Patent Publication Number: US-11037302-B2

Title: Motion video segmenting method, motion video segmenting device, and motion video processing system

Description:
TECHNICAL FIELD 
     The present disclosure relates to a motion video segmenting method, a motion video segmenting device, and a motion video processing system that perform processing on a motion video such as a sports video. 
     BACKGROUND ART 
     In recent years, tracking of position information of a player or a ball through a game is performed, a posture analysis, a behavior analysis, tracking of a position, and the like for each player are performed, and they are fed back to tactics of training or a next game for example, based on a sports motion video such as soccer. 
     In a case where a person manually analyzes many images configuring a motion video, much labor, time, or hand is required. Therefore, for example, NPL 1 discloses a sports video analysis system in which image analysis processing is automatically performed by a computer and in a case where the computer is erroneous, correction is accepted by the hand of a person, so that the analysis can be immediately performed and labor of a person can be reduced. 
     CITATION LIST 
     Non Patent Literature 
     
         
         NPL 1: Masamoto Tanabiki, and five others, “Sports Video Analysis Solution”, Panasonic Technical Journal Vol. 61 No. 2 November 2015, p. 78-83 
       
    
     SUMMARY OF THE INVENTION 
     In the technique disclosed in NPL 1, the motion video is segmented into files at predetermined times, image analysis is performed on each file which is segmented by the computer, corrections are performed by a plurality of correctors to each segmented file for which the image analysis is performed, and then the segmented files are joined. The image analysis performed by the computer includes, for example, a process of tracking positions of a player, a ball, and the like, a process of detecting a posture of the player, and the like. In the technique disclosed in NPL 1, times required for the image analysis processing and the correction thereof are reduced by such an operation. 
     However, for example, in an image in which the players are densely imaged, it is more difficult for the computer to specify the position of the player or the ball by the image analysis than an image in which the players are scatteredly imaged, and there are cases where an error of a position specified result by the computer increases. The image in which the players are densely imaged may be continuous for several frames to several tens of frames. In NPL 1, since the motion video is segmented at a predetermined time, the motion video may be segmented before and after the image in which the image analysis is difficult. 
     In NPL 1, in the segmented files, information of the positions of the player and the ball are overlapped as a result of the image analysis by the computer and the correction by the corrector. When the segmented files are joined, the same player is specified and the position information of the same player is connected before and after a joining place. Therefore, in a case where images before and after the joining are images that are difficult to be the image analysis by the computer, that is, are images in which the players are densely imaged, there is a problem that the accuracy of joining of a tracking result in which (1) the accuracy of the position information before and after the joining place decreases and (2) the accuracy of specifying the same player at the time of joining. That is, in the related art, there is a problem that the accuracy of the motion video analysis which is performed by segmenting the motion video decreases. 
     The present disclosure provides a motion video segmenting method, a motion video segmenting device, and a motion video processing system that increase accuracy of motion video analysis performed by segmenting a motion video. 
     The present disclosure provides a motion video segmenting method in which a processor segments images of a plurality of frames configuring a motion video into a plurality of groups, the method including: by the processor, determining a difficulty degree of performing an editing operation of the images based on a feature amount obtained from the image; and determining a place at which the images of the plurality of frames configuring the motion video are segmented into the plurality of groups based on the calculated difficulty degree of the image. 
     According to the disclosure, it is possible to increase the accuracy of motion video analysis performed by segmenting the motion video. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating an example of a configuration of a motion video processing system. 
         FIG. 2  is a diagram illustrating an example of a configuration of a motion video segmenting device (or an image processing device). 
         FIG. 3  is a sequence diagram for explaining a flow of an overall operation of the motion video processing system in Embodiment 1. 
         FIG. 4  is a view illustrating an example of a screen displayed on a display unit in a setting process of a background region. 
         FIG. 5  is a flowchart for explaining an operation of the motion video segmenting device in a segmenting transmission process of Embodiment 1. 
         FIG. 6  is a flowchart for explaining an operation of the motion video segmenting device in a pack forming process. 
         FIG. 7  is a flowchart for explaining an operation of the motion video segmenting device in a difficulty degree determination process. 
         FIG. 8  is a diagram illustrating a first example of a setting method of a load value. 
         FIG. 9  is a diagram illustrating a second example of a setting method of a load value. 
         FIG. 10  is a diagram for explaining load value accumulation information. 
         FIG. 11  is a sequence diagram for explaining a whole operation of a motion video processing system in Embodiment 2. 
         FIG. 12  is a flowchart for explaining an operation of the motion video segmenting device in a segmenting transmission process of Embodiment 2. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments will be appropriately described with reference to the drawings. However, detailed description may be omitted more than necessary. An unnecessarily detailed explanation is, for example, a detailed explanation of already well known matters, a duplicate explanation for substantially the same configuration, or the like. This is to avoid the unnecessary redundancy of the following description and to facilitate understanding by those skilled in the art. 
     It should be understood that the following description and the drawings referred to are provided to enable those skilled in the art to understand the present disclosure and thereby they are not intended to limit the gist described in the claims. 
     Embodiment 1 
     [1.1 Configuration] 
       FIG. 1  is a diagram illustrating an example of a configuration of motion video processing system  1 . As illustrated in  FIG. 1 , motion video processing system  1  includes motion video segmenting device  100 , a plurality of image processing devices  200 A,  200 B,  200 C, . . . , and camera  300 . Motion video segmenting device  100  and camera  300 , and motion video segmenting device  100  and the plurality of image processing devices  200 A,  200 B,  200 C, are connected so as to be capable of communicating to each other. Hereinafter, the plurality of image processing devices  200 A,  200 B,  200 C, may be described as image processing device  200 . 
     Motion video segmenting device  100  and image processing device  200  are configured by, for example, computers such as PCs and workstations.  FIG. 2  is a diagram illustrating an example of a configuration of motion video segmenting device  100  (or image processing device  200 ). As illustrated in  FIG. 2 , motion video segmenting device  100  (or image processing device  200 ) includes processor  101  ( 201 ), storage unit  102  ( 202 ), input unit  103  ( 203 ), display unit  104  ( 204 ), communication unit  105  ( 205 ), and bus  106  ( 206 ). 
     Processor  101  ( 201 ) performs calculation so as to control configuration elements other than motion video segmenting device  100  (or image processing device  200 ). 
     Storage unit  102  ( 202 ) temporarily or permanently stores information. Storage unit  102  ( 202 ) corresponds to a Read Only Memory (ROM), a Random Access Memory (RAM), or the like of motion video segmenting device  100  (or image processing device  200 ). In the example illustrated in  FIG. 2 , motion video segmenting device  100  (or image processing device  200 ) has only one storage unit  102  ( 202 ), but may have a plurality of storage units  102  ( 202 ) if necessary. Storage unit  102  ( 202 ) may be configured of, for example, a Hard Disk Drive (HDD), a Synchronous Dynamic Random Access Memory (SDRAM), a Solid State Drive (SSD), or the like. 
     Input unit  103  ( 203 ) accepts an input from an outside. Input unit  103  ( 203 ) may be configured by, for example, an input device such as a mouse, a keyboard, or a trackball. 
