Tactical analysis device, control method thereof, and control program

Abstract

To achieve a tactical analyzer that can predict a player's next tactics during a game.SOLUTION: A tactical analyzer (10) for analyzing a player's tactics in a game includes a tactics acquisition unit (22) that acquires the player's tactics history in games from images of the games, and a strategy estimation unit (23) that estimates the player's game strategy using a learning model including a machine-learned strategy, which is a chronological pattern of tactics, from the player's tactics history in the past games, and the tactics history acquired by the tactics acquisition unit (22).SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a tactics analysis device that analyzes the tactics of a player in a competition, and a control method and control program for the same. [Background technology]

[0002] In recent years, computer-based analysis has become popular in the field of competitive sports, etc. For example, Patent Document 1 discloses an attack pattern extraction device that analyzes attack patterns based on the position information and play history of players in team sports. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2017-209237 [Patent Document 2] Japanese Patent Publication No. 2020-185370 Summary of the Invention [Problem to be solved by the invention]

[0004] However, Patent Document 1 only identifies past tactics (offensive patterns) and does not predict future tactics from the tactical history of players during the game. Also, in baseball broadcasts, commentators sometimes predict future pitching based on the pitching history during the game, but this is based on the commentators' many years of experience and is not done automatically using a computer.

[0005] One aspect of the present invention aims to realize a tactical analysis device that can predict a player's future tactics during a game. [Means for solving the problem]

[0006] In order to solve the above problems, a tactical analysis device according to one embodiment of the present invention is a tactical analysis device that analyzes the tactics of a player in a competition, and includes a tactical acquisition unit that acquires the tactical history of the player in the competition from video of the competition, a learning model of the player in which a strategy, which is a time-series pattern of tactics, is machine-learned using the tactical history of the player in past competitions, and a strategy estimation unit that estimates the player's strategy in the competition using the tactical history acquired by the tactical acquisition unit.

[0007] Here, "tactics" refers to the characteristic behavior of the player in a certain unit of behavior in the game. For example, in baseball, the unit of behavior may be a single pitch, and the characteristic behavior may be at least one of the course, type, and speed of the pitch. In table tennis, the unit of behavior may be a single rally, and the characteristic behavior may be at least one of the rally trajectory, serve style, position and rotation of the served ball, and position and posture of the player.

[0008] According to the above configuration, a tactical history of a player in a game is acquired from a video of the game, and the acquired tactical history and a learning model of the player are used to estimate the strategy of the player in the game. Therefore, a user can refer to the strategy estimated from the video of the game, i.e., the time series pattern of the tactic, and thereby predict the future tactic of the player during the game. Note that the user may be the player's opponent or the opponent's coach.

[0009] In the tactic analysis device according to this aspect, the tactic acquisition unit may acquire the tactics of the player by extracting time-series data of representative points related to tactics from the video and performing singular value decomposition on the extracted time-series data. In this case, the tactics of the player can be acquired even if the time-series data of the representative points have different durations.

[0010] In the tactical analysis device according to this aspect, the tactical acquisition unit may extract a trajectory of a moving object from the video of the competition, acquire the tactical history of the player based on the extracted trajectory of the moving object, and repeat this process to acquire the tactical history of the player.

[0011] Here, the moving object is an object that moves in the game, such as a player in the game, a ball used in the game, etc. Furthermore, as a preprocessing step, the tactic acquisition unit may remove scenes that are unrelated to the acquisition of the tactics (for example, close-up footage of the player, etc.) from the video of the game as unnecessary scenes.

[0012] In the tactic analysis device according to this aspect, the moving object may include the player, and the tactic acquisition unit may perform machine learning using a video of the game to estimate a skeletal trajectory of the player and acquire at least a part of the tactic of the player based on the estimated skeletal trajectory. In this case, the behavior of the player can be estimated with high accuracy, and therefore the tactic of the player can be estimated with high accuracy.

[0013] In the tactical analysis device according to this aspect, the learning model may be created by performing machine learning on a combination of multiple models in an integrated manner, which can improve the accuracy of estimating the strategy compared to other learning models.

[0014] In the tactical analysis device according to this aspect, the plurality of models may be fuzzy clustering models. In this case, the estimated strategy is provided as a fuzzy rule (IF THEN rule). Therefore, the user can easily predict future tactics.

