Chip recognition learning system

Abstract

To provide a chip recognition learning system capable of accurately recognizing chips bet by a player.SOLUTION: A chip recognition learning system 10 includes: a game recording device 11 for recording, as an image, the state of chips stacked on a game table by a camera 212; a chip determination device 12 including an artificial intelligence device 12a for subjecting the recorded image of the state of the chips to image analysis and determining the number and kinds of the chips bet by a player; and a teacher device 13 for inputting, as teacher data, the image used for the determination by the chip determination device 12 and the correct number and kinds of chips for a wrong result to the artificial intelligence device 12a and causing the artificial intelligence device to learn them when it is determined that a result of the determination by the chip determination device 12 is suspected of being wrong.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a chip recognition learning system. [Background technology]

[0002] In games such as baccarat, customers (players) place bets by stacking multiple chips on the table. Therefore, it is necessary to accurately identify the stacked chips. International Publication No. 2008 / 120749 discloses an example of chips used in the game. Summary of the Invention

[0003] An object of the present invention is to provide a chip recognition learning system that can accurately recognize chips bet by players.

[0004] A chip recognition learning system according to one aspect of the present invention comprises: A chip recognition learning system in an amusement facility having a gaming table, comprising: a game recording device that records the state of the chips stacked on the gaming table as an image using a camera; a chip determination device including an artificial intelligence device that analyzes the image of the recorded chip state and determines the number and type of chips bet by the player; The device is also equipped with a teacher device that, when it is determined that the judgment result of the chip judgment device is suspected to be an error, inputs the image used in the judgment of the chip judgment device and the number and type of chips that are correct for the error as teacher data into the artificial intelligence device to allow it to learn.

[0005] According to this aspect, when the chip judgment device judges that there is a suspicion of an error in its judgment result, the teacher device inputs the images used in the judgment by the chip judgment device and the number and type of chips that are correct for the error as teacher data into the artificial intelligence device to cause it to learn, so that the artificial intelligence device can efficiently learn about image patterns for which the judgment accuracy of the chip judgment device is relatively low, and can improve the judgment accuracy of the chip judgment device by focusing on those image patterns. By repeating this teacher learning, the chip judgment device can accurately recognize the chips bet by the player, regardless of how the chips are stacked.

[0006] In one aspect of the present invention, there is provided a chip recognition learning system, When the teacher device determines that the judgment result of the chip judgment device is correct, the image used in the judgment by the chip judgment device and the number and type of chips in the judgment result may be further input into the artificial intelligence device as teacher data to allow it to learn.

[0007] According to this aspect, it is possible to further improve the judgment accuracy not only for image patterns for which the judgment accuracy of the chip judgment device is relatively low, but also for image patterns for which the judgment accuracy is relatively high, thereby enabling the chip judgment device to recognize the chips bet by the player with even greater accuracy.

[0008] A chip recognition learning system according to one aspect of the present invention comprises: A chip recognition learning system in an amusement facility having a gaming table, comprising: a game recording device that records the state of the chips stacked on the gaming table as an image using a camera; The image of the state of the recorded chips is analyzed to determine the number of chips bet by the player and and a chip determination device including an artificial intelligence device for determining the type of chip, When the artificial intelligence device determines that the judgment result of the chip judgment device is suspected to be an error, the image used in the judgment of the chip judgment device and the number or type of chips that are correct for the error are input as training data from a teacher device and the artificial intelligence device learns from this.

[0009] According to this aspect, when the chip judgment device judges that there is a suspicion of an error in the judgment result, the artificial intelligence device learns from the teacher device the images used in the judgment by the chip judgment device and the number and type of chips that are correct for the error as training data, so that the artificial intelligence device can efficiently learn about image patterns for which the judgment accuracy of the chip judgment device is relatively low, and can improve the judgment accuracy of the chip judgment device by focusing on those image patterns. By repeating this process, the chip judgment device can accurately recognize the chips bet by the player, regardless of how the chips are stacked.

[0010] A chip recognition learning system according to one aspect of the present invention comprises: Further, a control device is provided to determine whether the determination result of the chip determination device is correct or not. the chip determination device is capable of determining, from the image recorded in the game recording device, the type and number of chips in a chip tray provided on the gaming table in a game played on the gaming table, as well as the position, type and number of chips bet by each player; When all of the losing chips bet by each player have been collected, the control device may grasp the actual total amount of chips in the chip tray, calculate the total amount of chips that should be in the chip tray by adding the total amount of chips in the chip tray before settlement for each game based on the judgment result of the chip judgment device to the increase in the chip tray amount for that game calculated from the type and number of chips bet by the losing player, compare the total amount of chips that should be in the chip tray with the actual total amount of chips in the chip tray, and if there is a difference between the total amount that should be and the actual total amount, judge that there is a suspicion of an error in the judgment result of the chip judgment device.

[0011] According to this aspect, the control device can automatically determine whether or not there is a suspicion of an error in the determination result of the tip determination device.

[0012] A chip recognition learning system according to one aspect of the present invention comprises: A chip recognition learning system in an amusement facility having a gaming table, comprising: a game recording device that records the state of the chips stacked on the gaming table as an image using a camera; a chip determination device including an artificial intelligence device that analyzes the image of the recorded chip state and determines the number and type of chips bet by the player; and a teacher device that, when the judgment result of the chip judgment device is judged to be correct, inputs the image used in the judgment of the chip judgment device and the number and type of chips in the judgment result into the artificial intelligence device as training data to allow the artificial intelligence device to learn.