     Display unit  104  ( 204 ) displays information to the outside. Display unit  104  ( 204 ) may be configured by, for example, a display device such as a liquid crystal display or an organic EL display. 
     Communication unit  105  ( 205 ) transmits and receives information to and from the outside. Communication unit  105  ( 205 ) may be configured by, for example, a communication interface such as a communication port or a wireless communication device. Motion video segmenting device  100  can transmit and receive information to and from image processing device  200  via communication unit  105 . In addition, image processing device  200  can transmit and receive information to and from motion video segmenting device  100  via communication unit  205 . 
     Bus  106  ( 206 ) is a path for connecting each element configuring motion video segmenting device  100  (or image processing device  200 ). Bus  106  ( 206 ) can be configured inside processor  101  ( 201 ) by combining bus  106  ( 206 ) with processor  101  ( 201 ). Bus  106  ( 206 ) may be connected by wire to each element or may be connected wirelessly to each element. 
     The configuration of motion video segmenting device  100  (or image processing device  200 ) which is described above is an example. Therefore, motion video segmenting device  100  (or image processing device  200 ) may be configured by adding another configuration element to the configuration described above. In addition, motion video segmenting device  100  (or image processing device  200 ) may be configured by deleting a part of a configuration element from the configuration described above if necessary. In addition, motion video segmenting device  100  (or image processing device  200 ) may be configured by combining the configuration elements described above with each other. In addition, motion video segmenting device  100  (or image processing device  200 ) may be configured by a part of the configuration elements described above. 
     Camera  300  photographs an object to be photographed, generates an image, and transmits the image to motion video segmenting device  100 . In motion video processing system  1  of the disclosure, the image photographed by camera  300  is a motion video, for example, including 30 frames per second, that is, 30 still images per one second. 
     [1.2 Operation] 
     Next, an operation of motion video processing system  1  in Embodiment 1 will be described. In the following description, the operation of motion video segmenting device  100  is mainly performed by processor  101  in cooperation with respective other configuration elements of motion video segmenting device  100 . 
     Motion video processing system  1  tracks a position of a player, a sports equipment (ball or the like), or the like reflected in the motion video based on a sports video configured by the motion video of the sports event photographed by camera  300 . In the disclosure, for example, the sports event is supposed to be one in which a plurality of players play at the same time, such as soccer or American football but, for example, may be one in which one player plays at one time such as golf. 
     The sports video may be configured by a motion video which is photographed by one camera  300 , or may be configured by joining motion videos photographed by a plurality of cameras  300 . Particularly, in a case where a game with a wide field such as soccer is photographed, it is desirable to join the motion videos photographed by the plurality of cameras  300 . A technique for combining the motion videos is not particularly limited in the disclosure. 
       FIG. 3  is a sequence diagram for explaining a flow of an overall operation of motion video processing system  1  in Embodiment 1. 
     In step S 301 , motion video segmenting device  100  performs a preliminary process for segmenting the sports video. The preliminary process is processing for setting a region corresponding to the background in advance (for example, before the game starts) and acquiring background information in order to distinguish the player and the background (for example, a stadium or the like), for example, in the motion video. Details of the preliminary process will be described later. 
     In step S 302 , motion video segmenting device  100  segments the motion video into a plurality of pieces using information acquired in the preliminary process in step S 301  and performs a segmenting transmission process which is transmitted to a plurality of image processing devices  200 . Details of the segmenting transmission process will be described later. 
     In step S 303 , each of the plurality of image processing devices  200  performs a tracking process for tracking the position of the player, the ball, or the like reflected in the motion video on the segmented motion video received from motion video segmenting device  100 , and an edition acceptance process for accepting an editing operation of an error in the tracking process performed by image processing device  200 . The tracking process in image processing device  200  can use, for example, a moving body tracking technique of the related art. 
     The editing operation of an error in the tracking process is, for example, as follows. In a state where players are densely moving in the motion video, or the like, in the moving body tracking technique of the related art, for example, it is possible to be a situation in which two players who move adjacently are confused or the foot of the player is misidentified as a ball. When such a situation occurs, normal tracking of the player cannot be performed, so that it is necessary for the eyes of a person to check a result of the tracking process and perform correction. The edition acceptance process is a process of accepting the editing operation by a person (hereinafter, referred to as a corrector) who views a result of the tracking process. Specifically, for example, image processing device  200  displays the result of the tracking process on display unit  204  in almost real time and causes the corrector to determine whether or not the tracking process is incorrect. In a case where the tracking process is incorrect, the corrector accepts the correction via input unit  203 . Image processing device  200  performs the edition acceptance process as described above. 
     In step S 304 , image processing device  200  transmits the result (one in which the editing operation is reflected) of the tracking process in step S 303  to motion video segmenting device  100 . 
     In step S 305 , motion video segmenting device  100  joins the tracking results. Motion video segmenting device  100  joins the tracking results by comparing the position (obtained by referring to the tracking result) of the player in an end frame and the position (obtained by referring to the tracking result or an initial posture assignment result) of the player in a start frame of each segmented motion video (pack) which is transmitted from image processing device  200  in step S 304 , and considering players who are present in close positions as the same player. 
     With such an operation, motion video processing system  1  can perform the tracking process of the player, the ball, or the like in the sports video with high accuracy in a short time. 
     [1.2.1 Preliminary Process] 
     Hereinafter, the preliminary process performed by motion video segmenting device  100  in step S 301  of  FIG. 3  will be described in detail. The preliminary process includes an extraction process of background information and a setting process of a difficulty degree determination region. Hereinafter, the extraction process of the background information and the setting process of the difficulty degree determination region will be described individually. 
     (1) Extraction Process of Background Information 
     A background region is a region in which one other than the player or the ball is reflected in still images configuring the motion video of the sports video. That is, the background region is, for example, a region in which a stadium in which the sports event is performed is reflected. 
     In the extraction process of the background information, first, motion video segmenting device  100  acquires an image in which the stadium of the sports which is a target of the sports video photographed therefrom is reflected from camera  300 , and displays the image on display unit  104 . 
       FIG. 4  is a view illustrating an example of a screen displayed on display unit  104  in the extraction process of the background information.  FIG. 4  is a screen displaying an image of field  400  photographed before game starts as an example of the sports event. 
     As illustrated in  FIG. 4 , in the preliminary process, it is desirable that the screen displayed on display unit  104  is an image where there are a few players or the players are not scattered throughout the stadium in order to set the background region in which the player or the like is not reflected. Therefore, the preliminary process is performed, for example, immediately before the game starts. 
     At the start of the preliminary process, motion video segmenting device  100  displays the image of the stadium on display unit  104  and then displays an instruction message of an effect that an operator selects the background region. When the operator who views the instruction message performs a selection operation to select the background region via input unit  103  for the image of the stadium displayed on display unit  104 , motion video segmenting device  100  accepts a selection operation. Therefore, motion video segmenting device  100  sets the background region. For example, the operator is a person who performs an operation in the preliminary process while viewing display unit  104 . 