[0015] In the tactical analysis device according to this aspect, the learning model may be created by generating virtual data based on estimated data estimated by one of the plurality of models through machine learning, and then using the virtual data and learning data to create another model of the plurality of models through machine learning. In this case, the amount of data available for machine learning can be increased, thereby further improving the accuracy of the strategy estimation.

[0016] A control method for a tactical analysis device according to another aspect of the present invention is a control method for a tactical analysis device that analyzes the tactics of a player in a competition, the control method including: a tactical acquisition step of acquiring a tactical history of the player in the competition from video of the competition; and a strategy estimation step of estimating the strategy of the player in the competition using a learning model in which a strategy, which is a time-series pattern of tactics, is machine-learned using the tactical history of the player in past competitions, and the tactical history acquired in the tactical acquisition step.

[0017] According to the above control method, it is possible to achieve the same effects as the above-mentioned tactical analysis device.

[0018] The tactical analysis device according to each aspect of the present invention may be realized by a computer. In this case, the control program of the tactical analysis device that realizes the tactical analysis device by causing the computer to operate as each part (software element) of the tactical analysis device, and the computer-readable recording medium on which it is recorded, also fall within the scope of the present invention. [Effects of the Invention]

[0019] According to one aspect of the present invention, it is possible to predict a player's future tactics during a game. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a block diagram showing a schematic configuration of a tactical analysis device according to an embodiment of the present invention; [Figure 2]10 is a flowchart showing a process flow in a learning mode in the tactic analysis device. [Figure 3] 10 is a flowchart showing a processing flow in a competition mode in the tactic analysis device. [Figure 4] FIG. 10 is a diagram showing a black-and-white image converted from a color image for broadcast superimposed thereon. [Figure 5] 10 is a diagram showing an example of an image including tactics information acquired by a tactics acquisition unit in the tactics analysis device. FIG. [Figure 6] 3 is a block diagram showing a schematic configuration of a strategy estimation unit and a tactics history storage unit in the tactics analysis device. FIG. [Figure 7] 10 is a flowchart showing a flow of a learning process in the strategy estimation unit. [Figure 8] 10 is a diagram showing, in a table format, an example of a tactics history acquired by the tactics acquisition unit. FIG. [Figure 9] 10 is a flowchart showing the flow of processing for extracting a ball from a video in a tactical analysis device according to another embodiment of the present invention. [Figure 10] FIG. 10 is a diagram showing an example of a frame image processed by the white blog extraction process in the above processing. [Figure 11] FIG. 10 is a diagram showing an example of a frame image processed by the inter-frame difference detection step in the above processing. DETAILED DESCRIPTION OF THE INVENTION

[0021] Hereinafter, embodiments of the present invention will be described in detail. For the sake of convenience, the same reference numerals will be used to designate components having the same functions as those in the embodiments, and the description thereof will be omitted where appropriate.

[0022] [Embodiment 1] An embodiment of the present invention will be described with reference to FIGS.

[0023] (Tactical Analysis Device Overview) FIG. 1 is a block diagram showing a schematic configuration of a tactical analysis device according to this embodiment. The tactical analysis device 10 of this embodiment acquires and analyzes the tactics of an opponent (player) from video, and estimates the opponent's strategy. Note that the following description is given assuming that the sport is table tennis, but the present invention is not limited to this. Furthermore, details of the rules of table tennis are well known, and therefore will not be described here.

[0024] As shown in FIG. 1, the tactical analysis device 10 has a control unit 11, a memory unit 12, and a communication unit 13. The control unit 11 comprehensively controls the operation of various components of the tactical analysis device 10 and is configured, for example, by a computer including a CPU (Central Processing Unit) and memory. The operation of the various components is controlled by having the computer execute a control program. The memory unit 12 records information and is configured by a storage device such as a hard disk or flash memory. The communication unit 13 is configured by a communication device that sends and receives information via a communication network such as the Internet.

[0025] The control unit 11 has a tactic acquisition unit 22 and a strategy estimation unit 23. The storage unit 12 also includes a tactic history storage unit 31 that stores tactic history data. In this embodiment, the storage unit 12 includes a learning mode TM that performs machine learning on the learning model of the strategy estimation unit 23, and a competition mode PM that uses the machine-learned learning model during a competition.

[0026] The tactics acquisition unit 22 acquires data on the opponent's tactics history (chronological data on tactics) from video data acquired via the communication unit 13. The tactics acquisition unit 22 stores the acquired tactics history data in the tactics history storage unit 31.

