JP2019200642A - Image collection generation program, information processor, and image collection generation method - Google Patents

Abstract

To efficiently generate image collection in an image collection generation system.SOLUTION: For a plurality of events belonging to photographed image data recordable in image collection, weight setting is received, the candidate photographed image data recordable in the image collection is extracted from the photographed image data respectively corresponding to the plurality of events according to the weight received, and image collection in which the extracted photographed image data is recorded is generated.SELECTED DRAWING: Figure 4

Description

  The present invention relates to an image collection generation program, an information processing apparatus, and an image collection generation method.

  In a community such as club activities, an image collection (also called an album) may be created using captured images (eg, photographs) of members (participants) captured at various events. Systems that support the creation of image collections have been developed.

Japanese Patent Laid-Open No. 2018-7018 JP 2007-96379 A JP 2013-161394 A Japanese Patent Laying-Open No. 2015-60257 JP 2001-297090 A

  In such a conventional image collection generation system, it is difficult to cover important events and equalize the number of photographs taken by each member. There is a problem that it takes a lot of labor and time.

  In one aspect, the object is to generate an efficient image collection.

  The image collection generating program accepts weighting settings for a plurality of events to which captured image data that can be recorded in the image collection belongs, and the captured image data belonging to each of the plurality of events according to the received weighting. The candidate captured image data to be recorded in the image collection is extracted from the image collection, and an image collection in which the extracted captured image data is recorded is generated.

  According to one embodiment, an image collection can be generated efficiently.

It is a figure which illustrates the hardware constitutions of the image collection production | generation system as an example of embodiment. It is a figure which illustrates the hardware constitutions of the information processing apparatus in the image collection production | generation system as an example of embodiment. It is a figure which illustrates the hardware constitutions of the client terminal in the image collection production | generation system as an example of embodiment. It is a figure which illustrates the outline | summary of the image collection creation in the image collection production | generation system as an example of embodiment. It is a figure which illustrates the screen on the client terminal in the image collection production | generation system as an example of embodiment. It is a figure which illustrates the function structure of the information processing apparatus in the image collection production | generation system as an example of embodiment. It is a figure which illustrates the person information in the image collection production | generation system as an example of embodiment. It is a figure which illustrates member information in an image collection production system as an example of an embodiment. It is a figure which illustrates the event information in the image collection production | generation system as an example of embodiment. It is a figure which illustrates the photograph determination processing in the image collection production | generation system as an example of embodiment. It is a figure which illustrates the photograph determination processing result in the image collection production | generation system as an example of embodiment. It is a figure which illustrates the image collection production | generation process in the image collection production | generation system as an example of embodiment.

  Embodiments of the present invention will be described below with reference to the drawings. However, the embodiments described below are merely examples, and are not intended to exclude various modifications and technical applications that are not explicitly described below. For example, the present embodiment can be implemented with various modifications without departing from the spirit of the present embodiment. Note that, in the drawings used in the following embodiments, portions denoted by the same reference numerals represent the same or similar portions unless otherwise specified.

[1] One Embodiment [1-1] Hardware Configuration Example of Image Collection Generating System According to One Embodiment FIG. 1 is a diagram illustrating a hardware configuration of an image collection generating system 1 as an example of an embodiment. .

  As shown in FIG. 1, the image collection generating system 1 includes an information processing apparatus 10 and one or more client terminals 20-1, 20-2, 20-3,. n (n is an integer). Hereinafter, as codes indicating client terminals, codes 20-1, 20-2, 20-3,..., 20-n are used when one of a plurality of client terminals needs to be specified. Reference numeral 20 is used when referring to the client terminal. The information processing apparatus 10 and the client terminal 20 are connected via a network 90 such as the Internet.

  Further, the information processing apparatus 10 can communicate with, for example, a management terminal of an administrator (not shown) or a terminal owned by an image collection editor (described later) via the network 90.

  1 illustrates that the image collection generating system 1 includes one information processing apparatus 10, but may include a plurality of information processing apparatuses 10.

[1-2] Hardware Configuration Example of Information Processing Device in Image Collection Generating System According to One Embodiment FIG. 2 is a diagram illustrating a hardware configuration of the information processing device 10 as an example of the embodiment.

  As shown in FIG. 2, the information processing apparatus 10 is, for example, a computer having a server function, and includes a CPU (Central Processing Unit) 11, a storage unit 12, a memory 13, an IF (Interface) unit 14, an input unit 15, And the output part 16 may be provided.

  The information processing apparatus 10 has a server function and is connected to a plurality of client terminals 20 via a network 90 such as the Internet.

  The CPU 11 executes an OS (Operating System) or a program stored in a storage unit 12 or the like described later, and controls the information processing apparatus 10 to create an image collection described later, for example. In the present embodiment, the CPU 11 executes a generation program 91 described later.

  The storage unit 12 is an example of hardware that stores various data, programs, and the like. For example, the storage unit 12 may be used as a secondary storage device of the information processing apparatus 10, and may store an OS, firmware, a program such as an application, and various data. Examples of the storage unit 12 include a semiconductor storage device such as an SSD (Solid State Drive) or an SCM (Storage Class Memory) in addition to a magnetic disk device such as an HDD (Hard Disk Drive). The storage unit 12 may store a program that realizes all or some of the various functions of the information processing apparatus 10.

  The memory 13 is an example of hardware that stores various data and programs. Examples of the memory 13 include a volatile memory such as a RAM (Random Access Memory), and a nonvolatile memory such as a flash memory, an SCM, and a ROM (Read Only Memory).

  The IF unit 14 is an example of a communication interface that performs connection control and communication control with the client terminal 20 via the network 90. For example, examples of the IF unit 14 include an adapter compliant with Ethernet (registered trademark), optical communication (for example, Fiber Channel), and the like. The information processing apparatus 10 may have a communication interface for controlling connection and communication with a management terminal (not shown) of an administrator, and the generation program 91 is downloaded from the network 90 using the communication interface. You may download it.