     In  FIG. 4 , regions  401  surrounded by solid lines illustrate the background regions selected by the operator. The operator designates region  401  illustrated in  FIG. 4  using, for example, an operation device such as a mouse, a trackball, or a touch panel configuring input unit  103 . When the instruction message of the effect that the background region is selected is displayed to the operator, motion video segmenting device  100  instructs the operation to set a region, which does not include an object that may move during the game such as the player or the ball, as the background region. Motion video segmenting device  100  can set region  401  in which the player, the ball, or the like is not included as the background region by following the instruction by the operator. In  FIG. 4 , since a person or the like is present in the image of the stadium, region  401  is designated by the operator so as to avoid the person. In a case where the person or the like is not present in the image of the stadium, the whole field may be designated as region  401 . 
     Motion video segmenting device  100  extracts information included in each pixel of a region for each region  401  set by the operator and stores the information in storage unit  102  as the background information. One which is extracted by motion video segmenting device  100  as the background information is, for example, a range of a hue value obtained by region  401 . Motion video segmenting device  100  may extract not only the hue but also a value of saturation and/or a value as the background information. 
     (2) Setting process of Difficulty Degree Determination Region 
     Motion video segmenting device  100  performs the setting process of the difficulty degree determination region in addition to the extraction process of the background information described above. The difficulty degree determination region is a region which is set to determine whether or not the difficulty degree of the editing operation is high in the edition acceptance process for accepting the editing operation performed by the corrector on the result of the tracking process performed by image processing device  200 . The difficulty degree determination region of the embodiment corresponds to a specific region of the disclosure. 
     In the preliminary process, the order in which the extraction process of the background information and the setting process of the difficulty degree determination region which are described above are performed is not limited by the disclosure. That is, the extraction process of the background information may be performed first, the setting process of the difficulty degree determination region may be performed first, or the extraction process of the background information and the setting process of the difficulty degree determination region may be performed at the same time. 
     Whether or not the difficulty degree of the editing operation is high may be determined, for example, based on a feature amount extracted from the image (still image) of each frame configuring the motion video. The feature amount is an element indicating a feature of the player in the image. As a specific example of the feature amount, for example, a color, a luminance, a position of a vertex and a shape, or the like of the image corresponding to the player is provided. In addition, another known feature amount may be used as long as it indicates the feature of the player in the image. Hereinafter, for example, a case where a rate occupied by a region corresponding to the player to the whole image is adopted as the feature amount extracted from the image will be described. 
     In the setting process of the difficulty degree determination region, motion video segmenting device  100  displays an instruction message to the operator on display unit  104 . The instruction message indicates that the difficulty degree determination region is to be set to the operator. When the operator who views the instruction message performs the selection operation for selecting the difficulty degree determination region via input unit  103  for the image of the stadium displayed on display unit  104 , motion video segmenting device  100  accepts the selection operation thereof. Therefore, motion video segmenting device  100  sets the difficulty degree determination region. 
     Specifically, the operator designates region  402 , for example, illustrated in  FIG. 4  using the operation device such as a mouse, a trackball, or a touch panel configuring input unit  103 . When the instruction message, indicating that the difficulty degree determination region is to be set, is displayed to the operator, for example, motion video segmenting device  100  instructs the operator to set a region, in which many players may be present during the game, as the difficulty degree determination region. Motion video segmenting device  100  can set region  402  in which many players may be present during the game as the difficulty degree determination region by following the instruction by the operator. 
     In  FIG. 4 , region  402  surrounded by a dotted line illustrates the difficulty degree determination region which is selected by the operator. In  FIG. 4 , as an example, a case where a vicinity of a penalty area in a soccer field is selected as the difficulty degree determination region is illustrated. The vicinity of the penalty area is a region where it is easy to play involved in scoring and for the players to be more likely to be densely populated in a case where the ball is in the penalty area, so that it is desirable that the region is set as the difficulty degree determination region. In the disclosure, the difficulty degree determination region is not limited to the vicinity of the penalty area. For example, another region of the field may be selected and set as the difficulty degree determination region and in a case of another sports event, a region, where the players are more likely to be densely populated and which is specific to the game, may be selected and set as the difficulty degree determination region. 
     Region  402  which is selected by the operator as the difficulty degree determination region may be not only one but also plural. 
     Motion video segmenting device  100  stores a position of the region in storage unit  102  as position information of the difficulty degree determination region for each region  402  set by the operator. The position information of the difficulty degree determination region may be, for example, information in which any coordinate system is set in the still image and which includes a coordinate indicating the position of the difficulty degree determination region in the coordinate system, or the like. 
     [1.2.2 Segmenting Transmission Process] 
     Next, the segmenting transmission process in step S 302  of  FIG. 3  will be described in detail.  FIG. 5  is a flowchart for explaining an operation of motion video segmenting device  100  in the segmenting transmission process of Embodiment 1. 
     In step S 501 , motion video segmenting device  100  performs a pack forming process on the motion video photographed by camera  300 . The pack forming process is a process of segmenting the motion video and forming a plurality of groups (hereinafter, referred to as packs). 
     [1.2.2.1 Pack Forming Process] 
     Hereinafter, details of the pack forming process will be described.  FIG. 6  is a flowchart for explaining an operation of motion video segmenting device  100  in a pack forming process. A processing target of the pack forming process illustrated in  FIG. 6  is each still image configuring the motion video input from camera  300 . 
     In step S 601 , motion video segmenting device  100  performs initialization of parameters used in the pack forming process. The parameters used in the pack forming process are the number of frames f, count value cnt, and difficulty degree D_level of the still image corresponding to the number of frames f. The number of the frames is set to f=0, count value cnt=0, and the difficulty degree is set to D_level=Low by initialization of step S 601 . 
     In step S 602 , motion video segmenting device  100  acquires the still image of f th  frame of the motion video input from camera  300 . The still image of the f th  frame is a still image of the processing target in each step described below. In step S 603 , motion video segmenting device  100  extracts a foreground region from the still image acquired in step S 602 . 
     The foreground region is a region other than the background region in the still image. That is, in step S 603 , motion video segmenting device  100  extracts a foreground image based on the background information extracted in the preliminary process which is described above. Specifically, information of the hue or the like extracted as the background information is compared to information of the hue of each pixel in the still image, so that motion video segmenting device  100  extracts an image configured of pixels having large divergence as a result of the comparison as the foreground image. Motion video segmenting device  100  generates a binarized image in which a pixel value of the pixels configuring the background region is set to 0 (black) and a pixel value of the pixels configuring the foreground region is set to 255 (white) respectively in the still image of the f th  frame. Hereinafter, the binarized image generated by motion video segmenting device  100  in step S 603  is referred to as the foreground image. 
     In step S 604 , motion video segmenting device  100  performs a labeling process using the foreground image generated in step S 603 . The labeling process is a process in which the same number is allocated to continuous pixels of which a pixel value is 255 (white). That is, different numbers are assigned to respective independent foreground regions by the labeling process. In the following description, the foreground region in which the labeling process is performed is referred to as a labeling region and the number of the labeling regions in the foreground image is referred to as a labeling number. In step S 604 , motion video segmenting device  100  generates position information of the labeling region relating to the position of the labeling region and stores the position information in storage unit  102 . 
     In step S 605 , motion video segmenting device  100  performs the difficulty degree determination process. The difficulty degree determination process is a process of determining whether or not the difficulty degree of the editing operation is high in the edition acceptance process of accepting the editing operation performed by the corrector with respect to the result of the tracking process performed by image processing device  200 . 
