Apparatus and method for providing automated sports training session, and recording medium storing instructions

Abstract

The present disclosure relates to: a method performed by an electronic device; an apparatus; and a recording medium. The apparatus according to one embodiment of the present disclosure comprises: a communication circuit; one or more processors; and one or more memories in which instructions executed by the one or more processors are stored, wherein, when the one or more instructions are executed, the one or more processors can be configured to: receive, from one or more cameras through the communication circuit, a training video obtained by photographing a target training round; determine, on the basis of the training video, whether a predetermined training event has occurred in the target training round; as a result of determining that the training event has occurred, generate, on the basis of the training video, a replay video including an analysis result for the target training round; and transmit the replay video to an output device.

Description

Recording medium containing a device, method and command for providing an automated sports training session

[0001] The present disclosure relates to techniques for providing a sports training session to a user.

[0002] Modern sports training systems aim to enhance athlete training efficiency and optimize performance through technological advancements. Therefore, precise analysis of various movements and situations occurring in real time during training may be necessary. Conventional training methods have primarily relied on the experience and intuition of coaches and athletes. However, it is difficult for coaches to accurately monitor and provide feedback on the numerous movements and events occurring in real time during training. This limitation potentially leads to the overlooking of crucial elements for individual athlete performance improvement. Furthermore, conventional analog recording and analysis methods are not only time-consuming but also prone to human error.

[0003] At least one embodiment of the present disclosure may provide analysis results for sports training performance.

[0004] At least one embodiment of the present disclosure can provide real-time feedback on the training movements performed and their results through analysis of the performance of sports training.

[0005] At least one embodiment of the present disclosure may provide interaction with a user performing sports training by providing analysis results and information about the progress of the training to the user during the training.

[0006] The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art of the present disclosure from the description below.

[0007] A device according to one aspect of the present disclosure may include: a communication circuit; one or more processors; and one or more memories storing instructions to be executed by the one or more processors, wherein, upon execution of the one or more instructions, the one or more processors may be configured to receive, through the communication circuit, a training image captured by a target training repetition from one or more cameras, determine based on the training image whether a predetermined training event has occurred in the target training repetition, and generate a replay image including an analysis result for the target training repetition based on the training image in response to a determination that the training event has occurred, and transmit the replay image to an output device.

[0008] In one embodiment, the one or more processors may be further configured to generate object information about attributes of each of one or more objects within the training area of ​​the target training session and motion information about movement of each of the one or more objects based on the training images, and to determine whether the training event has occurred based on the object information and the motion information.

[0009] In one embodiment, the one or more objects may include at least one of a player corresponding to the target training session, a ball, the training area, or a training device placed in the training area.

[0010] In one embodiment, the one or more processors may be further configured to obtain training item information indicating a training item of the target training session, obtain characteristic information of the training event corresponding to the training item based on the training item information, and determine whether the training event has occurred based on whether the object information and the motion information correspond to the characteristic information, in determining whether the training event has occurred.

[0011] In one embodiment, the one or more processors may be further configured to receive the training item information from a user terminal through the communication circuit when obtaining the training item information.

[0012] In one embodiment, the one or more processors may be further configured to, when obtaining the training item information, identify a player corresponding to the target training session based on the object information and the motion information, and obtain the training item information based on the identified player.

[0013] In one embodiment, the analysis result may include at least one of a player corresponding to the target training session, one or more values ​​related to the training event that occurred in the target training session, success or failure of the training event, or progress information for a training set including the target training session.

[0014] In one embodiment, the one or more processors may be further configured to store information indicating the analysis result in association with player information for a player corresponding to the target training session.

[0015] In one embodiment, the one or more processors may be further configured to transmit the analysis results to a user terminal via the communication circuit.

[0016] In one embodiment, the one or more processors may be further configured to transmit, via the communication circuit, information about a training session subsequent to the target training session to the user terminal upon determining that the training event has occurred.

[0017] In one embodiment, the one or more processors may be further configured to determine a training item for a training set subsequent to the target training set based on player information corresponding to the target training session and the analysis result, upon determining that the target training set including the target training session has been completed, and to transmit information on the determined training item to the user terminal through the communication circuit.

[0018] In one embodiment, the one or more cameras may include one or more high-speed cameras, and the training images may include high-speed camera images taken of the target training session by the one or more high-speed cameras.

[0019] In one embodiment, the one or more processors may be configured to detect an event occurrence time corresponding to the training event and an event occurrence area among image areas included in the training image, upon determining that the training event has occurred, when generating the replay image, and to generate the replay image based on the event occurrence time, the event occurrence area, and the training image.

[0020] In one embodiment, the one or more processors may be configured to generate the replay image based on training images captured between a time point before a first time interval specified from the time point of occurrence of the event and a time point after a second time interval specified from the time point of occurrence of the event.

[0021] In one embodiment, the one or more processors may be further configured to determine shooting settings for the one or more cameras based on the event occurrence area, and to transmit information about the determined shooting settings to the one or more cameras through the communication circuit.

[0022] In one embodiment, the one or more processors may be further configured to control the output device to display, via the communication circuit, visual information corresponding to the training event on at least a portion of a training area of ​​the target training session.

[0023] In one embodiment, the output device may include a light output device that illuminates at least a portion of the training area with light.

[0024] In one embodiment, the one or more processors may be further configured to generate the analysis result based on the location of the area where the training image and the visual information are displayed.

[0025] According to another aspect of the present disclosure, a method performed in a device including one or more processors and one or more memories storing instructions to be executed by the one or more processors may include: receiving, by the one or more processors, training images captured during a target training session from one or more cameras through a communication circuit; determining, based on the training images, whether a predetermined training event has occurred during the target training session; generating, based on the training images, a replay image including analysis results for the target training session in response to a determination that the training event has occurred; and transmitting the replay image to an output device.

[0026] A non-transitory computer-readable recording medium having recorded thereon instructions to be executed by one or more processors according to another aspect of the present disclosure, wherein the instructions, when executed, cause the one or more processors to receive, through a communication circuit, a training image captured during a target training session from one or more cameras, determine based on the training image whether a predetermined training event has occurred during the target training session, and generate, based on the training image, a replay image including an analysis result for the target training session, and transmit the replay image to an output device.

[0027] According to various embodiments of the present disclosure, a quantitative and analytical training environment can be provided by providing analysis results on sports training performance.

[0028] According to various embodiments of the present disclosure, it is expected that the quality of a sports training session will be improved by providing real-time feedback on sports training during the performance of the sports training.

[0029] According to various embodiments of the present disclosure, a personalized training environment can be provided by providing interaction with a user regarding sports training.

[0030] The effects according to the technical idea of ​​the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the specification.

[0031] FIG. 1 is a drawing showing a training center where sports training is performed according to one embodiment of the present disclosure.

[0032] FIG. 2 is a diagram showing the operation process of a device according to one embodiment of the present disclosure.

[0033] FIG. 3 is a block diagram of a device according to one embodiment of the present disclosure.