[0013] A chip recognition learning system according to one aspect of the present invention comprises: A chip recognition learning system in an amusement facility having a gaming table, comprising: a game recording device that records the state of the chips stacked on the gaming table as an image using a camera; a chip determination device including an artificial intelligence device that performs image analysis of the recorded image of the state of the chips and determines the number and type of chips bet by the player; When the judgment result of the chip judgment device is determined to be correct, the artificial intelligence device learns by inputting the image used in the judgment by the chip judgment device and the number or type of chips in the judgment result as training data from a teacher device.

[0014] A chip recognition learning system according to one aspect of the present invention comprises: Further, a control device is provided to determine whether the determination result of the chip determination device is correct or not. the chip determination device is capable of determining, from the image recorded in the game recording device, the type and number of chips in a chip tray provided on the gaming table in a game played on the gaming table, as well as the position, type and number of chips bet by each player; When all of the losing chips bet by each player have been collected, the control device may grasp the actual total amount of chips in the chip tray, calculate the total amount of chips that should be in the chip tray by adding the total amount of chips in the chip tray before settlement for each game based on the judgment result of the chip judgment device to the increase in the chip tray amount for that game calculated from the type and number of chips bet by the losing player, compare the total amount of chips that should be in the chip tray with the actual total amount of chips in the chip tray, and determine that the judgment result of the chip judgment device is correct if the total amount that should be and the actual total amount match.

[0015] In one aspect of the present invention, there is provided a chip recognition learning system, The control device may grasp the actual total value of the chips in the chip tray based on the RFID tags attached to the chips.

[0016] According to this embodiment, the control device can automatically grasp the actual total amount of chips in the chip tray using RFID, thereby improving the measurement accuracy compared to visual measurement by an operator.

[0017] In one aspect of the present invention, there is provided a chip recognition learning system, The control device may include an artificial intelligence device for determining a correct answer, different from the artificial intelligence device of the chip judgment device, which determines the actual total amount of chips in the chip tray from the image recorded in the game recording device.

[0018] According to this embodiment, the control device can automatically determine the actual total amount of chips in the chip tray using an artificial intelligence device for determining the correct answer, thereby improving measurement accuracy compared to visual measurement by an operator.

[0019] In one aspect of the present invention, there is provided a chip recognition learning system, The game recording device may record images acquired from a camera by adding an index or time stamp, or by adding a tag identifying the chip collection scene or the chip payment scene, so that the game record can be later analyzed by the chip determination device.

[0020] According to this aspect, the chip determination device can easily identify an image of the chip state to be analyzed from the recorded contents of the game recording device by using the index, time, and tag assigned to the image, thereby reducing the time required for identification.

[0021] In one aspect of the present invention, there is provided a chip recognition learning system, The chip determination device may be capable of determining the type, number and position of multiple chips placed on the gaming table even if some or all of the chips are hidden by the blind spot of the camera.

[0022] According to this embodiment, particularly when multiple chips placed on a gaming table are partially or entirely hidden by an operator's blind spot, the chip determination device can be used to determine the chips bet by the player, thereby improving measurement accuracy compared to visual measurement by an operator.

[0023] A chip recognition learning method according to one aspect of the present invention includes: A chip recognition learning method in an amusement facility having a gaming table, comprising: a game recording step of recording the state of the chips stacked on the gaming table as an image by a camera; a chip determination step by an artificial intelligence device that performs image analysis of the recorded image of the state of the chips to determine the number and type of chips bet by the player; and a teaching step in which, if it is determined that there is a suspicion of an error in the judgment result of the chip judgment step, the image used in the judgment of the chip judgment step and the number and type of chips that are correct for the error are input into the artificial intelligence device as teaching data to allow it to learn.

[0024] According to this aspect, if it is determined that the determination result in the chip determination step is suspected to be an error, in the teaching step, the image used in the determination in the chip determination step and the number and type of chips that are correct for the error are input as teaching data to the artificial intelligence device for learning, so that the artificial intelligence device can efficiently learn about image patterns that have relatively low determination accuracy in the chip determination step and can focus on improving the determination accuracy for those image patterns. By repeating this, it becomes possible to accurately recognize the chips bet by the player in the chip determination step, regardless of how the chips are stacked.