[0027] Specifically, the tactical acquisition unit 22 processes the video to remove unnecessary scenes, and from the processed video, identifies the position and rotation of the ball (moving object), the position and posture of the player (moving object), etc., and uses these as tactical information. Next, the tactical acquisition unit 22 acquires this tactical information for each rally, and uses multiple pieces of tactical information from multiple rallies as a tactical history. Then, the tactical acquisition unit 22 stores the acquired data on the opponent's tactical history in the tactical history storage unit 31.

[0028] The video may be a broadcast video. In this case, there is no need to use a special camera system. The video may be a video taken by a single camera, or a plurality of videos taken by multiple cameras.

[0029] The strategy estimation unit 23 estimates the opponent's strategy using tactical history data stored in the tactical history storage unit 31. Specifically, the strategy estimation unit 23 has a learning model, and in the learning mode TM, machine learning is performed on the learning model using the opponent's past tactical history. On the other hand, in the competition mode PM, the strategy estimation unit 23 estimates the opponent's strategy using the opponent's tactical history during the competition and the learning model that has been machine-learned. The strategy estimation unit 23 outputs data on the estimated strategy to an external device such as a user's terminal via the communication unit 13.

[0030] (Learning mode processing) 2 is a flowchart showing the flow of processing in the learning mode TM in the tactic analysis device 10 configured as described above. The processing shown in FIG. 2 is executed for each player.

[0031] 2, first, the tactic acquisition unit 22 collects video of a certain player's past games (S11). Next, the tactic acquisition unit 22 collects the player's past tactic history from the collected video and stores it in the tactic history storage unit 31 (S12).

[0032] Next, the strategy estimation unit 23 performs machine learning on the learning model using the past tactical histories stored in the tactical history storage unit 31 (S13). Note that the strategy estimation unit 23 may estimate the player's strategy using a portion of the past tactical histories and the machine-learned learning model, and output data on the estimated strategy to an external device via the communication unit 13. Thereafter, the processing in the learning mode TM ends.

[0033] 3 is a flowchart showing the flow of processing in the competition mode PM in the tactic analysis device 10 configured as described above. The processing shown in FIG. 3 is executed during a competition.

[0034] 3, first, the tactics acquisition unit 22 acquires video of the opponent (player) during the competition (S21). Next, the tactics acquisition unit 22 acquires the tactics history of the opponent during the competition from the acquired video, and stores it in the tactics history storage unit 31 (S22, tactics acquisition step).

[0035] Next, the strategy estimation unit 23 estimates the opponent's strategy in the competition using the tactical history during the competition stored in the tactical history storage unit 31 and the opponent's learning model obtained by machine learning in step S13 of Fig. 2 (S23, strategy estimation step). Then, the strategy estimation unit 23 outputs data on the estimated strategy to the opponent's or his / her coach's terminal (external device) via the communication unit 13 (S24). Thereafter, the processing in the competition mode PM ends.

[0036] Therefore, the tactical analysis device 10 of this embodiment acquires the tactical history of the opponent in the game from video of the game, and estimates the opponent's strategy during the game using the acquired tactical history and the opponent's learning model that has been machine-learned. By referring to the estimated strategy, i.e., the time-series pattern of the tactics, the opponent or their coach can predict the opponent's future tactics.

[0037] (Additional notes) The strategy estimation unit 23 may have the learning model previously trained by machine learning. In this case, the learning mode can be omitted.

[0038] (Details of the Tactical Acquisition Department) Next, the tactics acquisition unit 22 and the strategy estimation unit 23 will be described in detail.

[0039] The tactical acquisition unit 22 first removes unnecessary scenes such as commercials, close-up images of players, and slow-motion playback images from the 30 fps broadcast video, and then divides the video into images for each rally. Next, the tactical acquisition unit 22 converts the color video into black and white video.

[0040] FIG. 4 is a diagram showing the black-and-white video superimposed on the image. As can be seen from FIG. 4, the tactic acquisition unit 22 can identify the positions of moving objects, i.e., the ball and the player, from the black-and-white video and acquire the trajectories of the ball and the player. The tactic acquisition unit 22 can also acquire the player's posture from the black-and-white video. The tactic acquisition unit 22 can also acquire the coordinates of the four corners of the table tennis table from the black-and-white video. The tactic acquisition unit 22 can also identify the game number, rally number, and the score immediately before the current rally from the upper left corner of the black-and-white video.