[0034] Figure 4 is a drawing showing a training video of sports training.

[0035] FIG. 5 is a diagram illustrating a process for generating object information according to one embodiment of the present disclosure.

[0036] FIG. 6 is a diagram illustrating a process for generating motion information of an object according to one embodiment of the present disclosure.

[0037] FIG. 7 is a diagram illustrating a process for detecting a training event according to one embodiment of the present disclosure.

[0038] FIG. 8 is a diagram for explaining analysis information for sports training according to one embodiment of the present disclosure.

[0039] FIG. 9 is a diagram for explaining analysis information for sports training according to one embodiment of the present disclosure.

[0040] FIG. 10 is a diagram for explaining analysis information for sports training according to one embodiment of the present disclosure.

[0041] FIG. 11 is a diagram illustrating analysis information for sports training according to one embodiment of the present disclosure.

[0042] FIG. 12 is a drawing for explaining a replay image for sports training according to one embodiment of the present disclosure.

[0043] FIG. 13 is a drawing for explaining a replay image for sports training according to one embodiment of the present disclosure.

[0044] FIG. 14 is a drawing for explaining a process in which a device according to one embodiment of the present disclosure controls an output device.

[0045] FIG. 15 is a diagram illustrating a learning model learned to determine whether an event has occurred in a target training session according to one embodiment of the present disclosure and to generate analysis information and a replay image.

[0046] FIG. 16 is a flowchart of a method for providing an automated training session according to one embodiment of the present disclosure.

[0047] The various embodiments described in this disclosure are exemplified for the purpose of clearly explaining the technical concept of this disclosure and are not intended to be limited to specific embodiments. The technical concept of this disclosure includes various modifications, equivalents, alternatives, and embodiments selectively combined from all or part of the embodiments described in this disclosure. Furthermore, the scope of the technical concept of this disclosure is not limited to the various embodiments presented below or the specific descriptions thereof.

[0048] Terms used in this disclosure, including technical or scientific terms, unless otherwise defined, may have the meaning commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0049] The expressions "includes," "may include," "comprises," "may have," "have," and "may have" used in this disclosure indicate the presence of a target feature (e.g., a function, operation, or component), but do not exclude the presence of other additional features. In other words, such expressions should be understood as open-ended terms that imply the possibility of including other embodiments.

[0050] The singular expressions used in this disclosure may include the plural meaning unless the context clearly indicates otherwise, and the same applies to the singular expressions set forth in the claims.

[0051] The expressions "first," "second," or "first", "second", etc. used in this document, unless the context indicates otherwise, are used to refer to multiple similar objects and to distinguish one object from another, and do not limit the order or importance among the objects.

[0052] As used herein, the expressions "A, B, and C", "A, B, or C", "A, B, and / or C", or "at least one of A, B, and C", "at least one of A, B, or C", "at least one of A, B, and / or C", etc., may refer to each of the listed items or to all possible combinations of the listed items. For example, "at least one of A or B" may refer to (1) at least one A, (2) at least one B, (3) at least one A and at least one B.

[0053] The expression "based on" as used in this disclosure is used to describe one or more factors that influence a decision, act of judgment, or action described in a phrase or sentence containing this expression, and this expression does not exclude additional factors that influence the decision, act of judgment, or action.

[0054] As used herein, the expression that a component (e.g., a first component) is “connected” or “connected” to another component (e.g., a second component) may mean that the component is directly connected or connected to the other component, as well as connected or connected via a new other component (e.g., a third component).

[0055] The expression "configured to" used in the present disclosure may have the meanings of "set to", "having the ability to", "modified to", "made to", "capable of", etc., depending on the context. This expression is not limited to the meaning of "specifically designed in hardware", and for example, a processor configured to perform a specific operation may mean a general purpose processor that can perform the specific operation by executing software, or a special purpose computer that is structured through programming to perform the specific operation.

[0056] In the present disclosure, a "learning model" may be designed to implement the structure of a human brain on a computer, and may include a plurality of network nodes that simulate neurons of a human neural network and have weights. The plurality of network nodes simulate the synaptic activity of neurons that exchange signals through synapses and may have connections among themselves. In the learning model, the plurality of network nodes may be located at layers of different depths and exchange data according to convolutional connections. For example, the learning model may be an artificial neural network model, a time series analysis model, etc. Here, the time series analysis model is a model that analyzes time series data, and may be a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, or an Autoregressive Integrated Moving Average (ARIMA) model. Meanwhile, the present disclosure is not limited thereto, and various types of models for analyzing data may be used.

[0057] In the present disclosure, a “training process” may mean a process in which a learning model extracts and analyzes features (patterns) of input data and output data pairs of learning data, repeats the process of deriving correlations between input and output data, and optimizes parameters of the learning model based on the correlations between input and output data.

[0058] In the present disclosure, an “inference process” may mean a process in which a learning model applies a previously learned pattern to new input data to generate output data as a result of prediction or classification of the input data.

[0059] Hereinafter, various embodiments of the present disclosure will be described with reference to the attached drawings. In the attached drawings and the description of the drawings, identical or substantially equivalent components may be assigned the same reference numerals. Furthermore, in the description of various embodiments below, duplicate descriptions of identical or corresponding components may be omitted, but this does not mean that such components are not included in the embodiments.

[0060] FIG. 1 is a diagram illustrating a training center (100) where sports training is performed according to one embodiment of the present disclosure. In the present disclosure, sports training may include sports training for various ball and non-ball sports, and is not limited to sports training for a single sport. Furthermore, the training types for sports training in the present disclosure may include various training types corresponding to one or more sports. Multiple sports training types may correspond to a single sport, and the various training types may correspond to various physical training techniques, skill training techniques, and the training types are not limited to a single training type.

[0061] In the present disclosure, sports training may be performed in units of sports training repetitions. A training repetition may be designated as a single session when repeatedly performing training for a specific training item.

[0062] In the present disclosure, sports training may be performed in units of training sets. A training set may include one or more training sessions for a specific training event.

[0063] In the present disclosure, a training ground (100) may include a camera group (130, 140) that photographs a training area (110) within the training ground. The camera group (130, 140) may include one or more cameras (131, 132, 133, 134, 135, 136, 141, 142, 143). The camera group (130, 140) may photograph the training area (110) of sports training from one or more angles through one or more cameras that photograph each area within the training ground (100), thereby generating a training video of the corresponding sports training. In one embodiment, the camera group (130, 140) may include a first camera group (130) and a second camera group (140). The first camera group (130) may include one or more cameras (131, 132, 133, 134, 135, 136), and the one or more cameras included in the first camera group (130) may capture training images for a target training session performed within the training area (110). In one embodiment, the first camera group (130) may include one or more cameras (131, 132, 133, 134, 135, 136) for acquiring first image information for generating analysis results for the target training session. In one embodiment, the first camera group (130) may include a high-speed camera for capturing high-speed camera images. For example, the one or more high-speed cameras included in the first camera group (130) may capture images of a large number of frames (e.g., 120 fps or more) in the same period of time compared to a general camera, and may be used to capture very fast movements. Additionally, high-resolution images can be acquired with a short exposure time. One or more cameras of the first camera group (140) can capture objects (120) including various subjects, including users performing training in a target training session.In one embodiment, the second camera group (140) may include one or more cameras (141, 142, 143) for acquiring second image information for generating a replay video. The second camera group (140) has a wide field of view (FOV) and may capture the second image information to include various objects (120) around the user, including the user performing the target training session.