  The input unit 15 may include at least one of input devices such as a mouse, a keyboard, a touch panel, and operation buttons, for example.

  The output unit 16 may include at least one of output devices such as a display, a projector, a speaker, and a printer, for example.

[1-3] Hardware Configuration Example of Client Terminal in Image Collection Generation System According to One Embodiment FIG. 3 is a diagram illustrating a hardware configuration of a client terminal 20 as an example of the embodiment.

  As illustrated in FIG. 3, the client terminal 20 may include a CPU 21, a storage unit 22, a memory 23, an IF unit 24, an input unit 25, an output unit 26, and an imaging unit 27.

  The client terminal 20 is connected to the information processing apparatus 10 and a plurality of client terminals 20 via a network 90 such as the Internet.

  The CPU 21 executes an OS and a program stored in a storage unit 22 and the like which will be described later. In the present embodiment, the CPU 21 executes an upload program 92 described later.

  The storage unit 22 is an example of hardware that stores various data, programs, and the like. The storage unit 22 includes a captured image 93, and may store a captured image captured by an imaging unit 27 described later, for example.

  For example, the storage unit 22 may be used as a secondary storage device of the client terminal 20, and may store an OS, firmware, an application, an upload program 92, a captured image 93, and the like. Examples of the storage unit 22 include a semiconductor storage device such as an SSD or SCM in addition to a magnetic disk device such as an HDD. The storage unit 22 may store a program that realizes all or some of the various functions of the client terminal 20.

  The upload program 92 stored in the storage unit 22 converts the captured image 93 stored in the storage unit 22 into, for example, the captured image group 44 of the storage unit 12 of the information processing apparatus 10 via the network 90 (see FIG. 6). Upload to. The upload program 92 may be downloaded to the storage unit 22 via the network 90 using a communication interface described later.

  The memory 23 is an example of hardware that stores various data and programs. Examples of the memory 23 include volatile memories such as RAM, and nonvolatile memories such as flash memory, SCM, and ROM.

  The IF unit 24 is an example of a communication interface that controls connection and communication between the information processing apparatus 10 and the client terminal 20 via the network 90. For example, examples of the IF unit 24 include an adapter compliant with Ethernet (registered trademark), optical communication (for example, Fiber Channel), and the like. Note that the client terminal 20 may include a communication interface that controls connection and communication with a management terminal (not shown) of an administrator.

  The input unit 25 may include at least one of input devices such as a mouse, a keyboard, a touch panel, and operation buttons, for example.

  The output unit 26 may include at least one of output devices such as a display, a projector, a speaker, and a printer, for example.

  The imaging unit 27 includes, for example, components such as a lens, an image sensor, and a signal processing device, and acquires a captured image such as a photograph using an image imaging function. The captured image captured by the imaging unit 27 is stored in the storage unit 22. The captured image stored in the storage unit 22 is denoted by reference numeral 93.

  The client terminal 20 configured as described above may be a computer such as a PC (Personal Computer), a tablet, a PDA (Personal Digital Assistant), a camera, or a mobile phone.

[1-4] Outline of Image Collection Creation in Image Collection Generation System According to One Embodiment As an example of the embodiment configured as described above, an outline of image collection creation in the image collection generation system 1 will be described with reference to FIGS. Will be described.

  FIG. 4 is a diagram showing an outline of image collection creation in the image collection generation system 1 that manages a plurality of participants.

  FIG. 5 is a diagram illustrating a screen on the client terminal 20 in the image collection generating system 1 as an example of the embodiment.

  As shown in FIG. 4, as a community, for example, an official such as a guardian of children belonging to a sports club (team) uses an imaging unit 27 of the client terminal 20 and a player belonging to the sports club (for example, , Related children) at various events. The captured images include, for example, images taken in game scenes such as practice games and various tournaments (scenes will be described later) and scenes other than the above games (other scenes) such as during practice, eating, and moving. included.

  In addition, captured images captured by the parties may include not only photographs but also moving images. In the present embodiment, a case where the captured image is a photograph will be described as an example.

  Then, the above-mentioned parties provide the captured image to the community by uploading the captured image to the storage unit 12 of the information processing apparatus 10 via the network 90 by using, for example, the upload program 92 ( (See arrow P1 in FIG. 4). Further, the related person may upload a captured image captured using an imaging device such as a camera (not shown) to the storage unit 12 of the information processing device 10.

  The information processing apparatus 10 may be a server on a cloud service, or may be a dedicated server that can be accessed only by players and related parties belonging to the community. Therefore, it can be said that the captured image group 44 of the storage unit 12 of the information processing apparatus 10 is a place for storing captured images dedicated to the community.

  In the present embodiment, an image collection (image data collection) is created for a specific person and a specific event among a plurality of persons belonging to a specific community. That is, the captured image is captured image data that can be recorded in an image collection, and a captured image belonging to a specific event is extracted.

  This specific person is also called a target person of the image book, and this specific event is also called a target event of the picture book. As will be described later, the selection (designation) of the target person or target event of such an image collection is performed by an editor or the like (see arrow B1 in FIG. 5). Note that the editor of the image collection may be composed of, for example, one or more of the above parties.

  In addition, in the image collection created in the present embodiment, a captured image including a specific person among captured images captured by persons related to a person belonging to the community is displayed for each specific event. Extracted and posted (recorded). In order to cover specific persons, it is desirable that the number of captured images extracted for these specific persons be (almost) the same for each event. The number of captured images is also referred to as the number of captured images.

  In the present embodiment, captured images are captured at various events. For example, an event with a larger weight (weighting) value indicating importance is extracted as a larger number.

  As described above, in this embodiment, an image collection that covers important events and covers a specific player is generated.

