Patent Publication Number: US-7901285-B2

Title: Automated game monitoring

Description:
CLAIM OF PRIORITY 
     This application claims priority to U.S. Provisional Application No. 60/568,977, entitled “AUTOMATED PLAYER TRACKING AND ANALYSIS SYSTEM AND METHOD”, filed on May 7, 2004, having inventors Louis Tran, Nam Banh, Gaurav Dudhoria and Charles Dang; which is incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention is directed to signal processing systems, 
     2. Description of the Related Art 
     Gambling activities and gaming relate back to the beginning of recorded history. Casino gambling has since developed into a multi-billion dollar worldwide industry. Typically, casino gambling consists of a casino accepting a wager from a player based on the outcome of a future event or the play of an organized game of skill or chance. Based on the result of the event or game play, the casino either keeps the wager or makes some type of payout to the player. The events include sporting events while the casino games include blackjack, poker, baccarat, craps, and roulette. The casino games are typically run by casino operators which monitor and track the progress of the game and the players involved in the game. 
     Blackjack is a casino game played with cards on a blackjack table. Players try to achieve a score derived from cards dealt to them that is greater than the dealer&#39;s card score. The maximum score that can be achieved is twenty-one. The rules of blackjack are known in the art. 
     Casino operators typically track players at table games manually with paper and pencil. Usually, a pit manager records a “buy-in”, average bet, and the playing time for each rated player on paper. A separate data entry personnel then enters this data into a computer. The marketing and operations department can decide whether to “comp” a player with a free lodging, or otherwise provide some type of benefit to a player to entice the player to gamble at the particular casino, based on the player&#39;s data. The current “comp” process is labor intensive, and it is prone to mistakes. 
     Protection of game integrity is also an important concern of gaming casinos. Determining whether a player or group of players are implementing orchestrated methods that decrease casino winnings is very important. For example, in “Bringing Down the House”, by Ben Mezrich, a team of MIT students beat casinos by using “team play” over a period of time. Other methods of cheating casinos and other gaming entities include dealer-player collusion, hole card play, shuffle tracking, and dealer dumping. 
     Automatic casino gaming monitoring systems should also be flexible. For example, a gaming monitoring system should be flexible so that it can work with different types of games, different types of gaming pieces (such as cards and chips), and in different conditions (such as different lighting environments). A gaming monitoring system that must be used with specifically designed gaming pieces or ideal lighting conditions is undesirable as it is not flexible to different types of casinos, or even different games and locations within a single casino. 
     What is needed is a system to manage casino gaming in terms of game tracking and game protection. For purposes of integrity, accuracy, and efficiency, it would be desirable to fulfill this need with an automatic system that requires minimal human interaction. The system should be accurate in extracting data from a game in progress, expandable to meet the needs of games having different numbers of players, and flexible in the manner the extracted data can be analyzed to provide value to casinos and other gaming entities. 
     SUMMARY OF THE INVENTION 
     The technology herein, roughly described, pertains to automatically monitoring a game. A determination is made that an event has occurred by capturing the relevant actions and/or results of relevant actions of one or more participants (i.e., one or more players and one or more game operators) in a game. Actions and/or processes are then performed based on the occurrence of the event. 
     A game monitoring system for monitoring a game may include a first camera, one or more supplemental cameras and an image processing engine. The first camera may be directed towards a game surface at a first angle from the game surface and configured to capture images of the game surface. The one or more supplemental cameras are directed towards the game surface at a second angle from the game surface and configured to capture images of the game surface. The first angle and the second angle may have a difference of at least forty-five degrees in a vertical plane with respect to the game surface. The image processing engine may process the images captured of the game surface by the first camera and the one or more supplemental cameras. 
     A method for monitoring a game begins with receiving image information associated with a game environment. Next, image information is processed to derive game information. The occurrence of an event is then determined from the game information. Finally, an action is initiated responsive to the event. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates one embodiment of a game monitoring environment. 
         FIG. 2  illustrates an embodiment of a game monitoring system. 
         FIG. 3  illustrates another embodiment of a game monitoring system. 
         FIG. 4  illustrates an embodiment of a method for monitoring a game. 
         FIG. 5A  illustrates an example of an image of a blackjack game environment. 
         FIG. 5B  illustrates an embodiment of a player region. 