[0025] A chip recognition learning method according to one aspect of the present invention includes: A chip recognition learning method in an amusement facility having a gaming table, comprising: a game recording step of recording the state of the chips stacked on the gaming table as an image by a camera; a chip determination step by an artificial intelligence device that performs image analysis of the recorded image of the state of the chips to determine the number and type of chips bet by the player; and a teaching step in which, if the judgment result of the chip judgment step is judged to be correct, the image used in the judgment of the chip judgment step and the number and type of chips in the judgment result are input as teaching data into the artificial intelligence device to allow it to learn. [Brief explanation of the drawings]

[0026] [Figure 1] FIG. 1 is a diagram schematically showing an amusement center equipped with a chip recognition learning system according to the first embodiment. [Figure 2]FIG. 2 is a diagram for explaining the progress of a baccarat game. [Figure 3] FIG. 3 is a block diagram showing a schematic configuration of a chip recognition learning system according to the first embodiment. [Figure 4] FIG. 4 is a flowchart illustrating a chip recognition learning method. [Figure 5] FIG. 5 is a flowchart for explaining a modified example of the chip recognition learning method. [Figure 6] FIG. 6 is a flowchart illustrating another modified example of the chip recognition learning method. [Figure 7] FIG. 7 is a diagram schematically showing an amusement center equipped with a chip recognition learning system according to the second embodiment. [Figure 8] FIG. 8 is a block diagram showing a schematic configuration of a chip recognition learning system according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In each drawing, components having equivalent functions are designated by the same reference numerals, and detailed description of the components having the same reference numerals will not be repeated.

[0028] First, a description will be given of a game played in a gaming parlor having a gaming table 4. In this embodiment, an example will be described in which the gaming table 4 is a baccarat table and a baccarat game is played, but the present invention can also be applied to other gaming parlors or other games.

[0029] FIG. 1 is a diagram schematically illustrating an amusement facility equipped with a chip recognition learning system 10 according to a first embodiment. As shown in FIG. 1, the amusement facility is provided with a substantially semicircular gaming table 4 and a plurality of chairs 201 arranged along the arc side of the gaming table 4 so as to face the dealer D. The number of chairs 201 is arbitrary, and in the example shown in FIG. 1, six chairs 201 are arranged. In addition, a betting area BA is provided on the gaming table 4 corresponding to each chair 201. That is, in the example shown, six betting areas BA are arranged in an arc shape.

[0030] As shown in Fig. 1, customers (players) C are seated at each of the chairs 201. Customers (players) C bet on the outcome of the baccarat game, whether the player or the banker will win, or whether the game will end in a draw (TIE), by placing chips W in a pile on a betting area BA in front of the chairs 201 where they are seated (hereinafter, this will be referred to as a "bet").

[0031] The chips W to be bet may be of one type only, or may be of multiple types. The number of chips W to be bet may be determined arbitrarily by the customer (player) C. The chip recognition learning system 10 according to this embodiment recognizes the number and types of the chips W arranged in a stack.

[0032] To end the betting by customer (player) C, dealer D waits for the right timing to call "No More Bets" and moves his hand sideways. Next, dealer D draws cards one by one from card shooter device S onto gaming table 4. As shown in FIG. 2, the first card becomes the player's hand, the second card becomes the banker's hand, the third card becomes the player's hand, and the fourth card becomes the banker's hand (hereinafter, the drawing of the first to fourth cards will be referred to as "dealing").

[0033] All cards are drawn face down from the card shooter S. Therefore, neither the dealer D nor the customer (player) C can know the rank (number) or suit (heart, diamond, spade, club) of the drawn cards.

[0034] After the fourth card is drawn, the customer (Player) C who bet on PLAYER (if there are multiple customers who bet on PLAYER, the customer C who bet the highest amount; if there are no customers who bet on PLAYER, the dealer D) turns the first and third cards, which were face down, face up. Similarly, the customer (Player) C who bet on BANKER (if there are multiple customers who bet on BANKER, the customer C who bet the highest amount; if there are no customers who bet on BANKER, the dealer D) turns the second and fourth cards face up (this turning face down cards face up is generally called a "squeeze").

[0035] Then, based on the ranks (numbers) of the first to fourth cards and the detailed rules of the baccarat game, dealer D draws the fifth and sixth cards, which become the hand of the player or banker, respectively. Similarly, customer (player) C, who bet on the player, squeezes the cards that will become the player's hand, and the banker squeezes the cards that will become the banker's hand. The customer (player) who bets on (BANKER) squeezes.

[0036] The most exciting part for customer (player) C is the time from when the first four cards are drawn until the fifth and sixth cards are squeezed and the outcome of the game is revealed.

[0037] Furthermore, depending on the rank (number) of the cards, the outcome may be decided by the first four cards, or it may take the fifth or even sixth card to finally decide the winner. Dealer D determines that the winner has been decided and the result of the game based on the rank (number) of the squeezed cards, and presses the win / loss result display button on the card shooter device S to display the result on the monitor to inform customer (player) C.

[0038] At the same time, the outcome of the game is determined by a win / loss determination unit possessed by the card shooter device S. If the dealer D attempts to draw another card without displaying the outcome of the game even though the outcome has been determined, this is an error. The card shooter device S detects the error and outputs an error signal. Finally, while the outcome of the game is being displayed, the dealer D settles the bets made by customers (players) C, paying the winning customers (players) C and collecting the bets made by losing customers (players) C. After the settlement is complete, the display of the outcome of the game is terminated and betting for the next game begins.

[0039] The above-described flow of a baccarat game is widely practiced in ordinary casinos, and the card shooter device S is an existing card shooter device that has a structure in which cards are drawn by the dealer D, is configured to read the drawn cards, and further has a result display button and result display unit, and is equipped with the function of determining whether or not a player has won or lost and displaying the results of the win or loss. On an ordinary casino floor, a card shooter device S and a monitor are placed at each of the multiple gaming tables 4 lined up, and the cards to be used are supplied in packages, sets, or even cartons to each gaming table 4 or to the cabinet below it.