     [1.2.2.1.1 Difficulty Degree Determination Process] 
     Hereinafter, details of the difficulty degree determination process will be described.  FIG. 7  is a flowchart for explaining an operation of motion video segmenting device  100  in the difficulty degree determination process. 
     In step S 701 , motion video segmenting device  100  acquires the number of labeling L_num determined by the labeling process of step S 604  in the still image of the f th  frame that is the processing target. 
     In step S 702 , motion video segmenting device  100  performs initialization of the parameters using in the difficulty degree determination process. The parameters using in the difficulty degree determination process are counter value x indicating what number of the labeling region in the still image, foreground amount D_size in the difficulty degree determination region, and foreground amount ALL_size of the whole still image. Moreover, the foreground amount is an amount indicating a size of the foreground region, specifically, for example, the number of pixels. 
     In step S 703 , motion video segmenting device  100  acquires size S_x of an x th  labeling region. In step S 704 , motion video segmenting device  100  adds size S_x of the x th  labeling region to foreground amount ALL_size of the whole still image. 
     In step S 705 , motion video segmenting device  100  determines whether or not the x th  labeling region is within the difficulty degree determination region. The determination in step S 705  may be performed based on the position information of the difficulty degree determination region and the position information of the labeling region. In a case where it is determined that the x th  labeling region is within the difficulty degree determination region in step S 705  (step S 705 : Yes), the procedure proceeds to step S 706  and otherwise (step S 705 : No), the procedure proceeds to step S 707 . 
     In step S 706 , motion video segmenting device  100  adds size S_x of the x th  labeling region to foreground amount D_size within the difficulty degree determination region. The procedure proceeds to step S 707 . 
     In step S 707 , motion video segmenting device  100  increments counter value x by 1 and the procedure proceeds to step S 708 . 
     In step S 708 , motion video segmenting device  100  determines whether or not counter value x reaches the number of labeling L_num. In a case where it is determined that counter value x does not reach the number of labeling L_num in step S 708  (step S 708 : Yes), the process returns to step S 703  and otherwise (step S 708 : No), the procedure proceeds to step S 709 . 
     In step S 709 , motion video segmenting device  100  determines whether or not foreground amount D_size within the difficulty degree determination region is larger than a value obtained by multiplying foreground amount ALL_size of the whole still image by predetermined rate “rate”. Predetermined rate “rate” is a preset rate for determining whether or not the difficulty degree of the editing operation is high and is, for example, 60%. In a case where it is determined that foreground amount D_size within the difficulty degree determination region is larger than the value obtained by multiplying foreground amount ALL_size of the whole still image by predetermined rate “rate” in step S 709  (step S 709 : Yes), the procedure proceeds to step S 710  and otherwise (step S 709 : No), the procedure proceeds to step S 711 . 
     In step S 710 , motion video segmenting device  100  determines that difficulty degree D_level of the still image of the f th  frame which is the processing target is High. 
     On the other hand, in step S 711 , motion video segmenting device  100  determines that difficulty degree D_level of the still image of the f th  frame is Low. 
     That is, in the difficulty degree determination process illustrated in  FIG. 7 , in the still image of the f th  frame, in a case where foreground amount D_size within the difficulty degree determination region is larger than the value obtained by multiplying foreground amount ALL_size of the whole still image by predetermined rate “rate”, since it is assumed that there are many players in the image, it is determined that the difficulty degree is high. 
     Returning to the explanation of  FIG. 6 . In step S 606 , motion video segmenting device  100  determines whether or not difficulty degree D_level is High in the difficulty degree determination process of step S 605 . In step S 606 , in a case where it is determined that difficulty degree D_level is High (step S 606 : Yes), the procedure proceeds to step S 607  and otherwise (step S 606 : No), the procedure proceeds to step S 609 . 
     In step S 607 , motion video segmenting device  100  updates difficulty degree D_level of the still image of the f th  frame to High. In step S 608 , motion video segmenting device  100  resets count value cnt to 0 and the process proceeds to step S 610 . 
     In step S 609 , motion video segmenting device  100  increments count value cnt by 1 and the procedure proceeds to step S 610 . 
     In step S 610 , motion video segmenting device  100  determines whether or not the number of the frames f of the still image of the processing target is predetermined number F (for example, F=750 frames) or more. In step S 610 , in a case where it is determined that the number of the frames f is predetermined number F or more (step S 610 : Yes), the procedure proceeds to step S 612  and otherwise (step S 610 : No), the procedure proceeds to step S 611 . 
     In step S 611 , motion video segmenting device  100  increments the number of frames f by 1 and the process returns to step S 602 . 
     In step S 612 , motion video segmenting device  100  determines whether or not count value cnt is predetermined value F_cnt or more. In step S 612 , in a case where it is determined that count value cnt is predetermined value F_cnt (for example, 10) or more (step S 612 : Yes), the procedure proceeds to step S 613  and otherwise (step S 612 : No), the process returns to step S 611 . 
     In steps S 608  and S 609 , count value cnt is reset to 0 in a case where difficulty degree D_level of the still image of the processing target is updated to High, and is incremented by 1 in a case where difficulty degree D_level is Low. That is, only in a case where the still image of which difficulty degree D_level is Low continues for F_cnt frame or more, in step S 612 , count value cnt is predetermined value F_cnt or more. Therefore, a process of step S 613  is performed in a case where the number of frames f of the still image of the processing target is predetermined number F or more from the start of the pack forming process and the still image of which difficulty degree D_level is Low continues F_cnt frame or more. 
     In the embodiment, as described above, it is determined whether or not the number of continuous still images of which the difficulty degree is Low is a predetermined value or more by using the count control, and then the motion video is segmented, but the configuration is not indispensable. For example, in a case where it is determined that the difficulty degree of the frame of the processing target is Low, the motion video may be segmented by the frame. Even in this case, since any of the still images before and after the segmentation of the motion video has the difficulty degree in Low, the accuracy of the image analysis improves before or after of the segmentation of the motion video, and the accuracy of the motion video analysis which is performed by segmenting the motion video can be increased. 
     However, if it is determined whether or not the number of continuous still images of which the difficulty degree is Low is a predetermined value or more by using the count control, and then the motion video is segmented as described in the embodiment, the difficulty degree of the still images before and after the segmentation of the motion video tends to be continuously Low for a predetermined number or more, because it is empirically known that the still images of which the difficulty degree is Low continue for a while after the segmentation. When the still images of which the difficulty degree is Low continue, since the accuracy of the image analysis before and after the segmentation of the motion video further improves, the accuracy of the motion video analysis performed by segmenting the motion video can be further increased. 
     In step S 613 , motion video segmenting device  100  performs pack formation as one group (pack) from the still image of a 0 th  frame which is the start frame of the pack forming process to the still image of the f th  frame. A length of the pack formed in step S 613  is predetermined number of frames F or more. In addition, the difficulty degree of the still image of an end frame of the pack is Low. 
     In the embodiment, as described above, the pack formation is performed so that the difficulty degree of the still image of the end frame of the pack is Low. In this way, the accuracy of the image analysis on the still image of the end frame improves, so that the accuracy of the motion video analysis performed by segmenting the motion video can be further increased. 