[0064] The training field (100) may include a training area (110). For example, the training area (110) may refer to a predetermined three-dimensional space related to sports training. Specifically, the training area (110) may include an area where sports training takes place (e.g., a court), an area where sports training objects (120) may be located, an area where output devices (180) within the training field (100) may be located, etc. Meanwhile, the object (120) may include at least one of an object or ball located in the training area (110), such as a sports training player, a coach, or sports training equipment (e.g., a net, a racket, a goal post).

[0065] For example, at least some of the cameras (131, 132, 133, 134, 135, 136, 141, 142, 143) of the camera group (130, 140) may be fixed cameras, and others may be mobile cameras. The fixed camera may refer to a camera that photographs at least a part of the training area (110) at a predetermined angle, magnification, etc. The mobile camera may be a camera that moves while tracking at least one object (120) within the training area (110). The mobile camera may track and photograph the object (120) while moving according to a preset, and the preset may be one or more settings for camera parameters such as a camera position, a shooting angle, and a magnification. The number and arrangement of cameras within the camera group (130, 140) may vary depending on the sport, the sport training, the shape (e.g., size) of the training field, etc.

[0066] The training area (100) may include one or more output devices (180). The output devices (180) may include a display (e.g., an electronic scoreboard) for providing feedback on sports training performed in the training area (100), and an optical output device (e.g., a projector) for displaying visual information such as images within the training area (110) by irradiating light within the training area (110).

[0067] FIG. 2 is a diagram illustrating an operation process of a device (101) according to one embodiment of the present disclosure. Referring to FIG. 2, the device (101) can obtain a training image (150). The training image (150) can include various pieces of information that the device (101) obtains in real time as the target training session progresses within the training area (110). In one embodiment, the training image (150) can include first image information (138) obtained from a first camera group (130) and second image information (148) obtained from a second camera group (140).

[0068] In one embodiment, each camera of the camera group (130, 140) can capture a training area (110) of a sports training session at a predetermined frame rate (e.g., 120 fps) to generate a training video (150) of the corresponding sports training session. Here, the training video (150) can include an image set obtained by dividing the captured video of the target training session into units of shooting frames (e.g., 120 fps).

[0069] In one embodiment, the device (101) can receive training images (150) periodically, continuously, or aperiodically from one or more cameras in the camera group (130, 140). The device (101) can acquire training images (150) in real time or substantially near real time.

[0070] The device (101) can obtain training information (160) from the user terminal (102). The training information (160) may be preset information that corresponds to a target training session, or may include data controlled or input by a user, a training instructor, a manager, etc.

[0071] For example, training information (160) may include at least one of player information (161), round information (162), and set information (163). Player information (161) may include information about a player performing a target training round. Player information (161) may include information about one or more players who are the target of training. For example, player information (161) may include various information about the players, such as each player's name, number, position, the player's game result record, and the player's past training record. Round information (162) may include information about the target training round. For example, round information (162) may include various information about the target round, such as a training item corresponding to the target training round, a training set, the order of rounds within the training set, feature information corresponding to the target round, and training progress information. In this case, the feature information may include information about a motion corresponding to the training item of the target round. The characteristic information may include information that can be detected in the training event of the target round, information about the conditions of each value, information that is the target of feedback in the target round, etc. For example, if the sport of the target training round is "volleyball" and the training event of the target round is "serve," the motion corresponding to the training event may be the motion for the serve motion performed by the player and the motion for the trajectory of the ball according to the serve motion, and information that can be detected by the serve motion may include information such as the height of the point of impact at which the serve motion is performed, the coordinates of the point of impact, the launch angle of the hit ball, the direction angle of the hit ball, the speed of the ball, the height from the net when the ball passes over the net (the height above the net), the coordinates of the point of impact of the ball, and information about the path the ball travels. In addition, the detection condition may be the condition of the ball being hit and the hit ball passing over the net. In addition, information that is the target of feedback may include speed, passing height, launch angle, direction angle, height of the hit point, success or failure, etc.In one embodiment, the device (101) can analyze a target training session based on feature information. The set information (163) can include information about the training set to which the target training session belongs. The set information (163) can include information about the training items of the target training set, the number of training sessions included in the target training set, and the progress of the target training set. In one embodiment, the session information (162) and the set information (163) can be updated based on the analysis result (20) generated by the device (101). For example, the device (101) can generate the analysis result (20) of the target training session and update the session information (162) and the set information (163) included in the training information (160) based on the analysis result (20).

[0072] In the present disclosure, a user terminal (102) can provide training information (160) to a device (101). According to one embodiment, the user terminal (102) can include terminals of various users, such as match analysts, coaches, and players.

[0073] The device (101) can determine whether a training event (10) has occurred in a target training session based on at least a portion of the training video (150) and the training information (160). The training event (10) can be understood as various situations resulting from the progress of sports training within the target training session. For example, the device (101) can identify, as a training event (10), a situation in which a ball is hit, a situation in which the ball goes over the net, a situation in which the ball gets caught in the net, a situation in which a player jumps, a situation in which a ball is dropped, a specific action corresponding to a player performing each target training session, or a result of a specific movement by a training object. In one embodiment, the training event (10) may be predetermined in correspondence with the target training session. For example, the training event (10) may be predetermined in relation to a training item corresponding to the target training session. For example, if the training subject is a receiving motion in volleyball, the training event (10) may include an event in which the ball goes over the net, an event in which the ball falls, and an event in which the player hits the ball. The device (101) may identify a predetermined training event (10) corresponding to a target training session based on training information (160). The predetermined training event (10) may be identified based on characteristic information of the training subject identified based on the training information (160). The device (101) may determine a predetermined training event (10) corresponding to a target training session based on the characteristic information of the training subject, and may determine whether the predetermined training event (10) has occurred in the target training session based on image information (150). In one embodiment, the device (101) may detect a training event (10) based on first image information (138) acquired through the first camera group (130) among the image information (150). The first image information (138) may include a high-speed camera image acquired through a high-speed camera included in the first camera group (130).The device (101) can detect a training event (10) based on a high-speed camera image.

[0074] In one embodiment, the device (101) may determine whether a training event (10) occurs based on at least one of a training video (150) and training information (160). For example, the device (101) may analyze the training video (150) and determine whether a training event (10) occurs based on whether motion corresponding to characteristic information for each training item identified based on the training information (160) is detected within the training video (150). In one embodiment, the device (101) may identify an athlete corresponding to a target training session based on the training video (150) and athlete information (161), and obtain information on a training item of the athlete corresponding to the target training session based on the session information (162). The device (101) may obtain information on a training item to be performed by a specific athlete identified based on the training video (150) according to the training progress of the corresponding athlete, and determine whether a training event (10) occurs based on the characteristic information for the training item.