[0041] Next, the tactic acquisition unit 22 performs an affine transformation using the coordinates of the four corners of the table to convert the trajectories of the ball and the player from coordinates on the image to coordinates on a plane when the table is viewed from above. Next, the tactic acquisition unit 22 identifies the position where the ball landed on the table (landing point) from the trajectory of the ball. A rally trajectory is acquired by connecting the identified positions in chronological order with straight lines.

[0042] The tactic acquisition unit 22 also identifies the serve style and ball spin from the trajectories of the ball and the player and the player's posture. Examples of serve styles include forehand, backhand, hook, etc. Examples of ball spin in the serve include right side spin, left side spin, topspin, underspin, a combination of these, and knuckle (no spin). The hook refers to a technique of throwing the ball up, standing the racket against the falling ball, and rubbing the ball with either face of the racket to serve.

[0043] The tactical acquisition unit 22 then acquires the rally trajectory, the serve style, and the ball spin of the serve as tactical information for each rally, and stores the tactical information for multiple rallies as a tactical history. In the learning mode TM, the tactical acquisition unit 22 stores multiple tactical history data from multiple past matches in the tactical history storage unit 31 as learning data, which will be described later. On the other hand, in the competition mode PM, the tactical history data during the competition (e.g., the tactical history of one game) is stored in the tactical history storage unit 31 as evaluation data, which will be described later. In the embodiment, these processes were realized using Anaconda Python 3.5.2, OpenCV 3.1.0, easygui, and tqdm programs.

[0044] FIG. 5 is a diagram showing an example of an image including the tactical information. As shown in FIG. 5, the left part of the image includes a frame showing the table and a rally trajectory, and the center right part of the image includes a legend for the rally trajectory. In the image, the serve rally trajectory is shown with a solid line, the final rally trajectory is shown with a two-dot chain line, and the rally trajectory between the serve rally trajectory and the final rally trajectory is shown with a dashed dot chain line. The ball spin for the serve is included below the rally trajectory. In the example of FIG. 5, the ball spin for the serve is a clockwise side back spin.

[0045] The top right corner of the image contains, in order from top to bottom, the server's name, the receiver's name, the game number, and the rally number. The bottom right corner of the image contains, in order from top to bottom, the score before the rally, the serve style, and the ball rotation of the serve.

[0046] (Details of the strategy estimation part) 6 is a block diagram showing a schematic configuration of the strategy estimation unit 23 and the tactical history storage unit 31. As shown in FIG. 6, the strategy estimation unit 23 includes a learning device group 41, a result acquisition unit 42, a virtual data generation unit 43, and a final decision unit 44. The tactical history storage unit 31 includes a learning data storage unit 51, an evaluation data storage unit 52, and a virtual data storage unit 53.

[0047] The learning data storage unit 51 stores data on past tactical history as learning data TRD. The evaluation data storage unit 52 stores data on tactical history during a game as evaluation data CHD.

[0048] In this embodiment, an ensemble learning type fuzzy clustering model is used as the learning model. Therefore, the learning device group 41 includes n learning devices (n is an integer equal to or greater than 2) that perform machine learning. Each learning device uses a fuzzy clustering model as a learning model. Fuzzy clustering classifies an input data set into classes using if-then type rules and estimates the boundaries of the classes.

[0049] In the learning mode TM, the first learning device 41a performs learning using a set of learning data TRD from the learning data storage unit 51, and outputs a set of estimated classes as a result. The other learning device 41b performs learning using a part of the set of learning data TRD from the learning data storage unit 51 and a set of virtual data VD from the virtual data storage unit 53, and outputs a set of estimated classes as a result.

[0050] In the competition mode PM, all of the learned learning devices 41a and 41b estimate classes from the set of evaluation data CHD from the evaluation data storage unit 52, and output the set of estimated classes as a result.

[0051] The result acquisition unit 42 acquires a set of classes estimated by each of the learning devices 41a and 41b in the learning device group 41. The result acquisition unit 42 sends the set of estimated classes to a virtual data generation unit 43 in the learning mode, and to a final determination unit 44 in the competition mode.

[0052] The virtual data generation unit 43 generates a set of virtual data VD using the set of training data TRD from the training data storage unit 51 and the set of estimated classes from the result acquisition unit 42. The virtual data generation unit 43 stores the generated virtual data in the virtual data storage unit 53.