[0040] The chip recognition and learning system 10 of this embodiment relates to a system for recognizing and learning chips W that a customer (player) C has stacked and placed in a betting area BA, and more specifically, to a system for recognizing and learning the number and / or type of chips W.

[0041] 1, in this embodiment, a monitoring camera 212 that captures images of the chips W stacked and arranged in the betting area BA is provided outside the gaming table 4. In addition, each chip W is provided with an RFID, and a chip tray 23 managed by the dealer D is provided with an RFID reader 22 that reads the RFID of the chips W in the chip tray 23.

[0042] The chip recognition learning system 10 according to this embodiment is communicatively connected to a surveillance camera 212 and an RFID reader 22, respectively.

[0043] FIG. 3 is a block diagram showing a schematic configuration of a chip recognition learning system 10 according to this embodiment.

[0044] 3, the chip recognition learning system 10 includes a game recording device 11, a chip determination device 12, a teacher device 13, and a control device 14. At least a part of the chip recognition learning system 10 is realized by a computer.

[0045] The game recording device 11 includes a fixed data storage such as a hard disk. The game recording device 11 records the state of the chips W stacked on the gaming table 4 in a card. The image is recorded as an image captured by the camera 212. The image may be a moving image or a series of still images.

[0046] The game recording device 11 may record images acquired from the camera 212 by adding an index or time stamp, or by adding a tag identifying the scene of collecting or paying the chip W, so that the game record can be later analyzed by the chip determination device 12 described below.

[0047] The chip determination device 12 includes an artificial intelligence device 12a that performs image recognition using, for example, deep learning technology, and performs image analysis on the images of the states of the chips W recorded in the game recording device 11 to determine the number and type of chips W bet by the customer (player) C. The chip determination device 12 may further determine the positions of the chips W bet by the customer (player) C on the betting area BA.

[0048] The chip determination device 12 may analyze the image of the state of the chips W recorded in the game recording device 11 to determine the number and type of chips W in the chip tray 23 before settlement of each game.

[0049] 3, the tip determination device 12 outputs the determination result to the output device 15. The output device 15 may output the determination result of the tip determination device 12 as text information to a monitor on the gaming table 4, or may output it as audio information to a headset of the dealer D, etc.

[0050] The control device 14 is a device that judges whether the judgment result of the chip judgment device 12 is correct or not. The control device 14 grasps the actual total amount V0 of the chips W in the chip tray 23 when all of the chips W (losing chips) bet by the losing customer (player) C have been collected.

[0051] In this embodiment, the control device 14 acquires RFID information of the chips W in the chip tray 23 from the RFID reading device 22, and based on the acquired RFID information, determines the type and number of chips W in the chip tray 23 and grasps the actual total amount V0.

[0052] The control device 14 also acquires the judgment result from the chip judgment device 12, and based on the acquired judgment result, calculates a total amount V1 of chips W in the chip tray 23 before settlement for each game from the type and number of chips W, and calculates a total amount V2 of chips W bet by the losing player C (i.e., the increase in the chip tray 23 for that game) from the position, type and number of chips W bet by each player C. Then, the control device 14 adds the increase V2 in the chip tray 23 for that game to the total amount V1 of chips W in the chip tray 23 before settlement for each game to calculate the total amount V3 (=V1+V2) of chips that should be in that chip tray 23.

[0053] The control device 14 compares the total amount V3 of the chips W in the chip tray 23 with the actual total amount V0 of the chips W in the chip tray 23, and if there is a discrepancy between the total amount V3 and the actual total amount V0 (V3≠V0), it determines that there is a suspicion of an error in the determination result of the chip determination device 12. On the other hand, if the total amount V3 and the actual total amount V0 match (V3=V0), the control device 14 determines that the determination result of the chip determination device 12 is correct.

[0054] After the chips W from the losing player C are collected, the chips W are paid to the winning player C. The control device 14 calculates the total amount of chips W bet by the winning player C and the amount V4 to be paid therefor from the positions, types and numbers of chips W bet by each player C. The control device 14 determines the actual total amount in the chip tray 23 after it has been reduced by the chips W that have been paid, determines whether it matches the V4 that should be paid, and displays a match / mismatch lamp according to the determination result.

[0055] The control device 14 compares the total amount V5 (=V1+V2-V4) of chips W in the chip tray 23 with the actual total amount of chips W in the chip tray 23 after the increase in collected chips and the decrease in paid chips, and if there is a discrepancy, determines that there is a suspicion of an error in the judgment result of the tip judgment device 12. If the total amount V5 matches the actual total amount, the control device 14 determines that the judgment result of the tip judgment device 12 is correct.

[0056] For each match / mismatch determination, a lamp may be lit, for example, green if it is a match and red if it is a mismatch.

[0057] The teacher device 13 obtains the correctness of the judgment result of the tip judgment device 12 from the control device 14. When the control device 14 judges that the judgment result of the tip judgment device 12 is suspected to be an error, the teacher device 13 may input the image used in the judgment (including the suspected error) by the tip judgment device 12 and the number and type of chips W that are correct for the error as teacher data to the artificial intelligence device 12a of the tip judgment device 12 to cause it to learn. The number and type of chips that are correct for the error are actually taught to the teacher device 13 by a person who checks the image. In other words, the teacher device 13 learns the number and type of chips that are correct for the error through human instruction via a device that teaches the image of the error and the correct number at that time.