[0075] In one embodiment, the device (101) can determine whether an event has occurred based on a training image (150). For example, the device (101) can analyze a training image (150) among the training images (150) to determine whether a training event (10) has occurred.

[0076] The device (101) can generate an analysis result (20) based on at least a portion of the training video (150) and the training information (160). The analysis result (20) can include at least one of an analysis result for a target training session and an analysis result for a target training set. The analysis result (20) for the target training session can be generated based on motion information detected based on the training video (150) of the training event (10). The analysis result (20) can include result information determined based on each numerical information and a combination of numerical information of the training event (10) corresponding to the target training session. For example, the analysis result (20) can include information on the speed of the ball, the trajectory of the ball, the location of the hitting point, the coordinates of the hitting point, the height above the net, the net passing speed, the landing point, the launch angle, the direction angle, etc. according to the occurrence of the training event (10), and information on whether it was successful or not determined from a combination thereof. In one embodiment, the analysis result (20) may include a trend in training performance generated based on past training information and result information of the target training session, and may include a comprehensive analysis result corresponding to a training set including the target training session. For example, statistical information corresponding to the target training session may be included in the analysis result (20) based on result information corresponding to each training session. The analysis result (20) may include, for each training set, information such as the average height of the hitting point, the average ball speed, whether it was successful, the success rate, and training result information for each player. For example, the analysis result (20) for car racing training may include information such as the vehicle's speed, path, steering angle, acceleration, deceleration, track entry position, track departure, speed per lap, real-time lap time, cornering angle, etc. according to the occurrence of the training event (10), and information on the player's capability evaluation and statistics derived from a combination thereof.In addition, the analysis results (20) may include an analysis of the performance trends of the player generated based on past training information and result information of the target training session, and may also include comprehensive analysis results corresponding to the training set including the target training session. For example, statistical information corresponding to the target training session may be included in the analysis results (20) based on result information corresponding to each training session. The analysis results (20) may include, for each training set, average vehicle speed, whether or not the coursework was successful, success frequency, average interval when exiting a corner, and training result information for each player.

[0077] The device (101) can store the analysis results (20) of the training event (10). In one embodiment, the device (101) can store the analysis results (20) of the training event (10) in association with player information (162) corresponding to the target training session.

[0078] In one embodiment, the device (101) may analyze the training event (10) upon determining that the training event (10) has occurred and determine that the target training session has been completed as a training result. The device (101) may generate or update result information for the training set upon determining that the target training session has been completed. The result information for the training set may include information on the progress of the target training set, such as the number of training sessions included in the target training set, the number of completed training sessions, training results and statistics for each training session included in the training set, etc. The result information for the training set may be included in the analysis result (20).

[0079] In one embodiment, the device (101) can determine that the target training set is completed based on the result information of the target training set. The device (101) can determine whether the target training set is completed based on the number of training sessions corresponding to the target training set. In one embodiment, the device (101) can determine the training items of the next training set based on the determination that the target training set is completed. The device (101) can determine the training items for the next training set to be performed by the player based on the player information corresponding to the target training set and the training record corresponding to the player. For example, the device (101) can determine the items of the training set to be performed after the completed training set based on information such as each player, the routine of the training items designated for each player, each player's position, or items requiring supplementary training determined based on each player's training record.

[0080] The device (101) can transmit the analysis result (20) to the user terminal (102). The device (101) can generate the analysis result (20) upon determining that a training event (10) has occurred and transmit the generated analysis result (20) to the user terminal (20). The device (101) can transmit the analysis result (20) for the target training session to the user terminal (20) upon determining that the target training session is completed. The device (101) can transmit the analysis result (20) of the target training set to the user terminal (20) upon determining that the target training set is completed.

[0081] The device (101) can generate a replay video (30). The device (101) can generate the replay video (30) based on a determination that a training event (10) has occurred. The device (101) can generate the replay video (30) based on the training video (150) and the analysis result (20).

[0082] In one embodiment, the device (101) may detect an event occurrence area of ​​the training video (150) in which the training event (10) has occurred, based on a determination that a training event (10) has occurred. The device (101) may extract a portion of the image area of ​​the training video (150) based on the event occurrence area of ​​the image area of ​​the training video (150), or may adjust the magnification of the training video (150) to generate a replay video (30). In one embodiment, the device (101) may extract a training video (150) within a specified time interval corresponding to the event occurrence time among the training video (150) based on the occurrence time of the training event (10). For example, a training video (150) captured during a time interval between a time point before a first time interval specified from the occurrence time of the training event (10) and a time point after a second time interval specified from the occurrence time of the training event (10) may be extracted. In this case, the first time interval may be shorter than the second time interval. In one embodiment, when multiple training events (10) occur in a target training session, the device (101) may extract a training image (150) for each of the multiple training events (10), or extract a training image (150) corresponding to a time interval between the time points corresponding to the first training event (10) and the last training event (10) among the multiple training events (10), and generate a replay image (30). The device (101) may generate a replay image (30) based on the training image (150) extracted corresponding to a specified time interval. The device (101) may generate a replay image (30) based on a partial region extracted based on an event occurrence region corresponding to the training event (10), from the training image (150) corresponding to the specified time interval extracted corresponding to the time points of occurrence of the training event (10). At this time, the size or position of the extracted partial region may change depending on the occurrence of consecutive training events (10) within the specified time interval.For example, as the occurrence area of ​​a training event (10) changes in the same training session, the image area to be included in the replay image (30) in the training image (150) may gradually change.

[0083] In one embodiment, the device (101) can generate a replay image (30) based on the analysis result (20). The replay image (30) can include the analysis result (20) in at least a portion of the replay image. For example, the replay image (30) can include a region displaying the training image (150) and a region displaying the analysis result (20). In one embodiment, when multiple training events (10) occur in a target training session, the replay image (30) includes a training image (150) for each of the multiple training events (10) that occur in the target training session, and the device (101) can generate the replay image (30) to include an analysis result (20) corresponding to each training event (10) at a time corresponding to each of the multiple training events (10). For example, in a training session for volleyball attack training, a replay video (30) can be generated to display the analysis result (20) for a toss event at the time the toss event occurs, and to display the analysis result (20) for a spike event at the time the spike event occurs.

[0084] The device (101) can transmit the replay image (30) to the output device (180). In one embodiment, the device (101) can transmit the replay image (30) to the output device (180) upon determining that a training event (10) has occurred. In one embodiment, the output device (180) can be positioned within the training area (110) and outputs the replay image (30) for the target training session, thereby providing the user with visual feedback for the target training session.

[0085] In one embodiment, the device (101) can transmit a replay image (30) to a user terminal (102).

[0086] The device (101) can store the replay video (30) in association with player information (162) corresponding to the target training session.