     In addition, as another embodiment, pack formation is performed as one group (pack) from the still image of a 0 th  frame to the still image of (f-a predetermined number-th frame), so that the difficulty degree of the still image of the start frame of the pack may be Low. In this way, the accuracy of the image analysis on the still image of the start frame improves, so that the accuracy of the motion video analysis performed by segmenting the motion video can be further increased. 
     In addition, as another embodiment, pack formation is performed as one group (pack) from the still image of the 0 th  frame to the still image of f-1 st  frame, so that the difficulty degree of the still images of both the start frame and the end frame of the pack may be Low. 
     In step S 614 , motion video segmenting device  100  calculates a load value of the pack formed in step S 613 . The load value is a degree indicating a load when the corrector performs the editing operation of the pack in image processing device  200 . 
     A setting method of the load value is specifically described. As one setting method, for example, there is a method of setting the load value based on a rate occupied by the still image in which difficulty degree D_level is High in the still images included in the pack. 
       FIG. 8  is a diagram illustrating a first example of a setting method of the load value. In  FIG. 8 , columns on a left indicate rates of the still image in which the difficulty degree D_level is High to the whole pack and columns on a right indicate examples of the load value according to the rates. In the example illustrated in  FIG. 8 , in a case where the rate of the number of the frames of the difficulty degree of High to the whole pack is 0, the load value is set to 1. In addition, in a case where the rate of the number of the frames of the difficulty degree of High to the whole pack is larger than 0 and less than 0.25, the load value is set to 2. In addition, in a case where the rate of the number of the frames of the difficulty degree of High to the whole pack is 0.25 or larger and less than 0.5, the load value is set to 3. In addition, in a case where the rate of the number of the frames of the difficulty degree of High to the whole pack is 0.5 or larger and less than 0.75, the load value is set to 4. In addition, in a case where the rate of the number of the frames of the difficulty degree of High to the whole pack is 0.75 or larger and 1.0 or less, the load value is set to 5. 
     That is, in the first example of the setting method of the load value, the lower the rate of the number of the frames of the difficulty degree of High to the whole pack is, the lower the load value is set, and higher the rate is, higher the load value is set. Moreover, in the example illustrated in  FIG. 8 , the load value is set to five stages, but the disclosure is not limited thereto. For example, the load value may have stages less than 5 stages, such as two stages, or may have stages larger than 5 stages illustrated in  FIG. 8 . In a case where the load value has 2 stages, for example, in a case where the frame of the difficulty degree of High is one, the load value may be 1, and in a case where the number of the frames of the difficulty degree of High is 0, the load value may be 0. 
       FIG. 9  is a diagram illustrating a second example of a setting method of a load value. In  FIG. 9 , columns on a left indicate rates of the number of the frames of the whole pack to a predetermined number of frames and columns on a right indicate examples of the load value according to the rates. Moreover, the predetermined number of frames may be the same as the predetermined number of frames F described in step S 610  of  FIG. 6 , or may be different therefrom. In the example illustrated in  FIG. 9 , in a case where the number of the frames of the whole pack is 1 time or less of the predetermined number of the frames, the load value is set to 1. In addition, in a case where the number of the frames of the whole pack is 1 time or more and 1.5 time or less of the predetermined number of the frames, the load value is set to 2. In addition, in a case where the number of the frames of the whole pack is 1.5 time or more and 2 time or less of the predetermined number of the frames, the load value is set to 3. In addition, in a case where the number of the frames of the whole pack is 2 time or more of the predetermined number of the frames, the load value is set to 4. 
     In the second example of the setting method of the load value, the lower the rate of the number of the frames of the whole pack to the predetermined number of the frames is, that is, the lower the length of the pack is, lower the load value is set and higher the rate is set, that is, the longer the length of the pack is, the higher the load value is set. Moreover, in the example illustrated in  FIG. 9 , the load value is set to 4 stages, but the disclosure is not limited thereto. For example, the load value may have stages less than 4 stages, such as 2 stages, or may have stages larger than 4 stages illustrated in  FIG. 9 . 
     As described above, motion video segmenting device  100  segments the motion video into the plurality of the packs including the still images of the predetermined number of the frames or more. More specifically, motion video segmenting device  100  determines the difficulty degree of each image configuring the pack, and in a case where the image of the difficulty degree of Low continues predetermined value F_cnt or more, the motion video is segmented and the pack is formed in a vicinity of a last image of the continuous images. 
     Returning to the explanation of  FIG. 5 . In step S 502 , motion video segmenting device  100  acquires the load value accumulation information for each of the plurality of image processing devices  200 , which are candidates of the transmission destination of the plurality of the packs formed in step S 501 . 
       FIG. 10  is a diagram for explaining the load value accumulation information. In  FIG. 10 , identification information (ID) of image processing device  200  that is the candidate of the transmission destination of the pack and tables  1001  to  1004  indicating the load value accumulation information are illustrated. The load value accumulation information in the tables  1001  to  1004  is updated with a lapse of time. 
     The table  1001  indicates a load value in an initial state in which no pack is transmitted to any one of image processing devices  200  of ID. 1 to ID. 3. An initial value of the load value is 1 and the load value accumulation information of each of three image processing devices  200  of ID. 1 to ID. 3 is 1. Hereinafter, for simplicity, for example, the load value accumulation information of image processing device  200  of ID. 1 is described like the load value of ID. 1. 
     The table  1002  indicates a load value in a state where time elapses from the table  1001  and a first pack having the load value of 3 is transmitted. Since the pack of the load value of 3 is transmitted to ID. 1, in the table  1002 , the load value of ID. 1 is updated to 4. 
     Furthermore, the table  1003  indicates a load value in a state where time elapses from the table  1002  and a second pack having the load value of 1 is transmitted. Since the pack of the load value of 1 is transmitted to ID. 2, in the table  1003 , the load value of ID. 2 is updated to 2. 
     The table  1004  indicates a load value in a state where time elapses from the table  1003  and a third pack having the load value of 2 is transmitted. Since the pack of the load value of 2 is transmitted to ID. 3, in the table  1004 , the load value of ID. 3 is updated to 3. 
     As described above, the load value accumulation information is a value obtained by accumulating the load value of the pack transmitted to any one of the plurality of image processing devices  200  before the current time for each image processing device  200 . 
     Returning to the explanation of  FIG. 5 . In step S 503 , motion video segmenting device  100  determines image processing device  200  which is the transmission destination to which the pack generated in step S 501  is transmitted based on the load value accumulation information acquired in step S 502 . 
     As a method of determining the transmission destination, for example, there is a method of referring the load value accumulation information acquired in step S 502  and setting image processing device  200  having a smallest accumulation value of the load value to the transmission destination. In addition, in a case where the plurality of image processing devices  200  having the same load value accumulation information are present such as a state where the pack is not transmitted, for example, image processing device  200  having the smallest ID may be determined as the transmission destination. 
     In step S 504 , motion video segmenting device  100  transmits the pack generated in step S 501  to the transmission destination determined in step S 503 . 
     In step S 505 , motion video segmenting device  100  updates the load value accumulation information corresponding to image processing device  200  of the transmission destination. Specifically, the load value calculated in step S 614  is added to the load value accumulation information of image processing device  200  of the transmission destination and is updated. 