[0058] When the control device 14 determines that the judgment result of the tip judgment device 12 is correct, the teacher device 13 may further input the image used in the (correct) judgment of the tip judgment device 12 and the number and type of chips W in the judgment result of the tip judgment device 12 (i.e., the correct number and type of chips W) as teacher data into the artificial intelligence device 12a of the tip judgment device 12 to allow it to learn.

[0059] The teacher device 13 repeatedly performs a teaching operation of inputting the teaching data into the artificial intelligence device 12a of the chip determination device 12 and causing it to learn, thereby improving the accuracy of chip W determination by the chip determination device 12. The artificial intelligence device 12a of the chip determination device 12 performs image analysis of images of the states of the chips W to determine the chips W, so even if some or all of the multiple chips W placed on the gaming table 4 are hidden by the blind spot of the camera 212, by repeatedly learning such incomplete images, it is possible to determine the type, number and position of the bet chip W.

[0060] Next, with reference to FIG. 4, the operation of the chip recognition learning system 10 according to this embodiment (chip recognition learning method) will be described.

[0061] As shown in FIG. 4, first, when a customer (player) C stacks and places chips W in the betting area BA of the gaming table 4 (bets chips W), the state of the stacked chips W is captured as an image by the camera 212, and the image is recorded in the game recording device 11 (step S31).

[0062] Next, the image recorded in the game recording device 11 is analyzed by the chip determination device 12 to determine the number and type of chips W bet by the customer (player) C (step S32). Note that the image analyzed by the chip determination device 12 may be selected based on an index, time, or tag that identifies the scene of collecting or paying the chips W, which is assigned to the image by the game recording device 11.

[0063] In step S32, the image of the state of the chips W recorded in the game recording device 11 is analyzed by the chip determination device 12, so that in addition to the number and type of chips W bet by the customer (player) C, the position of the chips W bet by the customer (player) C on the betting area BA may be determined, and the number and type of chips W in the chip tray 23 before settlement of each game may also be determined.

[0064] The determination result of the tip determination device 12 is output to the output device 15. The determination result of the tip determination device 12 may be output by the output device 15 to a monitor on the gaming table 4 as character information, or may be output as audio information to a headset of the dealer D, etc.

[0065] The determination result of the tip determination device 12 is also transmitted to the control device 14, and the control device 14 determines whether the determination result of the tip determination device 12 is correct or not (step S33).

[0066] If the control device 14 determines that there is a suspicion of an error in the judgment result of the chip judgment device 12 (step S34: No), the image used in the judgment (including the suspicion of an error) by the chip judgment device 12 and the number and type of chips W that are the correct answer for the error are input as training data from the teacher device 13 to the artificial intelligence device 12a of the chip judgment device 12, and the artificial intelligence device 12a performs learning (step S36).

[0067] On the other hand, if the control device 14 determines that the determination result of the tip determination device 12 is correct (step S34: Yes), the operation of the tip recognition learning system 10 for the game is terminated.

[0068] As described above, according to this embodiment, when it is determined that the judgment result of the chip judgment device 12 is suspected to be an error, the teacher device 13 inputs the image used in the judgment of the chip judgment device 12 and the number and type of chips W that are correct for the error as teacher data to the artificial intelligence device 12a for learning, so that the artificial intelligence device 12a can efficiently learn about image patterns for which the judgment accuracy of the chip judgment device 12 is relatively low, and can improve the judgment accuracy of the chip judgment device 12 by focusing on those image patterns. By repeating this type of teacher learning, the chip judgment device 12 can accurately recognize the chips W bet by player C, no matter how the chips W are stacked.

[0069] Furthermore, according to this embodiment, when all of the losing chips bet by each player C have been collected, the control device 14 grasps the actual total amount V0 of the chips W in the chip tray 23, calculates the total amount V3 (=V1+V2) of the chips W in that chip tray 23 by adding the increase V2 for the chip tray 23 for that game, which is calculated from the type and number of chips W bet by the losing player C, to the total amount V1 of the chips W in the chip tray 23 before settlement for each game based on the judgment result of the chip judgment device 12, compares the total amount V3 of the chips W in that chip tray 23 with the actual total amount V0 of the chips W in that chip tray 23, and if there is a difference between the total amount V3 that should be and the actual total amount V0 (V3≠V0), judges that there is a suspicion of an error in the judgment result of the chip judgment device 12. In this way, the control device 14 can automatically judge whether there is a suspicion of an error in the judgment result of the chip judgment device 12.

[0070] Furthermore, according to this embodiment, the control device 14 grasps the actual total amount V0 of the chips W in the chip tray 23 based on the RFID attached to the chips W, so the control device 14 can automatically grasp the actual total amount V0 of the chips W in the chip tray 23 using the RFID, thereby improving the measurement accuracy compared to visual measurement by an operator.

[0071] Furthermore, according to this embodiment, the game recording device 11 records the image acquired by the camera 212. Since the image is recorded by assigning an index or time, or by assigning a tag that identifies the chip collection scene or payment scene, the chip determination device 12 can easily identify the image of the state of the chip W to be analyzed from the recorded contents of the game recording device 11 by using the index, time, or tag assigned to the image, and can shorten the time required for identification.