[1-5] Functional Configuration Example of Information Processing Device in Image Collection Generation System According to One Embodiment FIG. 6 shows a functional configuration of the information processing device 10 in the image collection generation system 1 as an example of the embodiment shown in FIG. It is a figure illustrated.

  As illustrated in FIG. 6, the information processing apparatus 10 may include an extraction condition determination unit 31, a captured image determination unit 32, and a captured image extraction unit 33 exemplarily.

  The extraction condition determination unit 31 determines a person guide number and an event guide number. The estimated number of persons is the number of captured images extracted (assigned) per person. This approximate number of persons is also referred to as the approximate number of captured image data. Also, the event guide number is the number of captured images extracted per event.

  Note that the extraction condition determination unit 31 determines an imaging period and an imaging in advance by an administrator (not shown) or an editor (also referred to as an editor) of the image collection before determining the person guide number and the event guide number. It is assumed that the number of extracted images is designated (see arrows B2 and B3 in FIG. 5).

  The imaging period is a period in which a captured image that is a target (candidate) to be posted in the image collection is captured. The image collection generated by the image collection generation system includes a period designated as the imaging period. It is assumed that the captured image is extracted and published in the image collection. The number of picked-up images is the number of picked-up images (number of picked-up image data) posted in the image book, and this number is the number of picked-up images of all picked-up images picked up by persons related to the person belonging to the community. Of these, the number corresponds to the number of picked-up images extracted for posting in the image collection.

  Further, as described above, it is assumed that a person to be published in the image collection is selected (designated) in advance by an editor or the like. The extraction condition determination unit 31 determines the number of captured images to be extracted (selected) for each person designated by the editor or the like, that is, the person reference number.

  In addition, the weight of each event is determined in advance by an editor or the like, that is, the weight is set for each event. The extraction condition determination unit 31 receives the determined weight of each event and the specified number of extracted captured images, and based on these pieces of information, the number of captured images to be extracted for each event, that is, the event guide number To decide.

  Next, the captured image determination unit 32 determines, for each captured image, an event, a scene, and a person included in the captured image, which will be described later, when each captured image is captured.

  The captured image extraction unit 33 determines a processing order in which the captured image extraction process is performed, and extracts captured images each containing a subject as a subject for each predetermined event.

  As illustrated in FIG. 6, the storage unit 12 may include person information 41, member information 42, event information 43, and a captured image group 44.

  Of these, the person information 41, the member information 42, and the event information 43 are defined in advance by an editor or the like, for example, but may be changed as appropriate during operation. Below, each information stored in the memory | storage part 12 is demonstrated.

  First, the person information 41 manages information related to persons belonging to each community. In the present embodiment, the person information 41 manages player information. The person information 41 will be described with reference to FIG.

  FIG. 7 is a diagram illustrating person information 41 regarding players as persons belonging to a sports club in the image collection generating system 1. In this embodiment, as shown in FIG. 7, the person information 41 is expressed in a table format, but the expression format of the information stored in the person information 41 is not limited to a table, and various modifications are made. can do.

  The person information 41 illustrated in FIG. 7 includes fields of “player name”, “school year”, “date of entry”, and “face image”.

  First, the field “player name” of the person information 41 stores the name of the player. For example, as shown in FIG. 7, “Taro Yamada” is stored in the field of the person information 41.

  The field “school year” of the person information 41 stores the school year to which the player belongs. For example, as shown in FIG. 7, “sixth grade” is stored in the field “grade” as the grade to which the player whose “player name” is “Taro Yamada” belongs.

  The field “entrance date” of the person information 41 stores the date that the player belonged to (joins) the sport club. For example, “year”, “month”, and “day” are replaced with “/” (slash). Store the date clearly specified by separating with. For example, as shown in FIG. 7, when the date of entry of a player whose “player name” is “Taro Yamada” is May 11, 2014, “2014/5/11” Is stored.

  The field “face image” of the person information 41 stores, for example, a link to the face image as information on the player's face image. In the present embodiment, for example, when a face image obtained by imaging a player's face is stored in the storage unit 12, the field “face image” of the person information 41 is stored in the face image of the player stored in the storage unit 12. A link is stored.

  Next, the member information 42 provided in the storage unit 12 of the information processing apparatus 10 will be described. The member information 42 is used to manage information related to participants related to each event in the image collection generation system 1. The member information 42 will be described with reference to FIG.

  The member information 42 illustrated in FIG. 8 is a diagram illustrating the association between the game in which the sports club illustrated in FIG. 4 participates and the player who participated in the game in the image collection generating system 1. In this embodiment, as shown in FIG. 8, the member information 42 is expressed in a table format. However, the expression format of the information stored in the member information 42 is not limited to the table, and various modifications are made. can do.

  The member information 42 shown in FIG. 8 includes fields of “event name”, “player name”, and “back number”.

  The field “event name” of the member information 42 stores the name of the event. In the present embodiment, the name of the game in which the sports club participated is stored in the field “event name” as an event. Here, FIG. 8 illustrates the players who participated in the “prefectural tournament qualifying round 1” event.

  The field “player name” of the member information 42 stores the name of the person who participated (participated in) the event. FIG. 8 illustrates the names of players who participated in an event whose “event name” is “prefectural tournament qualifying round 1”.

  The field “number” of the member information 42 stores the number provided by the “player name” who participated (participated in) the event. In the example shown in FIG. 8, for example, among the players who participated in the event whose “event name” is “prefectural tournament qualifying round 1”, the “number” provided by the player whose “player name” is “Toshio Wada” “10” is stored in the field “number” of the member information 42 (see arrow P9 in FIG. 8).

  Next, the event information 43 provided in the storage unit 12 of the information processing apparatus 10 will be described. The event information 43 manages information related to events in the image collection generating system 1. The event information 43 will be described with reference to FIG.