     In step S 506 , motion video segmenting device  100  determines whether or not the motion video is completed with the pack generated in step S 501 . Moreover, the completion of the motion video means that camera  300  stops photographing of the motion video, for example, due to the completion of the game of the sports event photographed by camera  300 . In step S 506 , in a case where it is determined that the motion video is completed (step S 506 : Yes), the procedure is completed and otherwise (step S 506 : No), the process returns to step S 501 , and a process of forming a next pack is performed. 
     Moreover, the start frame (image obtained in the second and subsequent steps S 602 ) of the next pack forming process may be the f th  frame that is a final value in the previous pack forming process. 
     In this way, the start frame of the next pack forming process and the end frame of the previous pack forming process overlap. When the start frame of the next pack forming process and the end frame of the previous pack forming process overlap, the position (obtained by referring to the tracking result) of the player and the position of the player in the start frame in the end frame of each segmented motion video (pack) overlap, so that the accuracy of the process of step S 305  improves and, as a result, the accuracy of joining of the tracking results is improved. Moreover, even in a case where the start frame of the next pack forming process and the end frame of the previous pack forming process do not overlap, since the difficulty degree of the end frame of the previous pack forming process is Low, the accuracy of the position of the player in the end frame is improved, and thereby the accuracy of the joining of the tracking results is improved. 
     In addition, if the start frame (image obtained in the second and subsequent step S 602 ) of the next pack forming process is the f th  frame that is a final value in the previous pack forming process, it is also guaranteed that the difficulty degree of the start frame of the next pack forming process is Low. If the difficulty degree of the start frame is Low, as described later, there is also an advantage that the accuracy of processing of imparting the initial posture of the player to the still image. Moreover, even if the end frame of the previous pack forming process is the (f-1 st ) frame and the start frame of the next pack forming process is the f th  frame, it is guaranteed that the difficulty degree of the start frame of the next pack forming process is Low. 
     [1.2.3 Subsequent Processes] 
     When the pack is transmitted from motion video segmenting device  100  to image processing device  200  in step S 504  illustrated in  FIG. 5 , as illustrated in  FIG. 3 , image processing device  200  receives the pack and performs the tracking process on the received pack. Image processing device  200  displays the result of the tracking process on display unit  204  in almost real time and causes the corrector to determine whether or not the tracking process is incorrect. In a case where the tracking process is incorrect, the edition acceptance process of accepting the correction by the corrector via input unit  203  is performed (step S 303 ). The tracking process and the edition acceptance process are mainly performed by processor  201  in cooperation with the respective other configuration elements of image processing device  200 . 
     Specifically, the tracking process includes a player detection process of extracting a region corresponding to the player in the image, a posture imparting process of imparting an initial posture of the player in a leading image (start frame) of the pack, a team identification process of identifying a team to which the player belongs based on a color of the player or the like which is extracted, a position tracking process of tracking a position of the player, sports equipment such as a ball, and the like. Detailed description of the player detection process, the posture imparting process, the team identification process, the position tracking process, and the edition acceptance process is disclosed, for example, in PTL 1. 
     When the tracking process and the edition acceptance process are completed, image processing device  200  generates a tracking result listing the position of the player in each still image included in the pack. The tracking result generated by image processing device  200  is transmitted to motion video segmenting device  100  (step S 304  of  FIG. 3 ). Motion video segmenting device  100  joins the tracking results received from image processing device  200  in order to generate movement information for one game of each player (step S 305  of  FIG. 3 ). The movement information for one game generated as described above is useful for being superimposed on the motion video and displayed, objectively evaluating, for example, the momentum and mobility of the player, or constructing a strategy through the game. 
     [1.3 Effects Etc.] 
     As described above, in the embodiment 1, motion video processing system  1  includes motion video segmenting device  100 , image processing device  200 , and camera  300 . Motion video segmenting device  100  extracts the feature amount from the image (still image) configuring the motion video photographed by camera  300 , and determines the difficulty degree when the editing operation is performed on the image based on the feature amount. In the embodiment, the rate occupied by the region corresponding to the player to the whole image is adopted as the feature amount. In addition, the editing operation is an operation performed by the corrector to correct and edit the error in the tracking process of tracking the position of the player, the ball, or the like in the image, which is automatically performed by image processing device  200 . 
     Motion video segmenting device  100  segments the motion video photographed by camera  300  into the plurality of groups (packs), and determines image processing device  200  transmitting the pack based on the difficulty degree of the image included in the pack. More specifically, motion video segmenting device  100  determines the difficulty degree of each motion video configuring the pack, and in a case where the image of the difficulty degree of Low continues predetermined value F_cnt or more, the motion video is segmented and the pack is formed on one image before the last image of the continuous images. Therefore, the difficulty degree of the still image of the start frame or the end frame of each pack formed by motion video segmenting device  100  can be Low. As a result, the accuracy of connecting the tracking results can be improved. 
     Therefore, according to motion video processing system  1 , it is possible to avoid segmenting the motion video before or after the still image where the difficulty degree of the editing operation in image processing device  200  is high. 
     In addition, motion video segmenting device  100  calculates the load value for each pack and determines image processing device  200 , as the transmission destination, having the smallest accumulation value of the load value of the pack which is transmitted previously when image processing device  200  which is the transmission destination of the pack is determined. 
     With such a configuration, motion video segmenting device  100  can determine image processing device  200  having a small load as a transmission destination of a formed pack when the pack is formed from the image photographed by camera  300 . Therefore, the pack can be preferentially transmitted to image processing device  200  having a small load, so that the load of the editing operation can be dispersed for each image processing device  200  and the tracking process of the position of the player, the ball, or the like in the motion video can be efficiently performed. 
     Embodiment 2 
     [2.1 Configuration] 
     Hereinafter, motion video processing system  1  of Embodiment 2 will be described. As illustrated in  FIG. 1 , in Embodiment 2, motion video processing system  1  includes motion video segmenting device  100 , a plurality of image processing devices  200 A,  200 B,  200 C, . . . , and camera  300 . Motion video segmenting device  100  and camera  300 , and motion video segmenting device  100  and the plurality of image processing devices  200 A,  200 B,  200 C, . . . are connected so as to be capable of communicating to each other. 
     Motion video segmenting device  100  and image processing device  200  are configured by, for example, computers such as PCs and workstations. As illustrated in  FIG. 2 , motion video segmenting device  100  (or image processing device  200 ) includes processor  101  ( 201 ), storage unit  102  ( 202 ), input unit  103  ( 203 ), display unit  104  ( 204 ), communication unit  105  ( 205 ), and bus  106  ( 206 ). 
     [2.2 Operation] 
     Next, an operation of motion video processing system  1  in Embodiment 2 will be described. In the following description, the operation of motion video segmenting device  100  is mainly performed by processor  101  in cooperation with respective other configuration elements of motion video segmenting device  100 . 
     Motion video processing system  1  tracks a position of a player, a sports equipment (ball or the like), or the like reflected in the motion video based on a sports video configured by the motion video of the sports event photographed by camera  300 . 
       FIG. 11  is a sequence diagram for explaining a whole operation of motion video processing system  1  in Embodiment 2. In  FIG. 11 , in the plurality of image processing devices  200 , image processing device  200 X, a plurality of image processing devices  200 Y, and a plurality of image processing devices  200 Z, . . . respectively perform different operations. 