[0072] Furthermore, according to this embodiment, even if the plurality of chips W placed on the gaming table 4 are partially or entirely hidden by the blind spot of the camera 212, the chip determination device 12 can determine the type, number and position of the bet chip W. Therefore, particularly when the plurality of chips W placed on the gaming table 4 are partially or entirely hidden by the blind spot of the operator, by having the chip determination device 12 determine the chip W bet by player C, the measurement accuracy can be improved compared to visual measurement by an operator.

[0073] It should be noted that various modifications can be made to the above-described embodiment. An example of such a modification will be described below with reference to the drawings. In the following description and the drawings used in the following description, parts that can be configured similarly to the above-described embodiment will be designated by the same reference numerals as those used for the corresponding parts in the above-described embodiment, and redundant description will be omitted.

[0074] FIG. 5 is a flowchart for explaining a modified example of the chip recognition learning method.

[0075] In the example shown in Figure 5, if the control device 14 determines that there is a suspicion of an error in the judgment result of the chip judgment device 12 (step S34: No), the image used in the judgment (including the suspicion of an error) by the chip judgment device 12 and the number and type of chips W that are the correct answer for the error are input as training data from the teacher device 13 to the artificial intelligence device 12a of the chip judgment device 12, and the artificial intelligence device 12a performs learning (step S36).

[0076] On the other hand, if the control device 14 determines that the judgment result of the tip judgment device 12 is correct (step S34: Yes), the image used for the (correct) judgment by the tip judgment device 12 and the number and type of chips W in the judgment result of the tip judgment device 12 (i.e., the correct number and type of chips W) are further input as teacher data from the teacher device 13 to the artificial intelligence device 12a of the tip judgment device 12, and the artificial intelligence device 12a further learns (step S35).

[0077] According to this embodiment, the chip determination device 12 can further improve the determination accuracy not only for image patterns for which the determination accuracy is relatively low, but also for image patterns for which the determination accuracy is relatively high, thereby enabling the chip determination device 12 to recognize the chips W bet by player C with higher accuracy.

[0078] FIG. 6 is a flowchart illustrating another modified example of the chip recognition learning method.

[0079] In the example shown in Figure 6, if the control device 14 determines that the judgment result of the tip judgment device 12 is correct (step S34: Yes), the image used for the (correct) judgment by the tip judgment device 12 and the number and type of chips W in the judgment result of the tip judgment device 12 (i.e., the correct number and type of chips W) are further input as teacher data from the teacher device 13 to the artificial intelligence device 12a of the tip judgment device 12, and the artificial intelligence device 12a further learns (step S35).

[0080] On the other hand, if the control device 14 judges that there is a possibility of an error in the judgment result of the chip judgment device 12 (step S34: No), the chip recognition learning system 1 in the game Ends the operation of 0.

[0081] According to this embodiment, when the judgment result of the tip judgment device 12 is judged to be correct, the teacher device 13 inputs the image used for the judgment by the tip judgment device 12 and the number and type of chips W in the judgment result (correct answer) as teacher data to the artificial intelligence device 12a to make it learn, so that the artificial intelligence device 12a can efficiently learn about image patterns for which the judgment accuracy of the tip judgment device 12 is relatively high, and can improve the judgment accuracy of the tip judgment device 12 by focusing on those image patterns. By repeating such teacher learning, the tip judgment device 12 can accurately recognize the chips W bet by player C.

[0082] Fig. 7 is a diagram schematically showing an amusement center equipped with a chip recognition learning system 100 according to the second embodiment. Fig. 8 is a block diagram showing a schematic configuration of the chip recognition learning system 100 according to the second embodiment.

[0083] As shown in Figure 7, in the second embodiment, in addition to the surveillance camera 212 that captures the state of the chips W stacked and arranged in the betting area BA, a surveillance camera 24 for the chip tray that captures the state of the chips W in the chip tray 23 managed by the dealer D is provided outside the gaming table 4.

[0084] The chip recognition learning system 100 according to the second embodiment is communicably connected to the monitoring camera 212 and the chip tray monitoring camera 24, respectively.

[0085] 8, the game recording device 11 records the state of the chips W in the chip tray 23 as an image captured by the chip tray camera 24. The image may be a moving image or a series of still images.

[0086] The control device 14 includes an artificial intelligence device 14a (artificial intelligence device for determining the correct answer) different from the artificial intelligence device 12a of the chip determination device 12, which performs image recognition using, for example, deep learning technology, and performs image analysis on the image of the state of the chips W in the chip tray 23 recorded in the game recording device 11, determines the number and type of chips W in the chip tray 23, and grasps the actual total amount V0.

[0087] According to the second embodiment, the control device 14 can automatically grasp the actual total amount of chips W in the chip tray 23 using an artificial intelligence device 14a for determining the correct answer, thereby improving the measurement accuracy compared to visual measurement by an operator.

[0088] In addition, there is something generally called unsupervised data learning, which teaches whether the results determined by artificial intelligence are correct or incorrect, and this invention also considers this to be supervised data learning.