  The event information 43 illustrated in FIG. 9 stores the schedule of the game in which the sports club illustrated in FIG. 4 participates in the image collection generation system 1. In this embodiment, as shown in FIG. 9, the event information 43 is expressed in a table format. However, the expression format of information stored in the event information 43 is not limited to a table, and various modifications are made. can do.

  The event information 43 shown in FIG. 9 includes fields of “date or day of the week”, “time”, “event name”, “location”, and “weight”.

  In the field “date or day of the week” of the event information 43, for example, the date specified by separating the “year”, “month”, and “day” with “/” (slash) is stored as the date of the event. Or store information indicating the day of the week. In this embodiment, as illustrated in FIG. 9, for example, “day” or “2016/1/15” is stored in the field “date or day of the week”.

  The field “time” of the event information 43 stores the time and time zone when the event was held. In the present embodiment, as illustrated in FIG. 9, for example, “14:40 to 17:30” or “9:00 to” is stored in the field “date or day of the week”.

  The field “event name” of the event information 43 stores the name of the event. Since the field “event name” of the event information 43 is the same as the field “event name” of the member information 42 (FIG. 8) described above, description thereof is omitted here.

  The field “weight” of the event information 43 stores a weight such as importance corresponding to each event. This weight may be determined by an editor or the like. In this embodiment, an integer greater than or equal to 0 is stored in the field “weight”, but the field “weight” is not limited to this. In the present embodiment, as illustrated in FIG. 9, for example, “6” is stored in the field “weight” as the weight of the event whose “event name” is “prefectural tournament qualifying” (FIG. 9). (See arrow P2).

  Moreover, the captured image group 44 provided in the storage unit 12 of the information processing apparatus 10 stores, for example, captured images captured by a person who belongs to a community using the client terminal 20. As described above, these captured images are uploaded by the parties using the upload program 92, for example, from the storage unit 22 of the client terminal 20 to the storage unit 12 of the information processing apparatus 10 via the network 90. It is.

[1-6] Photo Determination Processing in Information Processing Device of Image Collection Generation System According to One Embodiment As an example of the embodiment configured as described above, photo determination by the information processing device 10 of the image collection generation system 1 is illustrated in FIG. 11 will be described according to the flowchart (steps T1 to T3) shown in FIG.

  FIG. 10 is a flowchart for explaining photo determination processing in the information processing apparatus 10 according to an embodiment.

  FIG. 11 is a diagram illustrating the result of performing the photo determination process in the information processing apparatus 10 according to the embodiment. The ten rectangular images (including a person) included in FIG. 11 are examples of captured images.

  Using FIG. 10 and FIG. 11 as an example of creating an image book for a specific player belonging to the sports club illustrated in FIG. 4 and FIG. 7 to FIG. The photo determination process in the information processing apparatus 10 will be described.

  As described above, the related person images the player using the client terminal 20 illustrated in FIG. 3, but the captured image 93 captured so far is stored in the information processing apparatus 10 via the network 90. Assume that it is stored (uploaded) in the captured image group 44 of the unit 12.

  In step T1 illustrated in FIG. 10, the captured image determination unit 32 of the information processing device 10 captures the captured image for each captured image stored in the captured image group 44 of the storage unit 12 of the information processing device 10. Determine the event. That is, the captured image determination unit 32 determines which of the events illustrated in FIG. 9 each captured image stored in the captured image group 44 of the storage unit 12 is captured. .

  Specifically, the captured image determination unit 32 extracts, for each captured image, an imaging date and time included in a profile or the like included in the captured image. Then, the captured image determination unit 32 determines, based on the event information 43 shown in FIG. 9, whether the extracted imaging date / time is included in the date or day of the week and the time when the event is held.

  For example, when the captured date and time extracted from the captured image by the captured image determination unit 32 is 16:00 on January 16, 2016, the captured image determination unit 32 captures the captured image in the “prefectural convention qualifying”. It is determined that it is a thing (see arrow P2 shown in FIG. 9).

  In step T1, the captured image determination unit 32 may determine an event based on coordinate information indicating position information. The coordinate information indicating the position information is also included in the profile and the like included in the captured image. The event determination based on the coordinate information indicating the position information may be performed together with the event determination based on the imaging date / time of the captured image, or may be performed instead of the event determination based on the imaging date / time of the captured image.

  As a result of the event determination processing in step T1 performed by the captured image determination unit 32, the captured image illustrated in FIG. 11 has three events (“entrance type”, “prefectural tournament qualifying”, “expedition”) based on each imaging date and time. (See arrows P3 to P5 in FIG. 11).

  In subsequent step T <b> 2, the captured image determination unit 32 determines, for each captured image stored in the captured image group 44 of the storage unit 12 of the information processing apparatus 10, the scene in which the captured image is captured. A scene represents a captured situation. In this embodiment, it is determined whether the scene is a game or a game other than a game. That is, for each captured image stored in the captured image group 44 of the storage unit 12, the captured image determination unit 32 determines whether the image is a game scene (game scene) or a scene other than the game (other scenes). ).

  Specifically, the captured image determination unit 32 determines a scene for each captured image based on a background (for example, a ground color) included in the captured image. The captured image determination unit 32 detects, for example, a player's clothing image included in the captured image, and determines a scene based on the detection result. For example, when the detected clothing image is a uniform image, the scene is based on whether the uniform included in the uniform image is worn at the time of a game or worn at a scene other than the game. Determine. In addition, known methods for determining a scene may be appropriately combined.

  As a result of the scene determination processing in step T2 performed by the captured image determination unit 32, it is determined whether the captured image illustrated in FIG. 11 is a game scene or another scene (FIG. 11). (See arrows P6 and P7).

  In subsequent step T <b> 3, the captured image determination unit 32 determines, for each captured image stored in the captured image group 44 of the storage unit 12 of the information processing apparatus 10, a person (captured person) included in the captured image. To do. In the example of FIG. 11, a player is determined as a person included in the captured image.