     In step S 1101 , motion video segmenting device  100  performs a preliminary process for segmenting the sports video. The preliminary process is the process described in section 1.2.1 of Embodiment 1 described above. 
     In step S 1102 , motion video segmenting device  100  segments the motion video into a plurality of pieces using information acquired in the preliminary process in step S 301  and performs a segmenting transmission process which is transmitted to the plurality of image processing devices  200 . 
     [2.2.1 Segmenting Transmission Process] 
     Hereinafter, details of the segmenting transmission process in Embodiment 2 will be described.  FIG. 12  is a flowchart for explaining an operation of motion video segmenting device  100  in a segmenting transmission process of Embodiment 2. 
     In step S 1201 , motion video segmenting device  100  performs a pack forming process on the motion video photographed by camera  300 . The pack forming process is the process described in the section 1.2.2.1 of Embodiment 1 described above. 
     In step S 1202 , motion video segmenting device  100  transmits a pack generated in step S 1201  to all image processing devices  200 X. 
     In step S 1203 , motion video segmenting device  100  determines whether or not the motion video is ended in the pack generated in step S 1201 . Here, the end of the motion video means that camera  300  stops photographing of the motion video due to, for example, the game of the sports event photographed by camera  300  is ended, or the like. In step S 1203 , in a case where it is determined that the motion video is ended (step S 1203 : Yes), the process is ended and otherwise (step S 1203 : No), the process returns to step S 1201 , and the next pack forming process is performed. 
     Returning to the explanation of  FIG. 11 . In step S 1103 , image processing device  200 X performs an initial process on a plurality of the packs transmitted from motion video segmenting device  100 . The initial process is a process of imparting the initial posture of the player to the still image of the start frame of the pack. 
     The process of imparting the initial posture to the image of the start frame of the pack is performed, for example, as follows. Image processing device  200 X extracts a feature amount from the still image (hereinafter, referred to as a start image) of the start frame of the pack and detects a region corresponding to the player based on the feature amount. Image processing device  200 X learns a position of a joint of the player by machine-learning another motion video of the sports event to be photographed in advance in motion video processing system  1 , and a plurality of the positions of the joints are estimated from the region corresponding to the detected player. Image processing device  200 X reduces the estimated positions of the joints to an appropriate number and, for example, selects an optimum combination of joints, for example, using a dynamic programming method. Image processing device  200 X estimates the posture of the detected player based on the combination of the selected joints. Image processing device  200 X performs the imparting of the initial posture on all players imaging on the start image. 
     Moreover, in the process of imparting of the initial posture described above, a feature amount extracted from the still image of the start frame of the pack may be a size of the foreground region which is the feature amount used in the difficulty degree determination process described above, or may adopt another feature amount. 
     In step S 1104 , image processing device  200 X distributes the pack in which the initial process is completed in step S 1103  to image processing device  200 Y and image processing device  200 Z including itself. For a distribution destination of each pack, image processing device  200 X may determine, for example, as follows. 
     Here, as a premise, in Embodiment 2, in image processing device  200 X and image processing device  200 Y, the editing operation is performed by the corrector, but in image processing device  200 Z, only automatic image analysis is assumed to be performed, so that it is assumed that no editing operation is performed by the corrector. 
     In consideration of such a premise, image processing device  200 X refers the load value of each pack and one of image processing device  200 X and image processing device  200 Y is determined as the distribution destination for the pack of which the load value is a predetermined value or more. Image processing device  200 X determines image processing device  200 Z as the distribution destination for the pack of which the load value is less than the predetermined value. The number of correctors can be reduced by the amount of image processing device  200 Z by adopting such a distribution method, so that an operation cost of motion video processing system  1  can be reduced. 
     In step S 1105 , image processing device  200 X performs a tracking process and an edition acceptance process for the pack distributed in step S 1104 . 
     In step S 1106 , image processing device  200 Y performs the tracking process and the edition acceptance process for the pack distributed in step S 1104 . 
     In step S 1107 , image processing device  200 Z performs the tracking process for the pack distributed in step S 1104 . 
     In step S 1108 , image processing device  200 X transmits a result (one reflecting the editing operation) of the tracking process in step S 1105  to motion video processing system  1 . 
     In step S 1109 , image processing device  200 Y transmits a result (one reflecting the editing operation) of the tracking process in step S 1106  to motion video processing system  1 . 
     In step S 1110 , image processing device  200 Z transmits a result of the tracking process in step S 1107  to motion video processing system  1 . 
     In step S 1111 , motion video segmenting device  100  joins results of the tracking processes in the segmented motion videos which are transmitted from image processing devices  200 X,  200 Y, and  200 Z from step S 1108  to step S 1110 , and generates a tracking result through the motion video. 
     [2.3 Effects Etc.] 
     As described above, in Embodiment 2, motion video processing system  1  includes motion video segmenting device  100 , image processing device  200 , and camera  300 . Motion video segmenting device  100  determines the difficulty degree of the editing operation based on the difficulty degree of the image photographed by camera  300  and performs segmentation of the pack, so that the motion video is not segmented before and after the image of which the difficulty degree is high. Motion video segmenting device  100  transmits the formed pack to image processing device  200 X, and image processing device  200 X performs the initial process of imparting the posture to the player imaged on the image with respect to all the packs, and distributes the packs to the plurality of image processing devices  200  including itself. In this case, image processing device  200 X distributes the pack of which the load value is a predetermined value or more to image processing device  200 X or image processing device  200 Y in which the corrector can perform the correction, and distributes the pack of which the load value is less than the predetermined value to image processing device  200 X in which the correction is not performed by the corrector. 
     With such a configuration, the number of correctors of whole motion video processing system  1  can be reduced, so that the operation cost of motion video processing system  1  can be reduced. 
     Other Embodiments 
     As described above, the exemplary embodiments of technique disclosed in the present application are described. However, the technique of the disclosure is not limited to the embodiments described above, but can be applied to other embodiments in which change, replacement, addition, omission, and the like are performed. Further, it is also possible to combine each of the configuration elements described in the embodiments described above to form a new embodiment. 
     Hereinafter, other embodiments will be illustrated. In the embodiments described above, motion video segmenting device  100  sets the difficulty degree of the editing operation on the still image of each frame to two stages of High and Low. However, motion video segmenting device  100  may set the difficulty degree in the still image of each frame, for example, to multiple stages, that is, for example, five stages. In this case, in the subsequent process, for example, in step S 606  illustrated in  FIG. 6 , or the like, instead of determining whether the difficulty degree is High or Low, it may be determined whether or not the difficulty degree is a predetermined stage or more. 
     In addition, in the embodiments described above, the feature amount is extracted from the image photographed by camera  300  and the difficulty degree of the editing operation is determined based on the feature amount. In the embodiments described above, for example, the rate occupied by the region corresponding to the player to the whole image is adopted as the feature amount. 
     However, the feature amount may be anything as long as it indicates the feature of the player in the image. That is, as a specific example of the feature amount, for example, a color, a luminance, a position of a vertex and a shape, or the like is provided. In addition, another known feature amount may be used as long as it indicates the feature of the player in the image. 