[0053] Specifically, the virtual data generation unit 43 first compares the actual class included in each piece of training data TRD with the estimated class to identify whether the class is correct or incorrect. Next, the virtual data generation unit 43 generates a set of virtual data VD based on the incorrectly estimated training data TRD (incorrectly estimated data) and at least a portion of the correctly estimated training data TRD (correctly estimated data), and stores the generated virtual data in the virtual data storage unit 53.

[0054] The final determination unit 44 finally determines the most frequent class from the set of classes estimated by the learning devices 41 a and 41 b for each piece of evaluation data CHD as the estimated class. The final determination unit 44 transmits the set of classes estimated for the set of evaluation data CHD to an external device via the communication unit 13.

[0055] ~ (learning process) 7 is a flowchart showing the flow of the learning process in the above-configured strategy estimation unit 23. As shown in Fig. 7, first, a variable i is initialized to 1 (S31), and the i-th learning device (first learning device 41a) performs learning using a set of learning data TRD from the learning data storage unit 51 (S32).

[0056] Next, the virtual data generation unit 43 compares the class estimated by the i-th learner with the actual class for each of the training data TRD to determine whether the class is correct (S33). Next, if the proportion of correctly estimated classes is equal to or greater than a threshold (YES in S34), the learning process ends.

[0057] On the other hand, if the proportion of correctly estimated data is less than the threshold (NO in S34), the virtual data generation unit 43 sets a region in the multidimensional coordinate system (space) that includes the incorrectly estimated data as the (i+1)th low estimation region (S35). Next, the virtual data generation unit 43 generates a set of virtual data VD based on the set of incorrectly estimated data and correctly estimated data located in the (i+1)th low estimation region, and stores the set in the virtual data storage unit 53 (S36).

[0058] Next, the (i+1)th learning device (another learning device 41b) performs learning using a set of mis-estimated data and correctly-estimated data located in the (i+1)th low estimation region and the set of virtual data VD (S37).

[0059] Next, the variable i is incremented by 1 (S38). If the variable i is equal to or less than the predetermined number of times (NO in S39), the process returns to step S12 and the above operation is repeated. Then, if the variable i exceeds the predetermined number of times (YES in S39), the learning process ends.

[0060] (Example) FIG. 8 is a diagram showing, in table form, an example of the tactical history acquired by the tactical acquisition unit 22. The tactical history shown in FIG. 8 includes tactical information for the first to fourth games when the opponent was the server and points were acquired. That is, the portions in FIG. 8 where no tactical information is listed correspond to the cases when the opponent was the server and points were acquired. In addition, in the example of FIG. 8, the tactical information includes the serving style (forehand, backhand, etc.), the ball spin, and the attack type.

[0061] The strategy estimation unit 23 performs a learning process using the tactical history shown in FIG. 8 as the learning data TRD, and the following is an example of a set of estimated classes.

[0062] If the game number is Small and the rally number is Small, then the (forehand + right side down) serve is Large. If Game Number is Small and Rally Number is Small, then (N or V) Attack is Large. If the rally number is Small, then (N or V) the attack is Large. If Rally Number is Large, then (A or V) Attack is Large. If Game Number is Large, then Serve Type is Large. Next, it is assumed that the tactic acquisition unit 22 acquires the tactic histories of the first game and the second game in Fig. 8 from the video of the first game and the second game. Then, it is assumed that the strategy estimation unit 23 estimates the set of classes using the tactic histories of the first game and the second game in Fig. 8 as a set of evaluation data CHD.

[0063] In this case, if the opponent or their coach refers to the set of the above class at the end of the second game, they can predict the opponent's strategy from the third game onwards as shown below (a) and (b).

[0064] (a) In the first half of a rally, players tend to use N or V attacks, and in the second half of a rally, players tend to use A or V attacks.

[0065] (b) As the game progresses, the variety of serves increases.

[0066] The above (a) is an intra-game strategy, and the above (b) is an inter-game strategy. In this way, since the intra-game strategy and the inter-game strategy can be predicted, it is possible to accurately predict the opponent's future strategy in this match.