  Specifically, the captured image determination unit 32 extracts a spine number included in the captured image for each captured image. Then, the captured image determination unit 32 determines which player the extracted back number belongs to based on the information of “back number” stored in the member information 42 illustrated in FIG. Since a method for extracting a number from this image is known, the description thereof is omitted.

  In step T3, for example, in the captured image indicated by arrows P8 and P9 in FIG. 11, the captured image determination unit 32 extracts the number “10” included in the captured image. Then, the captured image determination unit 32 refers to the member information 42 shown in FIG. 8 and the player whose back number is “10” is “Toshio Wada”. Therefore, the captured images shown by arrows P8 and P9 in FIG. The player included in is determined to be “Toshio Wada”.

  Moreover, the captured image determination unit 32 may determine a player for each captured image based on the face (image) included in the captured image. In that case, the captured image determination unit 32 extracts a face image included in each captured image, and stores the extracted face image and a link destination of the “face image” of the person information 41 illustrated in FIG. 7. A player included in the captured image is determined by comparing the face image (see arrow P10 in FIG. 11). Since a technique for determining a person included in a captured image based on such a face image is known, the description thereof is omitted. The person determination based on the face image may be performed together with the person determination based on the spine number, or may be performed instead of the person determination based on the spine number.

  In this way, the captured image (captured image data group) extracted in step T3 is associated with the information of the person (player information) included in the captured image.

  Through the processes of steps T1 to T3 described above, the captured image determination unit 32 determines which event and scene are captured for each captured image, and which player is included in each captured image. It can be determined (classified) whether it is included. And the photograph of each player is extracted through the process mentioned later using the captured image determined (classified) through the preprocessing shown in steps T1 to T3.

[1-7] Image Collection Generation Processing in Information Processing Device of Image Collection Generation System According to One Embodiment As an example of the embodiment configured as described above, image collection generation processing by information processing device 10 of image collection generation system 1 Is described according to the flowchart (steps S1 to S10) shown in FIG.

  FIG. 12 is a flowchart for explaining processing in the information processing apparatus 10 according to the embodiment.

  The processing in the information processing apparatus 10 will be described using FIG. 12 as an example in which an image collection is created for a specific player belonging to the sports club illustrated in FIGS. 4 and 7 to 11.

  Note that, as described above, the players involved image the players using the client terminal 20 illustrated in FIG. 3, but the captured image 93 captured so far is transmitted via the network 90 to the information processing apparatus 10. Are stored in the captured image group 44 of the storage unit 12.

  In step S1 shown in FIG. 12, the extraction condition determination unit 31 of the information processing apparatus 10 determines the person guide number and the event guide number. Specifically, the extraction condition determination unit 31 determines the estimated number of persons based on the imaging period, the number of captured images extracted, and the target person specified in advance by an editor or the like.

  For example, it is assumed that the total number of captured images of the players captured by the parties concerned is 1,000 (see arrow B4 shown in FIG. 5). Assume that the imaging period designated by the editor of the image collection is 2016 (see arrow B2 shown in FIG. 5) and the number of extracted captured images is 50 (see arrow B3 shown in FIG. 5). Further, it is assumed that the player who is the subject of the image collection is designated as “Taro Yamada” or “Yuichi Iguchi” by the editor of the image collection (see arrow B1 shown in FIG. 5).

  First, based on the imaging period designated by the editor or the like, the extraction condition determination unit 31 sets the date and time when the imaged date was taken out of all 1,000 captured images stored in the captured image group 44 of the storage unit 12 in FY2016. The captured image is extracted. Since this extraction method is known, the description thereof is omitted.

  The extraction condition determination unit 31 determines the number of picked-up images (50) from the number of picked-up images (50) and the information that the target person (number of players) of the image collection is the above two people. By dividing by the number of persons (2 persons), the estimated number of persons is calculated to be 25. This means that 25 captured images are extracted for each of “Taro Yamada” and “Yuichi Iguchi” among all captured images. This is because, in the present embodiment, the same number of captured images is extracted for each subject of the image collection.

  In the present embodiment, it is assumed that the target person is always included in the image extracted for each target person.

  In subsequent step S2, the extraction condition determination unit 31 determines the event guide number for each event. Specifically, the extraction condition determination unit 31 determines the event standard number of each event using the following formula (1) based on the weight of each event set by the editor of the image collection.

    Estimated number of events [sheets] = number of captured image extractions [sheets] x (weight of event / total weight) (1)

  In the above formula (1), the total weight is the sum of the weights given to each event. That is, by using the above formula (1), the extraction condition determination unit 31 extracts a corresponding captured image from captured images belonging to each of a plurality of events according to the quantity balance according to the weight.

  For example, among the events that are held, when a captured image is extracted for each event of “entrance type”, “prefectural tournament qualifying”, and “expedition”, the weight given to each event is “4”, respectively. ”,“ 6 ”, and“ 3 ”. In such a case, based on the above formula (1), the target number of events for the “entrance type” is 50 (sheets) × (4 / (4 + 6 + 3)) = 15 (sheets) (note that the last digit is rounded off) Is calculated. Similarly, the event guide number for “prefectural tournament qualifying” is calculated as 23, and the event guide number for “expedition” is calculated as 12, respectively.

  Since the estimated number of events calculated as described above is the number of captured images extracted for each event, the captured images of both players “Taro Yamada” and “Yuichi Iguchi” who are the subjects of the image collection are used. Including. As described above, in the present embodiment, it is desirable that the number of captured images extracted for each target person be (almost) the same for each event. Therefore, for example, in the case of an “entrance type” captured image, the event target number is 15, so the event target number (15) is divided (apportioned) by the number of subjects (number of players; 2 players). The value obtained by (7.5) is the number of captured images per target person.