[0087] In one embodiment, the device (101) can generate a replay image (30) based on second image information (148) obtained from a second camera group (140) among the camera groups (130, 140).

[0088] In one embodiment, the device (101) can determine the shooting settings based on the event occurrence area where the training event (10) occurred. The device (101) can determine the shooting settings, such as the location of the area where the camera group (130, 140) will shoot the training round and the magnification when shooting, in response to the area where the training event (10) occurred. Based on the determined shooting settings, the device (101) can control the area and magnification where the camera group (130, 140) will shoot the next training round after the target training round is completed.

[0089] In one embodiment, the device (101) may control the output device (180) to display visual information corresponding to a training event (10) on at least a portion of the training area (110). For example, the device (101) may control an optical output device (e.g., a projector) among the output devices (180) to display visual information corresponding to a designated training event (10) corresponding to a target training session on the training area (110). The visual information may be information that assists a player in performing a training event (10) that is to occur in a designated training session for the target training session. For example, the visual information may include visual information regarding a position at which a player is to be positioned in the target training session, a trajectory at which the player is to move, a trajectory at which the ball is to draw, a position at which a point of impact is to occur, a position at which the ball is to land, and the like. The visual information may include information that displays a result corresponding to a training event (10) that occurred in a target training set. For example, visual information about the position where a player was positioned in a target training session, the path the player moved, the path the ball's trajectory moved, the position where the hit occurred, the position where the ball landed, etc. can be displayed by the output device (180).

[0090] FIG. 3 is a block diagram of a device (101) according to one embodiment of the present disclosure. In one embodiment, the device (101) may include one or more processors (210) and / or one or more memories (220) as elements. In one embodiment, at least one of the elements of the device (101) may be omitted, or another element may be added to the device (101). In one embodiment, additionally or alternatively, some of the elements may be implemented in an integrated manner or implemented as a single or multiple entities. One or more processors may be referred to as processors (210). The expression “processor (210)” may mean a set of one or more processors, unless the context clearly indicates otherwise. One or more memories may be referred to as memories (220). The expression “memory (220)” may mean a set of one or more memories, unless the context clearly indicates otherwise. At least some of the components inside / outside the device (101) are connected to each other through a bus, GPIO (general purpose input / output), SPI (serial peripheral interface), MIPI (mobile industry processor interface), etc., and can exchange information (data, signals, etc.).

[0091] In one embodiment, the processor (210) may control at least one component of the device (101) connected to the processor (210) by executing instructions (e.g., code, software, program, etc.). In addition, the processor (210) may perform various operations such as calculations, processing, data generation, and processing related to the present disclosure. In addition, the processor (210) may load data, etc. from the memory (220) or store data in the memory (220). For example, the processor (210) may receive training images (150) from a camera group (130, 140) that captures a target training session, and receive training information (160) from a user terminal (102). The processor (210) determines whether a training event (10) has occurred based on the received data, generates an analysis result (20) and a replay image (30) based on the determination that a training event (10) has occurred, and transmits the replay image (30) to an output device (180) included in the training field (100), or generates a control signal for controlling the output device (180) so that the output device (180) displays visual information and transmits the control signal to the output device (180).

[0092] In one embodiment, the memory (220) can store various data. The data stored in the memory (220) is data acquired, processed, or used by at least one component of the device (101), and may include instructions (e.g., code, software, programs, etc.). The memory (220) may include volatile and / or non-volatile memory. The instructions or programs are software stored in the memory (220), and may include an operating system for controlling the resources of the device (101), an application, and / or middleware that provides various functions to the application so that the application can utilize the resources of the device (101). For example, the memory (220) may store instructions that, when executed by the processor (210), cause the processor (210) to perform operations. The memory (220) can store training images (150) of the target training session, analysis results (20), training information (160), information about an occurred training event (10), replay images (30), control signals for controlling an output device (180), etc. In addition, the memory (220) can store training images, training information, etc. of one or more other sports training sessions. In one embodiment, the processor (210) can control a communication circuit to obtain information from another server or device. The information obtained in this way can also be stored in the memory (220).

[0093] In one embodiment, the device (101) may further include a communication circuit (230). The communication circuit (230) may be omitted from the device (101) depending on the embodiment. The communication circuit (230) may perform wireless or wired communication between the device (101) and another server, or between the device (101) and another device. For example, the communication circuit (230) may perform wireless communication according to a method such as eMBB (enhanced Mobile Broadband), URLLC (Ultra Reliable Low-Latency Communications), MMTC (Massive Machine Type Communications), LTE (Long-Term Evolution), LTE-A (LTE Advance), NR (New Radio), UMTS (Universal Mobile Telecommunications System), GSM (Global System for Mobile communications), CDMA (Code Division Multiple Access), WCDMA (Wideband CDMA), WiBro (Wireless Broadband), WiFi (Wireless Fidelity), Bluetooth (Bluetooth), NFC (Near Field Communication), GPS (Global Positioning System), or GNSS (Global Navigation Satellite System). For example, the communication circuit (230) may perform wired communication according to a method such as USB (Universal Serial Bus), HDMI (High Definition Multimedia Interface), RS-232 (Recommended Standard-232), or POTS (Plain Old Telephone Service).For example, the communication circuit (230) may be used to perform communication with a camera group (130, 140), a user terminal (102), an output device (180), etc. The communication circuit (230) may be implemented as a circuit or chip configured to perform data transmission and reception.

[0094] In one embodiment, the device (101) may further include an input / output interface. The input / output interface may be omitted from the device (101) depending on the embodiment. For example, the input / output interface may include an input device and / or an output device. The input device may receive various information from an e-commerce service provider and transmit the input information to at least one component of the device (101). The output device may receive various information output by at least one component of the device (101) and provide (display) the information to the service provider in an audiovisual form. For example, the input device may include a mouse, a keyboard, a touch pad, etc. For example, the output device may include a display, a projector, a hologram, etc.

[0095] In one embodiment, the device (101) may be a device of various forms. For example, the device (101) may be a computer device, a back-end server, a front-end server, a portable communication device, a portable multimedia device, a wearable device, a device according to a combination of the aforementioned devices, or a chip, board, circuit, etc. within the aforementioned devices. However, the device (101) of the present disclosure is not limited to the aforementioned devices.

[0096] FIG. 4 is a diagram illustrating a training video (150) of sports training. In one embodiment, each of one or more cameras included in a camera group (130, 140) can capture a target object (120) for a training area (110) or a training event (10) within the training area (110) at a predetermined frame rate (e.g., 120 fps) to generate a training video (150). For example, the training video (150) can indicate an image set including one or more images (301, 302, 303, 304, 305) obtained by dividing a video of sports training captured by one or more cameras into frames. In the case of sports training of a ball game, one or more images (301, 302, 303, 304, 305) can capture a player, a ball, equipment (e.g., a racket), training facility (e.g., a net, court lines), etc. as target objects. For example, one or more images (301, 302, 303, 304, 305) can be used to determine motion information of an object (120) within a training area (110) of sports training and whether a training event (10) of sports training occurs. For example, motion information for each of one or more objects (120) is generated based on one or more images, and the motion information for each of one or more objects (120) within the training area (110) can be used to determine whether an event occurs.