         FIG. 5C  illustrates another example of an image of a blackjack game environment 
         FIG. 6  illustrates one embodiment of a method for performing a calibration process. 
         FIG. 7A  illustrates one embodiment of a method for performing card calibration. 
         FIG. 7B  illustrates one embodiment of a stacked image. 
         FIG. 8A  illustrates one embodiment of a method for performing chip calibration. 
         FIG. 8B  illustrates another embodiment of a method for performing chip calibration process 
         FIG. 8C  illustrates an example of a top view of a chip. 
         FIG. 8D  illustrates an example of a side view of a chip. 
         FIG. 9A  illustrates an example of an image of chip stacks for use in triangulation. 
         FIG. 9B  illustrates another example of an image of chip stacks for use in triangulation. 
         FIG. 10  illustrates one embodiment of a game environment divided into a matrix of regions. 
         FIG. 11  illustrates one embodiment of a method for performing card recognition during gameplay. 
         FIG. 12  illustrates one embodiment of a method for determining the rank of a detected card. 
         FIG. 13  illustrates one embodiment of a method for detecting a card and determining card rank. 
         FIG. 14  illustrates one embodiment of a method for determining the contour of the card cluster 
         FIG. 15  illustrates one embodiment of a method for detecting a card edge within an image 
         FIG. 16  illustrates an example of generated trace vectors within an image. 
         FIG. 17  illustrates one example of detected corner points on a card within an image. 
         FIG. 18  illustrates one embodiment of a method of determining the validity of a card. 
         FIG. 19  illustrates one example of corner and vector calculations of a card within an image. 
         FIG. 20  illustrates one embodiment of a method for determining the rank of a card. 
         FIG. 21  illustrates one example of a constellation of card pips on a card within an image. 
         FIG. 22  illustrates one embodiment of illustrates one embodiment of a method for recognizing the contents of a chip tray by well. 
         FIG. 23  illustrates one embodiment of a method for detecting chips during game monitoring. 
         FIG. 24A  illustrates one embodiment of clustered pixel group representing a wagering chip within an image. 
         FIG. 24B  illustrates one embodiment of a method for assigning chip denomination and values. 
         FIG. 25  illustrates another embodiment for performing chip recognition. 
         FIG. 26A  illustrates one embodiment of a mapped chip stack within an image. 
         FIG. 26B  illustrates an example of a mapping of a chip stack in RGB space within an image. 
         FIG. 26C  illustrates another example of a mapping of a chip stack in RGB space within an image. 
         FIG. 26D  illustrates yet another example of a mapping of a chip stack in RGB space within an image. 
         FIG. 27  illustrates one embodiment of game monitoring state machine. 
         FIG. 28  illustrates one embodiment of a method for detecting a stable ROI. 
         FIG. 29  illustrates one embodiment of a method for determining whether chips are present in a chip ROI. 
         FIG. 30A  illustrates one embodiment of a method for determining whether a first card is present in a card ROI. 
         FIG. 30B  illustrates one embodiment of a method for determining whether an additional card is present in a card ROI. 
         FIG. 31  illustrates one embodiment of a method for detecting a split. 
         FIG. 32  illustrates one embodiment of a method for detecting end of play for a current player. 
         FIG. 33  illustrates one embodiment of a method for monitoring dealer events within a game. 
         FIG. 34  illustrates one embodiment of a method for detecting dealer cards. 
         FIG. 35  illustrates one embodiment of a method for detecting payout. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention provides a system and method for monitoring a game, extracting player related and game operator related data, and processing the data. In one embodiment, the present invention determines an event has occurred by capturing the relevant actions and/or the results of relevant actions of one or more participants (i.e., one or more players and one or more game operators) in a game. Actions and/or processes are then performed based on the occurrence of the event. The system and methods are flexible in that they do not require special gaming pieces to collect data. Rather, the present invention is calibrated to the particular gaming pieces and environment already used in the game. The data extracted can be processed and presented to aid in game security, player and game operator progress and history, determine trends, maximize the integrity and draw of casino games, and a wide variety of other purposes. The data is generally retrieved through a series of images captured before and during game play. 
     Examples of casino games that can be monitored include blackjack, poker, baccarat, roulette, and other games. For purposes of discussion, the present invention will be described with reference to a blackjack game. Thus, some relevant player actions include wagering, splitting cards, doubling down, insurance, surrendering and other actions. Relevant operator actions in blackjack may include dealing cards, dispersing winnings, and other actions. Participant actions, determined events, and resulting actions performed are discussed in more detail below. 