[0089] The above-described embodiments have been described for the purpose of enabling a person having ordinary skill in the art to practice the present invention. Various modifications of the above-described embodiments would naturally be possible for a person skilled in the art, and the technical concept of the present invention may also be applied to other embodiments. Therefore, the present invention is not limited to the described embodiments, but should be accorded the widest scope in accordance with the technical concept defined by the claims.

Claims

1. A chip recognition learning system in an amusement facility having a gaming table, comprising: a game recording device that takes a picture of the state of the chips stacked on the gaming table with a camera and records the image; a chip determination device that analyzes the recorded images of the states of the chips and determines the number and type of chips placed on the gaming table by the player, the chip determination device including an artificial intelligence device that has been trained in advance using a plurality of training data; a control device that determines whether the determination result of the chip determination device is correct; a teacher device that, when the control device determines that there is a suspicion of an error in the judgment result of the chip judgment device, inputs the image used in the judgment of the chip judgment device and the number and type of chips that are correct for the error as the teacher data into the artificial intelligence device to allow additional learning, the chip determination device is capable of determining, from the image recorded in the game recording device, the type and number of chips in a chip tray provided on the gaming table in a game played on the gaming table, as well as the position, type and number of chips bet by each player; The control device grasps the actual total amount of chips in the chip tray when all of the losing chips bet by each player have been collected, calculates the total amount of chips that should be in the chip tray by adding the increase in the chip tray amount for that game calculated from the type and number of chips bet by the losing player to the total amount of chips in the chip tray before settlement for each game based on the judgment result of the chip judgment device, compares the total amount of chips that should be in the chip tray with the actual total amount of chips in the chip tray, and if there is a difference between the total amount that should be and the actual total amount, judges that there is a suspicion of an error in the judgment result of the chip judgment device.

2. The chip recognition learning system according to claim 1, The teaching device is a chip recognition learning system in which, when the control device determines that the judgment result of the chip judgment device is correct, the image used in the judgment by the chip judgment device and the number and type of chips in the judgment result are further input into the artificial intelligence device as teaching data for learning.

3. A chip recognition learning system in an amusement facility having a gaming table, comprising: a game recording device that takes a picture of the state of the chips stacked on the gaming table with a camera and records the image; a chip determination device that analyzes the recorded images of the states of the chips and determines the number and type of chips placed on the gaming table by the player, the chip determination device including an artificial intelligence device that has been trained in advance using a plurality of training data; a control device that determines whether the determination result of the tip determination device is correct, When the control device determines that there is a suspicion of an error in the judgment result of the chip judgment device, the artificial intelligence device receives the image used in the judgment of the chip judgment device and the number or type of chips that are correct for the error as the training data from a training device, and performs additional learning; the chip determination device is capable of determining, from the image recorded in the game recording device, the type and number of chips in a chip tray provided on the gaming table in a game played on the gaming table, as well as the position, type and number of chips bet by each player; The control device grasps the actual total amount of chips in the chip tray when all of the losing chips bet by each player have been collected, calculates the total amount of chips that should be in the chip tray by adding the increase in the chip tray amount for that game calculated from the type and number of chips bet by the losing player to the total amount of chips in the chip tray before settlement for each game based on the judgment result of the chip judgment device, compares the total amount of chips that should be in the chip tray with the actual total amount of chips in the chip tray, and if there is a difference between the total amount that should be and the actual total amount, judges that there is a suspicion of an error in the judgment result of the chip judgment device.

4. A chip recognition learning system according to any one of claims 1 to 3, The control device is a chip recognition learning system that grasps the actual total value of the chips in the chip tray based on the RFID tags attached to the chips.

5. A chip recognition learning system according to any one of claims 1 to 3, The control device is a chip recognition learning system including an artificial intelligence device for determining a correct answer, which is different from the artificial intelligence device of the chip judgment device, and grasps the actual total amount of chips in the chip tray from the image recorded in the game recording device.

6. A chip recognition learning system according to any one of claims 1 to 3, A chip recognition learning system in which the game recording device records images acquired from a camera by assigning an index or time stamp, or by assigning a tag that identifies the chip collection scene or payment scene, so that the game record can be later analyzed by the chip judgment device.

7. A chip recognition learning system according to any one of claims 1 to 3, The chip determination device is a chip recognition learning system that can determine the type, number and position of multiple chips placed on the gaming table even if some or all of the chips are hidden by the blind spot of the camera.

8. A chip recognition learning method in an amusement facility having a gaming table, comprising: a game recording step of photographing the state of the chips stacked on the gaming table with a camera and recording the image; a chip determination step using an artificial intelligence device that has previously learned from a plurality of training data to determine the number and type of chips placed on the gaming table by the player by analyzing the image of the state of the chips recorded; a step of a control device determining whether a determination result of the chip determination step is correct; a teaching step of, when the control device determines that there is a suspicion of an error in the determination result of the chip determination step, inputting the image used in the determination of the chip determination step and the number and type of chips that are correct for the error as the teaching data into the artificial intelligence device to cause additional learning, In the chip determination step, the type and number of chips in a chip tray provided on the gaming table, and the position, type and number of chips bet by each player in a game played on the gaming table can be determined from the image recorded in the game recording step; The control device grasps the actual total amount of chips in the chip tray when all of the losing chips bet by each player have been collected, calculates the total amount of chips that should be in the chip tray by adding the increase in the chip tray amount for that game calculated from the type and number of chips bet by the losing player to the total amount of chips in the chip tray before settlement for each game based on the judgment result of the chip judgment step, compares the total amount of chips that should be in the chip tray with the actual total amount of chips in the chip tray, and if there is a difference between the total amount that should be and the actual total amount, judges that there is a suspicion of an error in the judgment result of the chip judgment step.