[0067] Therefore, in the learning unit group 41 of the tactical analysis device 10 of this embodiment, multiple learning units 41a and 41b are integrated and combined to perform machine learning, which can improve the accuracy of strategy estimation compared to other learning models. Furthermore, each of the multiple learning units 41a and 41b uses fuzzy clustering as a learning model, so the estimated strategy is provided using fuzzy rules (IF THEN rules). Therefore, opponents or their coaches can easily predict future tactics.

[0068] Furthermore, virtual data VD is generated based on estimated data estimated by the (i-1)th learner (i is an integer between 2 and n) that has undergone machine learning, and the i-th learner 41b performs machine learning using the virtual data VD and the training data TRD. This allows for an increase in the amount of data available for machine learning, thereby further improving the estimation accuracy of the strategy.

[0069] [Embodiment 2] Another embodiment of the present invention will be described with reference to Figures 9 to 11. The tactic analysis device 10 of this embodiment is different from the tactic analysis device 10 shown in Figures 1 to 8 in the operation of the tactic acquisition unit 22 to acquire the trajectories of the ball and players from video, but is otherwise similar.

[0070] 9 is a flowchart showing the flow of processing for extracting a ball from video in the tactic acquisition unit 22 of the tactic analysis device 10 of this embodiment. In this embodiment, the trajectory of the ball is estimated from the broadcast video automatically divided into rallies by a white image detection process (S40) for a white ball and an inter-frame difference detection process (S45).

[0071] As shown in FIG. 9, in the white image detection process (S40), first, a threshold process is performed on each of the RGB color scales to extract an image of a white portion (S41).

[0072] Next, the skeletal positions of the athletes are estimated by learning CenterNet (S42). The input to CenterNet is a video in MP4 format, and the output from CenterNet is 17 two-dimensional skeletal position coordinates and two two-dimensional coordinates that specify the entire skeletal region. Therefore, in the case of two athletes, the number of output variables is (17 × 2 + 2 × 2) × 2 = 76. Step S42 enables accurate estimation of the athletes' behavior, which in turn enables accurate estimation of their tactics.

[0073] Then, the white blog of the white ball is detected by erasing the white blogs other than the white ball, such as the player's racket, taping, bib letters, shoes, etc. (S43).

[0074] 10 is a diagram showing an example of a frame image processed by the white blog extraction step (S40). Referring to FIG. 10, it can be seen that not only the player's skeleton and the white ball, but also several white blog parts such as the white frame of the table, the white part above the net, and the white of the score box have been detected.

[0075] On the other hand, in the inter-frame difference detection process (S45), as shown in Fig. 9, first, the difference in RGB color scale between the image of the current frame and the image of the seven frames before is calculated (S46). Next, the same process as step S42 of the white image detection process (S40) is performed (S47). Next, white blogs other than the player's white ball are deleted from the player's skeleton position coordinates estimated in step S47, thereby detecting white blogs outside the skeleton area (S48).

[0076] 11 shows an example of a frame image processed by the frame difference detection process (S45). Referring to FIG. 11, it can be seen that only the player's skeleton and the white ball are detected, and other white parts, such as the white frame of the table, the white part above the net, and the white score box, have been erased. Therefore, the position of the white ball can be detected by the white image detection process (S40) and the frame difference detection process (S45).

[0077] After the white image detection process (S40) and the inter-frame difference detection process (S45), the ball position in the next frame is estimated (S51). This estimation is performed by extracting the white blog of the white ball within a circular area with a radius of 80 pixels from the ball position in the current frame and limited to the white blog portion (ball candidate group) within an inner area that is 10 pixels smaller than the area of ​​the table. If detection is impossible, the radius is expanded by 20 pixels for each frame and the estimation is performed again. Then, using a Kalman filter, bicube interpolation, etc., the ball position is interpolated when the ball is hidden by a player's body, etc. (occlusion), and the position coordinates of the ball's trajectory are estimated (S52). Subsequent processing is the same as that of the tactical analysis device 10 shown in Figures 1 to 8.

[0078] (Additional notes) The trajectories of the ball and players are represented by time-series data of positions (representative points), but the length of the time-series data varies depending on the number of rallies, the elapsed time, etc. Therefore, the tactic acquisition unit 22 may acquire, as tactic information for each rally, singular values ​​calculated by singular value decomposition of the time-series data. In this case, even if the time length of the time-series data varies, it is possible to acquire the opponent's tactics.

[0079] 3 may be executed after the match. In this case, the strategies of the opponents in the current match can be estimated and analyzed, and the results can be used in the next match against the opponents.