     An embodiment of a game monitoring environment is illustrated in  FIG. 1 . Game monitoring environment includes game monitoring system  100  and game surface  130 . System  100  is used to monitor a game that is played on game surface  130 . Game monitoring system  100  includes first camera  110 , supplemental camera  120 , computer  140 , display device  160  and storage device  150 . Computer  140  is connectively coupled to first camera  110 , supplemental camera  120 , display device  160  and storage device  150 . First camera  110  and supplemental camera  120  capture images of gaming surface  130 . Gaming surface  130  may include gaming pieces, such as dice  132 , cards  134 , chips  136  and other gaming pieces. Images captured by first camera  110  and supplemental camera  120  are provided to computer  140 . Computer  140  processes the images and provides information derived from the images to be displayed on display device  160 . Images and other information can be stored on storage device  150 . In one embodiment, computer  140  includes an image processor engine (IPE) for processing images captured by cameras  110  and  120  to derive game data. In another embodiment, one or both of cameras  110  and  120  include an IPE for processing images captured by the cameras and for deriving game data. In this case, the cameras are interconnected via a wired or wireless transmission medium. This communication link allows one camera to process images captured from both cameras, or one camera to synchronize to the other camera, or one camera to act as a master and the other acts as a slave to derive game data. 
     In one embodiment, first camera  110  and supplemental camera  120  of system  100  are positioned to allow an IPE to triangulate the position as well as determine the identity and quantity of cards, chips, dice and other game pieces. In one embodiment, triangulation is performed by capturing an image of game surface  130  from different positions. In the embodiment shown, first camera  110  captures an image of a top view playing surface  130  spanning an angle θ. Angle θ may be any angle as needed by the particular design of the system. Supplemental camera  120  captures an image of a side view of playing surface  130  spanning an angle φ. The images overlap for surface portion  138 . An IPE within system  100  can then match pixels from images captured by first camera  110  to pixels from images captured by supplemental camera  120  to ascertain game pieces  132 ,  134  and  136 . In one embodiment, other camera positions can be used as well as more cameras. For example, a supplemental camera can be used to capture a portion of the game play surface associated with each player. This is discussed in more detail below. 
     An embodiment of a game monitoring system  200  is illustrated in  FIG. 2 . Game monitoring system  200  may be used to implement system  100  of  FIG. 1 . System  200  includes a first camera  210 , a plurality of supplemental view cameras  220 , an input device  230 , computer  240 , Local Area Network (LAN)  250 , storage device  262 , marketing/operation station  264 , surveillance station  266 , and player database server  268 . 
     In one embodiment, first camera  210  provides data through a CameraLink interface. A CameraLink to gigabit Ethernet (GbE) converter  212  may be used to deliver a video signal over larger distances to computer  240 . The transmission medium (type of transmission line) to transmit the video signal from the first camera  210  to computer  240  may depend on the particular system, conditions and design, and may include analog lines, 10/100/1000/10G Ethernet, Firewire over fiber, or other implementations. In another embodiment the transmission medium may be wireless. 
     Bit resolution of the first camera may be selected based on the implementation of the system. For example, the bit resolution may be about 8 bits/pixel. In some embodiments, the spatial resolution of the camera is selected such that it is slightly larger than the area to be monitored. In one embodiment, one spatial resolution is sixteen (16) pixels per inch, though other spatial resolutions may reasonably be used as well. In this case, for a native camera spatial resolution of 1280×1024 pixels, an area of approximately eighty inches by sixty-four inches (80″×64″) will be covered and recorded and area of approximately seventy inches by forty inches (70″×40″) will be processed. 
     The sampling or frame rate of the first camera can be selected based on the design of the system. In one embodiment, a frame rate of five or more frames per second of raw video can reliably detect events and objects on a typical casino game such as blackjack, though other frame rate may reasonably be used as well. The minimum bandwidth requirement, BW, for the communication link from first camera  210  to computer  240  can be determined by figuring the spatial resolution, R S , multiplied by the pixel resolution, R P , multiplied by the frames per second, f frames , such that BW=R S ×R P ×f frames . Thus, for a camera operating at eight pits per pixel and five frames per second with 1280×800 pixel resolution, the minimum bandwidth requirement for the communication link is (8 bits/pixel)(1200×800 pixels/frame)(5 f/s)=40 Mbs. Camera controls may be adjusted to optimize image quality and sampling. Camera controls as camera shutter speed, gain, dc offset can be adjusted by writing to the appropriate registers. The iris of the lens can be adjusted manually to modulate the amount of light that hit the sensor elements (CCD or CMOS) of the camera. 