9. A chip recognition learning system in an amusement facility having a gaming table, comprising: a game recording device that takes a picture of the state of the chips stacked on the gaming table with a camera and records the image; a chip determination device that analyzes the recorded images of the states of the chips and determines the number and type of chips placed on the gaming table by the player, the chip determination device including an artificial intelligence device that has been trained in advance using a plurality of training data; a control device that determines whether the determination result of the chip determination device is correct; a teacher device that, when the control device determines that the determination result of the chip determination device is correct, inputs the image used for the determination of the chip determination device and the number and type of chips in the determination result as the teacher data into the artificial intelligence device to allow additional learning, the chip determination device is capable of determining, from the image recorded in the game recording device, the type and number of chips in a chip tray provided on the gaming table in a game played on the gaming table, as well as the position, type and number of chips bet by each player; When all of the losing chips bet by each player have been collected, the control device ascertains the actual total amount of chips in the chip tray, calculates the total amount of chips that should be in the chip tray by adding the increase in the chip tray for that game calculated from the type and number of chips bet by the losing player to the total amount of chips in the chip tray before settlement for each game based on the judgment result of the chip judgment device, compares the total amount of chips that should be in the chip tray with the actual total amount of chips in the chip tray, and judges that the judgment result of the chip judgment device is correct if the total amount that should be and the actual total amount match.

10. A chip recognition learning system in an amusement facility having a gaming table, comprising: a game recording device that takes a picture of the state of the chips stacked on the gaming table with a camera and records the image; a chip determination device that analyzes the recorded images of the states of the chips and determines the number and type of chips placed on the gaming table by the player, the chip determination device including an artificial intelligence device that has been trained in advance using a plurality of training data; a control device that determines whether the determination result of the tip determination device is correct, When the control device determines that the judgment result of the chip judgment device is correct, the artificial intelligence device inputs the image used for the judgment of the chip judgment device and the number or type of chips in the judgment result as the teaching data from the teaching device, and performs additional learning; the chip determination device is capable of determining, from the image recorded in the game recording device, the type and number of chips in a chip tray provided on the gaming table in a game played on the gaming table, as well as the position, type and number of chips bet by each player; When all of the losing chips bet by each player have been collected, the control device ascertains the actual total amount of chips in the chip tray, calculates the total amount of chips that should be in the chip tray by adding the increase in the chip tray for that game calculated from the type and number of chips bet by the losing player to the total amount of chips in the chip tray before settlement for each game based on the judgment result of the chip judgment device, compares the total amount of chips that should be in the chip tray with the actual total amount of chips in the chip tray, and judges that the judgment result of the chip judgment device is correct if the total amount that should be and the actual total amount match.

11. The chip recognition learning system according to claim 9 or 10, The control device is a chip recognition learning system that grasps the actual total value of the chips in the chip tray based on the RFID tags attached to the chips.

12. The chip recognition learning system according to claim 9 or 10, The control device is a chip recognition learning system including an artificial intelligence device for determining a correct answer, which is different from the artificial intelligence device of the chip judgment device, and grasps the actual total amount of chips in the chip tray from the image recorded in the game recording device.

13. The chip recognition learning system according to claim 9 or 10, A chip recognition learning system in which the game recording device records images acquired from a camera by assigning an index or time stamp, or by assigning a tag that identifies the chip collection scene or payment scene, so that the game record can be later analyzed by the chip judgment device.

14. The chip recognition learning system according to claim 9 or 10, The chip determination device is a chip recognition learning system that can determine the type, number and position of multiple chips placed on the gaming table even if some or all of the chips are hidden by the blind spot of the camera.

15. A chip recognition learning method in an amusement facility having a gaming table, comprising: a game recording step of photographing the state of the chips stacked on the gaming table with a camera and recording the image; a chip determination step using an artificial intelligence device that has previously learned from a plurality of training data sets to determine the number and type of chips bet by a player by analyzing the recorded image of the state of the chips; a step of a control device determining whether a determination result of the chip determination step is correct; a teaching step of, when the control device determines that the determination result of the chip determination step is correct, inputting the image used in the determination of the chip determination step and the number and type of chips in the determination result as teaching data into the artificial intelligence device to cause additional learning, In the chip determination step, the type and number of chips in a chip tray provided on the gaming table, and the position, type and number of chips bet by each player in a game played on the gaming table can be determined from the image recorded in the game recording step; The control device grasps the actual total amount of chips in the chip tray when all of the losing chips bet by each player have been collected, calculates the total amount of chips that should be in the chip tray by adding the increase in the chip tray amount for that game calculated from the type and number of chips bet by the losing player to the total amount of chips in the chip tray before settlement for each game based on the judgment result of the chip judgment step, compares the total amount of chips that should be in the chip tray with the actual total amount of chips in the chip tray, and judges that the judgment result of the chip judgment step is correct when the total amount that should be and the actual total amount match.

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