[0097] FIG. 5 is a diagram illustrating a process for generating object information (127) according to one embodiment of the present disclosure. In one embodiment, the processor (210) receives a training image (150) including an image set in which the image of the target training session is divided into frames from a camera group (130), and can generate frame-by-frame position information (125) of an object (120) within a training area (110) of the target training session based on the image set. For convenience of explanation, it is assumed that the object (120) of FIG. 5 is a player of the target training session.

[0098] In one embodiment, the processor (210) may determine frame-by-frame three-dimensional coordinates of an object (120) from a plurality of images constituting an image set, and generate frame-by-frame position information (125) including the determined three-dimensional coordinates and the corresponding time. Here, the three-dimensional coordinates may be composed of X (x-axis), Y (y-axis), and Z (z-axis) values ​​in pixel units.

[0099] For example, the processor (210) may select one or more two-dimensional images corresponding to a specific frame (time) among a plurality of two-dimensional images in an image set, and determine two-dimensional coordinates of an object (120) in each of the selected one or more two-dimensional images.

[0100] For example, the two-dimensional coordinates of the object (120) may be one or more two-dimensional coordinates forming an outline of the object (120) within a two-dimensional image, or may be a center point of one or more such two-dimensional coordinates.

[0101] For example, the two-dimensional coordinates of the object (120) may be one of one or more two-dimensional coordinates indicating a specific part of the object (120) or a center point obtained by averaging one or more two-dimensional coordinates. As a specific example, if the sport of the target training session is a ball game, the specific part of the object (120) may be a part where the ball and the object (120) come into direct or indirect contact. That is, if the sport of the target training session is table tennis, the specific part of the object (120) may be a hand holding a racket. Alternatively, if the sport of the target training session is a non-ball game, the specific part of the object (120) may be a part that the object (120) mainly uses during the target training session. For example, if the sport of the target training session is boxing, the specific part of the object (120) may be a hand that the object (120) mainly uses during the target training session. Each object (120) may be modified depending on the sport or training event of the target training session. For example, if the training event is a serve, the object (120) may include a net. If the training event is a toss, the object (120) may not include a net. Information regarding the type of object (120) corresponding to each training event may be included in the characteristic information of the training event.

[0102] For example, the processor (210) can determine the three-dimensional coordinates of the object (120) corresponding to a specific frame (time) based on the two-dimensional coordinates of the object (120) in each of one or more images.

[0103] Meanwhile, the processor (210) can determine the three-dimensional coordinates of the object (120) from the image set using various computer vision algorithms. However, the present disclosure is not limited thereto.

[0104] In one embodiment, the processor (210) may analyze the properties of an object (120) included in one or more two-dimensional images corresponding to a specific frame (time) among a plurality of two-dimensional images in an image set. The processor (210) may generate the result of analyzing the properties of the object (120) as object information (127). For example, the object information (127) may include information about the type, size, state, etc. of the object (120). For example, the object information (127) may include information about the type corresponding to the object (120), what kind of player the object (120) is, if the object (120) is a player, etc. For example, the processor (210) may analyze the face of a player. The processor (210) may extract one or more features of the face of a player included in one or more two-dimensional images using facial expression recognition technology (e.g., MPEG-4 face model, Active Shape Model, Bayesian Shape Model), and identify who the player is based on the extracted features to generate object information (127).

[0105] FIG. 6 is a diagram illustrating a process for generating motion information (126) of an object according to one embodiment of the present disclosure. In one embodiment, the processor (210) may generate motion information (126) regarding the movement of an object (120) based on frame-by-frame position data (125) of the object (120) within a training area (110) of a target training session.

[0106] In one embodiment, the motion information (126) of the object (120) may include indicators indicating the movement of the object (120), such as the direction of movement, the speed of movement, the time of movement (e.g., t seconds), and the movement trajectory of the object (120). Here, each indicator included in the motion information (126) may be numerical data or categorical data.

[0107] For example, the motion information (126) of the object (120) may include at least one of a movement direction, a movement speed, a movement time, or a movement trajectory regarding the object (120) moving from a first location (X, Y, Z) to a second location (X', Y', Z').

[0108] In one embodiment, the motion information (126) of the object (120) may include indicators indicating the movement of a specific portion of the object (120), such as the movement direction, movement speed, movement time (e.g., t seconds), and movement trajectory of a specific portion of the object (120). Here, each indicator included in the motion information (126) may be numerical data or categorical data.

[0109] For example, the motion information (126) of the object (120) may include at least one of a movement direction, a movement speed, a movement time, or a movement trajectory regarding the movement of a specific part of the object (120) from a first position (X, Y, Z) to a second position (X', Y', Z'). Meanwhile, the processor (210) may obtain the motion information (126) of the object (120) of the target training session according to the type of the target sport identified based on the training information (160). For example, if the target sport is a ball game, the processor (210) may generate frame-by-frame position data (125) of the player (120) and frame-by-frame position data (125) of the ball (120) of the target training session, respectively, and may generate motion information (126) of the player and motion information (126) of the ball, respectively, based on the frame-by-frame position data (125) of the player (120) and frame-by-frame position data (125) of the ball (120). Alternatively, if the target sport is a non-ball game, the processor (210) may generate frame-by-frame position data (125) of the player of the target training session, and generate motion information (126) of the player based on the frame-by-frame position data (125) of the player.

[0110] FIG. 7 is a diagram illustrating a process for detecting a training event (10) according to one embodiment of the present disclosure. In one embodiment, one or more cameras included in a first camera group (130) capture training images (150) for a target training set performed within a training area (110), and a processor (210) can detect one or more objects (120a, 120b, 120c) based on the training images (150). The processor (210) can identify each object as a player (120a), a ball (120b), and a net (120c) based on object information (127) corresponding to each attribute of one or more objects (120a, 120b, 120c), and can generate motion information (126) corresponding to each object (120a, 120b, 120c). The processor (210) can determine that a training event (10), such as a sub-event, has occurred based on the motion information (126), and can generate an analysis result (20) accordingly. For example, the processor (210) can generate information on the player's hitting point location (A1), the ball's hitting point height (A2), the ball's net passage height (A3), the ball's trajectory and speed (A4), and the ball's landing point (A5) as analysis results (20) for the target training session.