[0080] Furthermore, although the above embodiment is applied to table tennis, the present invention is not limited to this. The above embodiment can be applied to various competitive sports using a ball, such as tennis, baseball, and soccer. In baseball, the pitching content corresponds to the tactics. In soccer, the tactics are not determined on a player-by-player basis, but on a team-by-team basis to which the player belongs. The above embodiment can also be applied to martial arts, such as wrestling and judo. In addition, although the above embodiment estimates the strategy of the opponent (enemy), the strategy of the opponent (ally) may also be estimated.

[0081] [Software implementation example] The functions of the tactical analysis device 10 (hereinafter referred to as the "device") can be realized by a program that causes a computer to function as the device, and a program that causes a computer to function as each control block of the device (particularly each part included in the control unit 11).

[0082] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program, thereby realizing the functions described in each of the above embodiments.

[0083] The program may be non-transitory and may be recorded on one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0084] Furthermore, some or all of the functions of the control blocks can be realized by logic circuits. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of the control blocks can also be realized by, for example, a quantum computer.

[0085] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

[0086] According to the aspects of the present invention, for example, the following becomes possible: (1) By analyzing the opponent's past data from broadcast footage, the opponent's next strategy can be predicted, allowing the opponent to know what kind of attack and defense they should use. (2) The results of the first game can be analyzed immediately. This allows the opponent's coach to give advice to the opponent about what kind of attack to use in the second game, even during the second game. (3) Since the strategies of top players can be referenced, this can also be used to develop players. [Explanation of symbols]

[0087] 10 Tactical Analysis Device 11 Control section 12 Storage section 13 Communications Department 22 Tactical Acquisition Department 23 Strategy Estimation Department 31 Tactical History Memory Unit 41 Learning Units 41a·41b Learning Unit 42 Result acquisition part 43 Virtual Data Generation Unit 44 Final Decision Section 51 Learning data storage unit 52 Evaluation data storage unit 53 Virtual Data Storage Unit

Claims

1. A tactics analysis device that analyzes tactics, which are the action content of a player in a specific action unit in a competition, comprising: a tactic acquisition unit that acquires a tactic history of the player in the competition from a video of the competition; A tactic analysis device comprising: a learning model of the player in which a strategy, which is a time-series trend of a plurality of tactics executed by the player since the tactic acquisition unit acquired the tactic history, is machine-learned using the tactic history of the player in the past competition; and a strategy estimation unit that estimates the player's strategy for the competition using the tactic history acquired by the tactic acquisition unit.

2. The tactic analysis device according to claim 1 , wherein the tactic acquisition unit extracts time-series data of representative points relating to tactics from the video, and acquires the player's tactics by performing singular value decomposition on the extracted time-series data.

3. 3. The tactic analysis device according to claim 1, wherein the tactic acquisition unit extracts a trajectory of a moving object from the video of the game, acquires the tactic of the player based on the extracted trajectory of the moving object, and repeats this process to acquire a tactic history of the player.

4. the moving object includes the player, 4. The tactic analysis device according to claim 3, wherein the tactic acquisition unit estimates a skeletal trajectory of the player by performing machine learning using a video of the game, and acquires at least a part of the tactic of the player based on the estimated skeletal trajectory.

5. The tactical analysis device according to claim 1 , wherein the learning model is created by performing machine learning by integrally combining a plurality of models.

6. The tactical analyzer of claim 5 , wherein the plurality of models are fuzzy clustering models.

7. 7. The tactical analysis device according to claim 5 or 6, wherein the learning model is created by generating virtual data based on estimated data estimated by a certain model of the plurality of models that have been machine-learned, and using the virtual data and learning data to machine-learn another model of the plurality of models.

8. A control method for a tactics analysis device that analyzes tactics, which are the action content of a player in a specific action unit in a game, comprising: a tactic acquisition step of acquiring a tactic history of the player in the competition from a video of the competition; A control method for a tactic analysis device, comprising: a learning model in which a strategy, which is a time-series trend of a plurality of tactics executed by the player after the tactic acquisition step, is machine-learned using the tactic history of the player in past competitions; and a strategy estimation step in which the strategy of the player in the competition is estimated using the tactic history acquired in the tactic acquisition step.

9. 2. A control program for causing a computer to function as the tactical analysis device according to claim 1, the control program causing a computer to function as the tactical acquisition unit and the strategy estimation unit.

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