  In subsequent step S3, the captured image extraction unit 33 receives the result of the scene determination process shown in step T2 of FIG. 10 (see arrow Q2 in FIGS. 10 and 12), and among the 1,000 captured images, the captured image. A captured image that is determined to be a game scene by the determination unit 32 is extracted. That is, the captured image extraction unit 33 selects a captured image of a game scene (a captured image in which a predetermined uniform image is detected) in preference to a captured image of another scene (a captured image in which a predetermined uniform image is not detected). To do.

  The captured image extraction unit 33 receives the result of the person determination process shown in step T3 in FIG. 10 (see the arrow Q3 in FIGS. 10 and 12), and the target person is included as a subject in the extracted captured images. The captured image to be extracted is extracted, and the number of images is calculated. That is, in step T3 in FIG. 10, the captured images (captured image data group) associated with “Taro Yamada” and “Yuichi Iguchi” are extracted, and the number of images is calculated. For example, it is assumed that among all 1,000 captured images, there are 250 captured images determined to be a game scene and to include “Taro Yamada” among subjects. Similarly, it is assumed that among all 1,000 captured images, there are 50 captured images that are determined to be a game scene and that include “Yuichi Iguchi” among the subjects.

  The processing in step S3 may be started, for example, by pressing a “extract photo” button on the screen on the client terminal 20 illustrated in FIG. 5 (see arrow B5 in FIG. 5). .

  In subsequent step S <b> 4, the captured image extraction unit 33 determines the processing order for performing the captured image extraction process. That is, the captured image extraction unit 33 determines a processing order in which the captured image extraction process is performed first for a player with a small number of captured images of the game scene calculated in step S3.

  In the example described above, the captured image extraction unit 33 first performs captured image extraction processing on “Yuichi Iguchi” (number of captured images of the match scene: 50), and then “Taro Yamada” (captured image of the match scene). The number of picked-up image extraction processing is determined for the number of sheets: 250).

  In subsequent step S <b> 5, the captured image extraction unit 33 determines whether there is a person who has not performed extraction processing of the captured image. If there is a person who has not performed the captured image extraction process (Yes route in step S5), the process proceeds to step S6. On the other hand, when there is no person who has not performed the captured image extraction process (No route in step S5), the process proceeds to step S7.

  In step S6, processing for extracting captured images for each event is performed in descending order of weight. That is, in step S6, the order of “prefectural tournament qualifying” (weight: “6”), “entrance type” (weight: “4”), “expedition” (weight: “3”) based on the size of the weight. A captured image is extracted. In step S6, the result of the event determination process shown in step T1 in FIG. 10 is received (see arrow Q1 in FIGS. 10 and 12), and a captured image is extracted for each event.

  In the example described above, the captured image extraction unit 33 performs the captured image extraction process shown in step S6 before “Yuichi Iguchi”. Specifically, the captured image extraction unit 33 extracts “prefectural tournament qualifying” (weight: of the captured images (50 images) of the game scene determined to include “Yuichi Iguchi” extracted in step S3). “6”) extracts the imaged image. In this extraction, the captured image extraction unit 33 extracts the image determined by the captured image determination unit 32 as the captured image of “prefectural tournament qualifying” in the event determination process shown in step T1 shown in FIG.

  Next, the captured image extraction unit 33 similarly applies the “entry type” (weight: “4”) out of the captured images (50 images) of the game scene determined to include “Yuichi Iguchi”. Extract what was imaged. The captured image extraction unit 33 then captures images captured in the “expedition” (weight: “3”) among the captured images (50) of the game scene determined to include “Yuichi Iguchi”. Extract.

  In this way, through the processing shown in step S6, a captured image of “Yuichi Iguchi” captured at each event of “Iribe type”, “Prefectural tournament qualifying”, and “Expedition” is extracted. Then, the process returns to step S5.

  In this embodiment, since “Taro Yamada” is the person who has not performed the extraction process of the captured image in step S5, the Yes route shown in step S5 is followed, and the extracted captured image shown in step S6 for “Taro Yamada” is displayed. Perform processing. In step S6, when the captured image extraction process for “Taro Yamada” ends, the process returns to step S5. In this example, the picked-up image extraction process is performed for all the target persons, and the process proceeds to step S7 via the No route shown in step S5.

  In step S <b> 7, the captured image extraction unit 33 determines whether or not there is a player who is less than the target number of persons among the target persons. When there is a player who does not satisfy the estimated number of persons (Yes route in step S7), the process proceeds to step S8. On the other hand, when there is no player who does not satisfy the estimated number of persons (No route in step S7), the process proceeds to step S9.

  In step S <b> 8, the captured image extraction unit 33 extracts captured images in which the player is included as a subject from the other scenes for the number of players that is less than the estimated number of people for the players that are less than the estimated number of people. For the captured image of the other scene, the captured image extraction unit 33 uses the result of the scene determination process shown in step T2 of FIG. 10 (see arrow Q2 'in FIGS. 10 and 12).

  For example, in the above-described step S6 (captured image extraction process), for example, regarding “Yuichi Iguchi”, the captured image extraction unit 33 generates five “entrance type” captured images and 8 “prefectural tournament qualifying” captured images. Assume that a total of 21 captured images are extracted, that is, eight captured images of “expedition”. In addition, regarding “Taro Yamada”, the captured image extraction unit 33 has 13 images of “Iribe-style”, 6 images of “prefectural tournament qualifying”, and 6 images of “expedition”. Assume that one captured image is extracted.

  In this example, in step S <b> 7, the captured image extraction unit 33 determines whether or not there is a player that does not meet the person guide number, and determines that “Yuichi Iguchi” does not reach the person guide number. Therefore, the captured image extraction unit 33 extracts a captured image including “Yuichi Iguchi” as a subject from other scenes for “Yuichi Iguchi”, which is a number that is less than the estimated number of people.

  In this manner, the captured image extraction unit 33 can extract captured images for the target number of persons for each target person.