[0111] In one embodiment, the device (101) can generate a replay image (30) based on a training image (150) acquired from a second camera group (140). In one embodiment, the processor (210) can adjust the angle of view and magnification of the second camera group (140) according to the occurrence location of the training event (10). In one embodiment, the processor (210) can extract a portion of the image area of ​​the training image (150) corresponding to the occurrence location of the training event (10) to generate the replay image (30). For example, the processor (210) can extract a portion of the image area centered on the position of the player (120a) corresponding to the occurrence time of a hitting event, and can extract a portion of the image area corresponding to the net (120c) and the ball (120b) corresponding to a net-over-the-ball event to generate the replay image (30). For example, the processor (210) may change the shooting settings of the second camera group (140) based on the occurrence area of ​​the training event (10) so that the angle of view to be shot in the next training session is set to be centered on the player (120a), or may determine the shooting settings for the shooting magnification of the second camera group (140) so that the player (120a), the net (120c), and the landing point (A5) are shot as a whole.

[0112] FIGS. 8 to 11 are diagrams for explaining analysis results (20) for sports training according to one embodiment of the present disclosure. In one embodiment, the processor (210) may transmit the analysis results (20) to the user terminal (102). In one embodiment, the analysis results (20) may include information (201) on the number of times the target training session was performed, information (202) on the training volume by type of training (movement) for the number of times the target training session was performed, and information (203) on the training volume by type of training session for each athlete and each training session.

[0113] In one embodiment, the analysis results (20) may include numerical information (204, 205, 206) analyzing the training results for each training event. For example, the analysis results (20) may include statistical information (204) for each player for each sub-event. For example, in the case of sub-training events, the statistical information (204) for each player for each event may include statistical information on the net height and serve speed generated during each player's sub-training.

[0114] In one embodiment, the analysis results (20) may include game-specific statistical information (205). For example, the analysis results (20) may include game-specific win contribution information (205) for each player.

[0115] In one embodiment, the analysis results (20) may include detailed analysis information (206) for each player. For example, the detailed analysis information (206) may include information on the speed of the ball for each training event for each player, points for each game (number of games played, number of sets played, number of points scored), a heat map for each player's attack position, a heat map for each player's serve position, statistical information for each game, etc.

[0116] FIGS. 12 and 13 are diagrams for explaining a replay video (30) according to one embodiment of the present disclosure. In one embodiment, the processor (210) may transmit the replay video to the user terminal (102). The user terminal (102) may play the replay video (301, 302) in response to a user input. In one embodiment, the processor (210) may transmit tag information for each replay video (301, 302) to the user terminal (102) together with the replay video. The tag information may include player information, training session information, event information, time information, and summary information corresponding to each replay video (301, 302). The summary information may be summary information for analysis information corresponding to each replay video (301, 302). For example, the summary information for the replay video (301, 302) of a sub-event may include ball speed information and net height information.

[0117] In one embodiment, the replay video (30) may include analysis information (303, 304, 305) and a training video (306). The processor (210) may place the analysis information (303, 304, 305) in a portion of the training video (306) to generate the replay video (30). For example, the replay video (30) for sub-training may include information on the ball's trajectory (303), speed and net passage height information (304), launch angle, direction angle, hitting point height, and number of successes (305).

[0118] FIG. 14 is a diagram for explaining a process in which a device (101) according to one embodiment of the present disclosure controls an output device (180). In one embodiment, the processor (210) of the device (101) can control the output device (180) to display visual information (41, 42, 43) in at least a portion of a training area (110). In one embodiment, the output device (180) can include a light output device (e.g., a projector) that can display visual information (41, 42, 43) such as images in the training area (110) by irradiating light into the training area (110).

[0119] For example, the output device (180) can display first visual information (41) indicating the area where the player is to be positioned in the sub-training, second visual information (42) indicating the trajectory the ball should draw, and third visual information (43) indicating the landing point of the ball, under the control of the processor (210).

[0120] In one embodiment, the shape and display location of each visual piece of information may be changed depending on the training item, training session, and training athlete.

[0121] In one embodiment, the processor (210) may generate an analysis result (20) based on the location of each visual information (41, 42, 43) in the training image (150) and the motion information of the object (120) within the training image (150). For example, the analysis result (20) may be generated by comparing the second visual information (42) and the third visual information (43) with the path along which the ball moved and the actual landing point of the ball within the actual training image (150).

[0122] In one embodiment, each visual information (41, 42, 43) may include information indicating the result of a training event (10) that occurred in the target training session. For example, the first visual information (41) may be information indicating the area where the player was positioned in the target training session, the second visual information (42) may be information indicating the movement path of the ball hit by the player, and the third visual information (43) may be information indicating the location where the ball landed.

[0123] FIG. 15 is a diagram illustrating a learning model (1500) trained to determine whether an event occurs in a target training session according to one embodiment of the present disclosure and to generate analysis information and a replay image. In one embodiment, the learning model (1500) may be a model trained to determine whether an event corresponding to a target training session of sports training occurs by using learning data (910) for other sports training during the learning process, and to generate output data (1530) regarding whether an event occurs in the target training session from input data (1520) for the target training session during the inference process.

[0124] In one embodiment, the learning model (1500) may be a model that is trained to generate analysis information based on a determination that an event corresponding to a sports training has occurred using learning data (1510) for other sports training during the learning process, and generates output data (1530) for the analysis results of a target training session from input data (1520) for the target training session during the inference process.

[0125] In one embodiment, the learning model (1500) may be a model that is trained to generate a replay image based on a determination that an event corresponding to a sports training event has occurred using learning data (1510) for other sports training during the learning process, and generates output data (1530) for a replay image of a target training session from input data (1520) for the target training session during the inference process.

[0126] For example, the processor (210) may input at least one of the training image (150) or training information (160) of the target training round as input data (1510) to the learning model (1500), and obtain information on whether an event occurred in the target training round as output data (1530) of the learning model (1500).

[0127] For example, the processor (210) can input information about an event that occurred in a target training session as input data (1510) to the learning model (1500), and obtain analysis information according to the event that occurred in the target training session as output data (1530) of the learning model (1500).

[0128] For example, the processor (210) can input training images (150) of a target training session and information about an event that occurred as input data (1510) into a learning model (1500), and obtain analysis information according to an event that occurred in the target training session as output data (1530) of the learning model (1500).

[0129] For example, the processor (210) may input training images (150) of a target training session and information about an event that occurred as input data (1510) into a learning model (1500), and obtain a replay image according to an event that occurred in the target training session as output data (1530) of the learning model (1500).

[0130] In one embodiment, the learning model (1500) may be a model trained using image information, information about training events, analysis information, and replay videos from one or more different sports training sessions as training data (1510). Here, the one or more different sports training sessions may be sports training sessions for different sports.

[0131] FIG. 16 is a flowchart of a method for providing an automated training session according to one embodiment of the present disclosure. In step S1610, the device (101) may acquire training images (150) for a target training session. The device (101) may acquire training images (150) for the target training session from a camera group (130, 140).

[0132] In step S1620, the device (101) may determine whether a predetermined training event (10) has occurred in the target training session based on the training image (150) and the training information (160) for the target training session. In one embodiment, the device (101) may determine whether a predetermined training event (10) has occurred in the target training session based on the training image (150) and the training information (160) for the target training session.

