152  and the storage unit  153 . 
     The server device  100 - 3  includes the reception unit  101 , the object detection unit  110 , the area calculation unit  120 , the determination unit  130 , a communication control unit  104 - 3 , and the setting unit  103 . 
     The communication control unit  104 - 3  controls the communication of information to external devices such as the image capturing device  300 - 3  and the client device  200 - 3 . For example, the communication control unit  104 - 3  receives the image data from the image capturing device  300 - 3  and passes the image data to the reception unit  101 . The communication control unit  104 - 3  transmits the determination result of the determination unit  130  to the client device  200 - 3 . 
     The server device  100 - 3  may be implemented as, for example, a server device constructed on a cloud environment. 
     The distribution the functions shown in  FIG. 12  is an example, and is not limited thereto. For example, the image capturing device  300 - 3  may incorporate all of the functions of the object detection unit  110 , or a portion of the functions of the object detection unit  110  (for example, up to a function of extracting feature data, or the like). For example, although the setting unit  103  is provided in the server device  100 - 3  here, it may instead be provided in the client device  200 - 3 . Further, although  FIG. 12  shows an example that is implemented by distributing the functions of the information processing system to three devices, the information processing system may be distributed to two or four or more devices. 
     As described above, according to the first to second embodiments, it is possible to determine with higher accuracy the presence or absence of an object (moving object) that operates in conjunction with a specific object. 
     Next, the hardware configuration of certain devices (e.g., an information processing device, a client device, a server device) related to the first and/or second embodiment will be described with reference to  FIG. 13 .  FIG. 13  is a schematic diagram showing a hardware configuration example of devices related to the first and second embodiment. 
     The devices related to the first or second embodiment includes a control device (controller) such as a CPU  51 , a storage device (such as a read only memory (ROM)  52  or a RAM  53 ), a communication I/F  54  that connects to a network to perform communication, and a bus  61  that connects to each sub-unit. 
     The program (s) executed by the device (s) related to the first or second embodiment can be provided by being incorporated in the ROM  52  in advance. 
     The program (s) executed by the device (s) related to the first or second embodiment may be provided in an installable format or an executable format. Such file(s) can be recorded on a non-transitory computer-readable recording medium such as a compact disk read only memory (CD-ROM), a flexible disk (FD), a compact disk recordable (CD-R), and a digital versatile disk (DVD), or stored on a computer connected to a network such as the Internet, and downloaded via the network. The program(s) executed by the device (s) related to the first or second embodiment may be provided or distributed via a network such as the Internet. 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the disclosure. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the disclosure. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the disclosure.