  In subsequent step S9, the captured image extraction unit 33 evaluates the selection (extraction) result obtained through the selection process according to the predetermined algorithm shown in steps S1 to S9 described above. Specifically, the captured image extraction unit 33 calculates the ratio (%) of the captured image extracted for each target person to the number of captured images of the game scene (the number of captured images corresponding to the game scene). Thus, the evaluation result is obtained.

  In the case of the above example, since all the captured images (25) extracted for “Taro Yamada” are captured images (25) of the game scene, the evaluation result regarding “Taro Yamada” is 100%. . On the other hand, out of all the captured images (25 images) extracted for “Yuichi Iguchi”, only 21 images are the captured images of the game scene, so the number of captured images of the game scene (21 images) is the total captured image (25 images). The evaluation result is 84% based on the value (0.84) calculated by dividing by (sheet).

  In the present embodiment, the ratio of the number of captured images of the game scene in the extracted captured images is calculated as an evaluation result. However, among the extracted captured images, the captured image corresponding to a predetermined event is calculated. The ratio of the number of sheets may be calculated as the evaluation result. Further, the ratio of the number of captured images included as subjects for each player of a predetermined sports club (team, organization) in the extracted captured images may be calculated as an evaluation result.

  In subsequent step S10, the captured image extraction unit 33 transmits the extraction result obtained through the extraction process according to the predetermined algorithm shown in steps S1 to S8 described above to the client terminal 20 such as an editor. Then, on the client terminal 20 side, for example, the extraction result is notified to the editor or the like by performing a display as indicated by reference numeral B6 in FIG.

  In step S10, the captured image extraction unit 33 transmits the evaluation result obtained in step S9 to the client terminal 20 such as an editor. Then, on the client terminal 20 side, for example, an evaluation result is notified to the editor or the like by performing a display as shown by reference numeral B6 in FIG.

  Further, for example, when an editor or the like clicks a number (number of captured images) corresponding to an event with a mouse or the like on the display screen as shown by reference numeral B6 in FIG. It may be displayed. In this case, for example, when the editor or the like clicks “13” indicated by the arrow P11 in FIG. 5 with a mouse or the like, the “Iribe type” captured image extracted for “Taro Yamada” is displayed in the viewer. To do.

  In the present embodiment, as described above, the ratio of the number of captured images of the game scene in the extracted captured images is calculated as an evaluation result and displayed on the client terminal 20. A result may also be calculated, and any one or a plurality of evaluation results may be displayed.

  In step S10, the captured image extraction unit 33 generates an image collection on which the captured images (candidate captured image data to be recorded in the image data collection) extracted through the processing in steps S1 to S8 described above are generated and processed. Ends.

[1-8] Effects As described above, the image collection generation system 1 efficiently generates an image collection that covers a specific event or person by determining an event or person for each captured image. Make it possible. Further, in the image collection generating system 1, by calculating the number of captured images based on the weight set for each event, an image collection in which a large number of captured images are posted for important events set with a large weight. Can be generated.

  Then, in the present image collection generation system 1, when there is a person who is less than the approximate number of persons, the person included in the image collection is extracted from the captured image captured in a scene other than the game, including the person as a subject. As a result, it is possible to extract the same number of captured images.

[2] Others The disclosed technique is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the present embodiment. Each structure and each process of this embodiment can be selected as needed, or may be combined suitably.

  In the embodiment described above, an image collection is created for a specific person among a plurality of persons belonging to a specific community. However, an image collection is created for all persons belonging to the community. It may be a thing. In the above-described embodiment, the image collection is created for a specific event. However, the image collection may be created for all events held in the community.

  In the embodiment described above, the sports club is exemplified as the community, but is not limited thereto. Moreover, although the player was illustrated as a person and the protector was illustrated as a related person, it is not limited to this.

  In the above-described embodiment, the image collection is created through the processing of steps S1 to S10. However, the editor or the like can be re-edited according to the evaluation result or the extraction result displayed on the client terminal 20. Good. In that case, re-editing is facilitated by changing the number of captured image extractions or changing the weight set for each event.

[3] Supplementary Notes Regarding the above embodiment, the following supplementary notes are further disclosed.

(Appendix 1)
Accepts weighting settings for multiple events to which captured image data that can be recorded in an image collection belongs,
In accordance with the weighting received, candidate captured image data to be recorded in the image collection is extracted from captured image data belonging to each of the plurality of events,
Generating an image collection containing the extracted captured image data;
An image collection generating program that causes a computer to execute processing.

(Appendix 2)
Accept settings for the number of captured image data to be recorded in the image collection,
Identify the number of people by referring to the storage unit that stores the person information,
Based on the number of the captured image data received and the specified number of persons, calculate the estimated number of captured image data to be assigned to each person,
Extracting candidate captured image data to be recorded in the image collection from a plurality of the captured image data associated with the person information included in the captured image data so as to approach the calculated number of estimated captured image data.
An image collection generating program that causes a computer to execute processing.

(Appendix 3)
Identify captured image data from which a predetermined clothing image is detected from a plurality of captured image data,
When extracting the captured image data to be included in the image collection from the plurality of captured image data, the captured image data from which the predetermined clothing image is detected has priority over the captured image data from which the predetermined clothing image is not detected. To extract,
An image collection generating program that causes a computer to execute processing.

(Appendix 4)
From one or more captured image data, one or more captured image data is extracted according to a predetermined algorithm as a candidate to be recorded in the image collection,
For the one or more extracted captured image data, the number of captured image data corresponding to a predetermined scene, the number of captured image data corresponding to a predetermined event, and captured image data including a person of a predetermined tissue as a subject, respectively. Output information about one or more of the numbers,
An image collection generating program that causes a computer to execute processing.

(Appendix 5)
Identifying captured image data in which a predetermined person is detected from a plurality of the captured image data,
When extracting captured image data to be included in the image collection from a plurality of the captured image data, extract captured image data from which the predetermined person is detected;
The extracted captured image data is classified and output for each predetermined person.
The image collection generating program according to any one of Supplementary Note 1 to Supplementary Note 4, which causes the computer to execute processing.