[0133] In step S1630, the device (101) may generate an analysis result (20) for the target training session based on the training image (150). In one embodiment, the device (101) may generate an analysis result (20) for the target training session based on the training image (150) upon determining that a training event (10) has occurred.

[0134] In step S1640, the device (101) may generate a replay image (30) for the target training session based on the training image (150). In one embodiment, the device (101) may generate the replay image (30) based on the training image (150) and the analysis result (20) upon determining that a training event (10) has occurred in the target training session.

[0135] In step S1650, the device (101) can transmit the replay image (30) to the output device (180).

[0136] In the flowcharts according to the present disclosure, each step of the method or algorithm is described in a sequential order. However, the steps may be performed in any order that can be arbitrarily combined, in addition to being performed sequentially. The description of the flowcharts or flowcharts of the present disclosure does not exclude changes or modifications to the method or algorithm, and does not imply that any step is essential or desirable. In one embodiment, at least some of the steps may be performed in parallel, iteratively, or heuristically. In another embodiment, at least some of the steps may be omitted, or other steps may be added.

[0137] Various embodiments according to the present disclosure may be implemented as software on a machine-readable storage medium. The software may be software for implementing various embodiments described in the present disclosure. The software may be inferred from various embodiments described in the present disclosure by programmers skilled in the art to which the present disclosure pertains. For example, the software may be a program including machine-readable commands (e.g., instructions, codes, or code segments). The device may be a device capable of operating according to commands called from a storage medium, such as a computer. In one embodiment, the device may be a computing device according to various embodiments described in the present disclosure. In one embodiment, the processor of the device may execute the called command, causing components of the device to perform functions corresponding to the command. The storage medium may refer to any type of recording medium that stores data and can be read by the device. The storage medium may include, for example, a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. In one embodiment, the storage medium may be implemented in a distributed form, such as in a network-connected computer system. In this case, the software may be distributed, stored, and executed in the computer system. In another embodiment, the storage medium may be a non-transitory storage medium. A non-transitory storage medium refers to a tangible medium that exists regardless of whether data is stored semi-permanently or temporarily, and does not include signals that are transmitted transitively.

[0138] While the technical concepts of the present disclosure have been described through various embodiments, it should be understood that the technical concepts of the present disclosure encompass various substitutions, modifications, and variations that can be made within the scope of those skilled in the art to which the present disclosure pertains. Furthermore, it should be understood that such substitutions, modifications, and variations are encompassed within the scope of the appended claims.

Claims

1. Communication circuit; one or more processors; and comprising one or more memories storing instructions executed by the one or more processors; Upon execution of one or more of the above instructions, the one or more processors, Through the above communication circuit, training images taken of target training repetitions are received from one or more cameras, Based on the above training video, determine whether a predetermined training event occurred in the target training session, Upon determining that the above training event has occurred, Based on the above training video, a replay video including analysis results for the target training session is generated, A device configured to transmit the above replay image to an output device.

2. In paragraph 1, The one or more processors, in determining whether the training event has occurred, Based on the training image, object information for each attribute of one or more objects in the training area of ​​the target training session and motion information for each movement of the one or more objects are generated, A device further configured to determine whether the training event has occurred based on the object information and the motion information.

3. In paragraph 2, One or more of the above objects, A device comprising at least one of a player, a ball, the training area, or a training device placed in the training area corresponding to the target training session.

4. In paragraph 2, The one or more processors, in determining whether the training event has occurred, Obtain training item information indicating the training items of the above target training session, Based on the above training item information, characteristic information of the training event corresponding to the training item is obtained, A device further configured to determine whether the training event has occurred based on whether the object information and the motion information correspond to the feature information.

5. In paragraph 4, The one or more processors, in obtaining the training item information, A device further configured to receive training item information from a user terminal through the above communication circuit.

6. In paragraph 4, The one or more processors, in obtaining the training item information, Based on the object information and the motion information, identify a player corresponding to the target training session, A device further configured to obtain training item information based on the identified player.

7. In paragraph 1, The results of the above analysis are as follows: A device comprising at least one of a player corresponding to the target training session, one or more numerical values ​​related to the training event that occurred in the target training session, success or failure of the training event, or progress information for a training set including the target training session.

8. In paragraph 1, One or more of the above processors, A device further configured to store information indicating the above analysis results in association with player information for a player corresponding to the target training session.

9. In paragraph 1, One or more of the above processors, A device further configured to transmit the analysis result to a user terminal through the above communication circuit.

10. In paragraph 1, One or more of the above processors, A device further configured to transmit information about a training session after the target training session to the user terminal via the communication circuit upon determining that the training event has occurred.

11. In paragraph 10, One or more of the above processors, Upon determining that the target training set including the above target training session is completed, based on the athlete information corresponding to the above target training session and the analysis results, the training items for the training set after the above target training set are determined. A device further configured to transmit information about the training item determined through the communication circuit to the user terminal.

12. In paragraph 1, wherein said one or more cameras include one or more high-speed cameras, A device wherein the training video includes a high-speed camera video taken by one or more high-speed cameras during the target training session.

13. In paragraph 1, One or more of the above processors, In generating the above replay video, Upon determining that the above training event has occurred, detect the event occurrence time corresponding to the training event and the event occurrence area among the image areas included in the training image, A device configured to generate the replay image based on the event occurrence time, the event occurrence area, and the training image.

14. In paragraph 13, One or more of the above processors, In generating the above replay video, A device configured to generate the replay image based on training images captured between a time point before a first time interval specified from the time point of occurrence of the event and a time point after a second time interval specified from the time point of occurrence of the event.

15. In paragraph 13, One or more of the above processors, Based on the above event occurrence area, determine the shooting settings for the one or more cameras, A device further configured to transmit information about the determined shooting settings to the one or more cameras via the communication circuit.

16. In paragraph 1, One or more of the above processors, A device further configured to control the output device to display visual information corresponding to the training event on at least a portion of a training area of ​​the target training session through the communication circuit.

17. In paragraph 16, A device wherein the output device includes a light output device that irradiates light to at least a portion of the training area.

18. In paragraph 16, One or more of the above processors, A device further configured to generate the analysis result based on the location of the area where the training image and the visual information are displayed.

19. A method performed in a device including one or more processors and one or more memories storing instructions to be executed by the one or more processors, One or more of the above processors, A step of receiving training images taken of a target training repetition from one or more cameras through a communication circuit; A step of determining whether a predetermined training event occurred in the target training session based on the training image; Upon determining that the above training event has occurred, A step of generating a replay image including analysis results for the target training session based on the training image; and A method comprising the step of transmitting the replay image to an output device.

20. In a non-transitory computer-readable recording medium recording instructions to be executed by one or more processors, The above instructions, when executing the above instructions, cause the one or more processors to: Receive training images taken of target training repetitions from one or more cameras through a communication circuit, Based on the above training video, determine whether a predetermined training event occurred in the target training session, Upon determining that the above training event has occurred, Based on the above training video, a replay video including analysis results for the target training session is generated, A non-transitory computer-readable recording medium that transmits the above replay image to an output device.

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