     In addition, in the embodiments described above, the difficulty degree is determined based on the rate of the foreground region included in the difficulty degree determination region (specific region). The difficulty degree determination region is a region that is predicted to be a lot of players set by the operator of motion video processing system  1 , so that if the rate of the foreground region included in the difficulty degree determination region is high, it means that the players are densely populated in one place. However, in the disclosure, instead of determining the difficulty degree based on the rate of the foreground region included in the difficulty degree determination region (specific region), motion video segmenting device  100  may count the number of plays in the difficulty degree determination region and in a case where the number of players is large, it may be determined that the difficulty degree is higher than that of a case where the number of players is small. As a method of counting the number of players in the difficulty degree determination region, a technique of the related art may be used. 
     In addition, in the embodiments described above, as illustrated in  FIG. 6  or the like, in a case where the image of the difficulty degree of Low continues predetermined value F_cnt or more, the motion video is segmented and the pack is formed on one image before the last image of the continuous images. However motion video processing system  1  of the disclosure may form the pack by another method. For example, in a case where motion video segmenting device  100  temporarily sets a predetermined number-th frame from the start frame as the end frame, and then the difficulty degree of the end frame is High, the pack may be formed by a resetting method in which a frame after the end frame is set as the end frame. 
     In addition, in the embodiments described above, motion video segmenting device  100  determines image processing device  200  that is the transmission destination based on the accumulation value of the load values of the pack previously transmitted to image processing device  200 . However, for example, in a case where the time required for the editing operation varies depending on the corrector, in a final time of the game, or the like, it is conceivable that an unprocessed pack remains in each image processing device  200 . In such a case, it is desirable that the transmission destination is set in consideration of a time required for a future editing operation rather than determining image processing device  200  of the transmission destination based on an accumulation load value so far. 
     Therefore, motion video segmenting device  100  calculates a sum value of the load values of the packs (unprocessed packs) which have already be transmitted and not notified of the completion of the process for each image processing device  200 , and the transmission destination may be set based on the sum value. In this case, image processing device  200 , which completes the tracking process and the edition acceptance process for one pack, notifies motion video segmenting device  100  of the completion thereof, and motion video segmenting device  100  may calculate the sum value of the load values of the unprocessed packs by subtracting the load value of the pack notified of the completion from the load value accumulation information described above. 
     In addition, in the embodiments described above, image processing device  200  transmits the tracking result listing the position of the player in each still image included in the pack to motion video segmenting device  100 , and image processing device  200  generates the movement information of the player for one game by joining the tracking results received from the plurality of image processing devices  200 . However, the information, which is transmitted by image processing device  200  to motion video segmenting device  100  to generate the movement information of the player for one game, is not limited to the format. The information, which is transmitted by image processing device  200  to motion video segmenting device  100 , may be any information useful to generate the movement information of the player for one game. For example, image processing device  200  superimposes a marker indicating the position of the player or the like on each still image included in the pack to generate a position information addition pack, and the generated position information addition pack may be transmitted to motion video segmenting device  100 . Even in this case, motion video segmenting device  100  can generate the movement information of the player for one game by joining the packs received from the plurality of image processing devices  200 . 
     In addition, it is not necessary for motion video segmenting device  100  to join the tracking results or the position information addition packs generated by the plurality of image processing devices  200 , and generate the movement information of the player for one game. For example, the plurality of image processing devices  200  may transmit the tracking results or the position information addition packs which are respectively generated to another motion video joining device, and the motion video joining device may join a plurality of the tracking results or the position information addition packs to generate the movement information of the player for one game. In this case, similar to motion video segmenting device  100  and image processing device  200  described above, the motion video joining device is configured by, for example, a computer such as a PC and a workstation. 
     In addition, in the embodiments described above, image processing device  200 X and image processing device  200 Y, the editing operation is performed by the corrector, but the disclosure is not limited thereto, and for example, the editing operation is performed by the corrector in image processing device  200 Y, in image processing device  200 X and image processing device  200 Z, only automatic image analysis is performed, and no editing operation may be performed by the corrector. In this case, the editing operation can be divided among correctors for each editing content, such as, the corrector of image processing device  200 X performs confirmation and correction of processing of imparting the initial posture, and the corrector of image processing device  200 Y performs the confirmation and correction of the tracking process. 
     In addition, in the embodiments described above, a plurality of image processing devices  200 A,  200 B,  200 C,  200 X,  200 Y, and  200 Z, and the like are described as having the same configuration. However, in Embodiment 2, with respect to image processing device  200 Z in which the corrector does not perform the correction operation, for example, there may be a difference in the configuration such as not having input unit  203  or display unit  204 . 
     In addition, in the embodiments described above, motion video segmenting device  100  and image processing device  200  are described as different configurations. However, for example, a function of motion video segmenting device  100  and a function of image processing device  200  may be mounted on a computer such as a small number of PCs or workstations. In such a case, the functions of motion video segmenting device  100  and the plurality of image processing devices  200  can be realized by, for example, a very small number of computers such as one computer by preparing the display unit and the input unit for performing the correction operation for the number of correctors. Therefore, an installation cost of motion video processing system  1  can be reduced. 
     Moreover, since the embodiments described above are intended to exemplify the technique in the disclosure, various modifications, replacements, additions, omissions, or the like may be performed within the scope of claims or the equivalent thereof. For example, in the embodiments described above, as an example of the motion video, an example, in which the motion video configuring the sports video obtained by photographing the game of the sports event is used, is described. However, the motion video used in the disclosure is not limited to the motion video configuring the sports video obtained by photographing the game of the sports event. The motion video used in the disclosure may include all the motion videos to be analyzed widely. An example of the motion video to be analyzed includes a motion video recording a behavior of a person. In a case where the disclosure is applied to the motion video recording the behavior of the person, if there are many persons who are present in a specific region in the image configuring the motion video, it is determined that the difficulty degree is higher than that of a case where there are a few persons who are present in the specific region. In addition, a motion video recording a concert image of a pop star is included in the motion video used in the disclosure. In a case where the disclosure is applied to the motion video recording the concert image of the pop star, if there are many pop stars who are present in a specific region in the image configuring the motion video, it is determined that the difficulty degree is higher than that of a case where there are a few pop stars who are present in the specific region. 
     INDUSTRIAL APPLICABILITY 
     The disclosure can be applied to the motion video segmenting method, the motion video segmenting device, and the motion video processing system that segment the motion video. 
     REFERENCE MARKS IN THE DRAWINGS 
       1  MOTION VIDEO PROCESSING SYSTEM 
       100  MOTION VIDEO SEGMENTING DEVICE 
       101  PROCESSOR 
       102  STORAGE UNIT 
       103  INPUT UNIT 
       104  DISPLAY UNIT 
       105  COMMUNICATION UNIT 
       106  BUS 
       200 ,  200 A.  200 B.  200 C.  200 X.  200 Y,  200 Z IMAGE PROCESSING DEVICE 
       201  PROCESSOR 
       202  STORAGE UNIT 
       203  INPUT UNIT 
       204  DISPLAY UNIT 
       205  COMMUNICATION UNIT 
       206  BUS 
       300  CAMERA 
       400  FIELD 
       401 ,  402  REGION 
       1001 ,  1002 ,  1003 ,  1004  TABLE