(Appendix 6)
Accepts settings relating to the date and time when captured image data that can be recorded in the image collection was captured;
According to the received date and time, extract candidate captured image data to be recorded in the image collection,
The image collection generating program according to any one of Supplementary Note 1 to Supplementary Note 5, which causes the computer to execute processing.

(Appendix 7)
For each person, candidate imaged image data to be recorded in the image collection is selected from the plurality of imaged image data associated with the person information included in the imaged image data so as to be equal to or greater than the calculated number of estimated imaged image data. To extract,
The image collection generating program according to appendix 2, characterized by causing the computer to execute processing.

(Appendix 8)
The captured image data that can be recorded in the image collection includes a moving image,
The image collection generating program according to any one of Supplementary Note 1 to Supplementary Note 7, wherein:

(Appendix 9)
A receiving unit that receives weighting settings for a plurality of events to which captured image data that can be recorded in an image collection belongs;
According to the received weighting, candidate captured image data to be recorded in the image collection is extracted from the captured image data belonging to each of the plurality of events, and an image collection in which the extracted captured image data is recorded is extracted. A generating unit for generating,
An information processing apparatus characterized by that.

(Appendix 10)
Accepts weighting settings for multiple events to which captured image data that can be recorded in an image collection belongs,
In accordance with the weighting received, candidate captured image data to be recorded in the image collection is extracted from captured image data belonging to each of the plurality of events,
Generating an image collection containing the extracted captured image data;
An image collection generating method characterized by the above.

(Appendix 11)
Accepts input of conditions for extracting multiple captured image data,
Corresponds to the date and time or position information satisfying the condition by referring to a storage unit that stores and associates with at least one of the date and time when the plurality of captured image data are captured and the position information where the plurality of captured image data is captured The attached captured image data is extracted, the event in which the extracted captured image data is captured is identified,
Identify the type of scene in the event from the background area included in the extracted captured image data,
The extracted captured image data is classified and output for each identified scene type,
An extraction program that causes a computer to execute processing.

DESCRIPTION OF SYMBOLS 1 Image collection generation system 10 Information processing apparatus 11 CPU
DESCRIPTION OF SYMBOLS 12 Memory | storage part 13 Memory 14 IF part 15 Input part 16 Output part 20 Client terminal 21 CPU
22 storage unit 23 memory 24 IF unit 25 input unit 26 output unit 27 imaging unit 31 extraction condition determination unit (reception unit)
32 Captured Image Determination Unit 33 Captured Image Extraction Unit (Generation Unit)
41 Person information 42 Member information 43 Event information 44 Captured image group 90 Network 91 Generation program 92 Upload program 93 Captured image

Claims (10)

Accepts weighting settings for multiple events to which captured image data that can be recorded in an image collection belongs,
In accordance with the weighting received, candidate captured image data to be recorded in the image collection is extracted from captured image data belonging to each of the plurality of events,
Generating an image collection containing the extracted captured image data;
An image collection generating program that causes a computer to execute processing. Accept settings for the number of captured image data to be recorded in the image collection,
Identify the number of people by referring to the storage unit that stores the person information,
Based on the number of the captured image data received and the specified number of persons, calculate the estimated number of captured image data to be assigned to each person,
Extracting candidate captured image data to be recorded in the image collection from a plurality of the captured image data associated with the person information included in the captured image data so as to approach the calculated number of estimated captured image data.
An image collection generating program that causes a computer to execute processing. Identify captured image data from which a predetermined clothing image is detected from a plurality of captured image data,
When extracting the captured image data to be included in the image collection from the plurality of captured image data, the captured image data from which the predetermined clothing image is detected has priority over the captured image data from which the predetermined clothing image is not detected. To extract,
An image collection generating program that causes a computer to execute processing. From one or more captured image data, one or more captured image data is extracted according to a predetermined algorithm as a candidate to be recorded in the image collection,
For the one or more extracted captured image data, the number of captured image data corresponding to a predetermined scene, the number of captured image data corresponding to a predetermined event, and captured image data including a person of a predetermined tissue as a subject, respectively. Output information about one or more of the numbers,
An image collection generating program that causes a computer to execute processing. Identifying captured image data in which a predetermined person is detected from a plurality of the captured image data,
When extracting captured image data to be included in the image collection from a plurality of the captured image data, extract captured image data from which the predetermined person is detected;
The extracted captured image data is classified and output for each predetermined person.
The image collection generating program according to any one of claims 1 to 4, wherein the computer causes the computer to execute processing. Accepts settings relating to the date and time when captured image data that can be recorded in the image collection was captured;
According to the received date and time, extract candidate captured image data to be recorded in the image collection,
The image collection generating program according to claim 1, wherein the computer causes the computer to execute processing. For each person, candidate imaged image data to be recorded in the image collection is selected from the plurality of imaged image data associated with the person information included in the imaged image data so as to be equal to or greater than the calculated number of estimated imaged image data. To extract,
The image collection generating program according to claim 2, wherein the computer causes the computer to execute processing. The captured image data that can be recorded in the image collection includes a moving image,
The image collection generating program according to any one of claims 1 to 7, wherein A receiving unit that receives weighting settings for a plurality of events to which captured image data that can be recorded in an image collection belongs;
According to the received weighting, candidate captured image data to be recorded in the image collection is extracted from the captured image data belonging to each of the plurality of events, and an image collection in which the extracted captured image data is recorded is extracted. A generating unit for generating,
An information processing apparatus characterized by that. Accepts weighting settings for multiple events to which captured image data that can be recorded in an image collection belongs,
In accordance with the weighting received, candidate captured image data to be recorded in the image collection is extracted from captured image data belonging to each of the plurality of events,
Generating an image collection containing the extracted captured image data;
An image collection generating method characterized by the above.