     In one embodiment, the supplemental cameras implement an IEEE 1394 protocol in isochronous mode. In this case, the supplemental camera(s) can have a pixel resolution of 24-bit in RGB format, a spatial resolution of 640×480, and capture images at a rate of five frames per second. In one embodiment, supplemental camera controls can be adjusted include shutter speed, gain, and white balance to maximize the distance between chip denominations. 
     Input device  230  allows a game administrator, such as a pit manager or dealer, to control the game monitoring process. In one embodiment, the game administrator may enter new player information, manage game calibration, initiate and maintain game monitoring and process current game states. This is discussed in more detail below. Input device  230  may include user interface (UI), touch screen, magnetic card reader, or some other input device. 
     Computer  240  receives, processes, and provides data to other components of the system. The server may include a memory  241 , including ROM  242  and RAM  243 , input  244 , output  247 , PCI slots, processor  245 , and media device  246  (such as a disk drive or CD drive). The computer may run an operating system implemented with commercially available or custom-built operating system software. RAM may store software that implements the present invention and the Operation System. Media device  246  may store software that implements the present invention and the operating system. The input may include ports for receiving video and images from the first camera and receiving video from a storage device  262 . The input may include Ethernet ports for receiving updated software or other information from a remote terminal via the Local Area Network (LAN)  250 . The output may transfer data to storage device  262 , marketing terminal  264 , surveillance terminal  266 , and player database server  268 . 
     Another embodiment of a gaming monitoring system  300  is illustrated in  FIG. 3 . In one embodiment, gaming monitoring system  300  may be used to implement system  100  of  FIG. 1 . System  300  includes a first camera  320 , wireless transmitter  330 , a Digital Video Recorder (DVR) device  310 , wireless receiver  340 , computer  350 , dealer Graphical User Interface (GUI)  370 , LAN  380 , storage device  390 , supplemental cameras  361 ,  362 ,  363 ,  364 ,  365 ,  366 , and  367 , and hub  360 . First camera  320  captures images from above a playing surface in a game environment to capture images of actions such as player bet, payout, cards and other actions. Supplemental cameras  361 ,  362 ,  363 ,  364 ,  365 ,  366 , and  376  are used to capture images of chips at the individual betting circle. In one embodiment, the supplemental cameras can be placed at or near the game playing surface. Computer  350  may include a processor, media device, memory including RAM and ROM, an input and an output. A video stream is captured by camera  320  and provided to DVR  310 . In one embodiment, the video stream can also be transmitted from wireless transmitter  330  to wireless receiver  340 . The captured video stream can also be sent to a DVR channel  310  for recording. Data received by wireless receiver  340  is transmitted to computer  350 . Computer  350  also receives a video stream from supplementary cameras  361 - 367 . In the embodiment illustrated, the cameras are connected to hub  360  which feeds a signal to computer  350 . In one embodiment, hub  360  can be used to extend the distance from the supplemental cameras to the server. 
     In one embodiment the overhead camera  320  can process a captured video stream with embedded processor  321 . To reduce the required storing capacity of the DVR  310 , the embedded processor  321  compresses the captured video into MPEG format or other compression formats well known in the art. The embedded processor  321  watermarks to ensure authenticity of the video images. The processed video can be sent to the DVR  310  from the camera  320  for recording. The embedded processor  321  may also include an IPE for processing raw video to derive game data. The gaming data and gaming events can be transmitted through wireless transmitter  330  (such as IEEE 802.11a/big or other protocols) to computer  350  through wireless receiver  340 . Computer  350  triggers cameras  361 - 367  to capture images of the game surface based on received game data. The gaming events may also be time-stamped and embedded into the processed video stream and sent to DVR  310  for recording. The time-stamped events can be filtered out at the DVR  310  to identify the time window in which these events occur. A surveillance person can then review the time windows of interest only instead of the entire length of the recorded video. These events are discussed in more detail below. 
     In one embodiment, raw video stream data sent to computer  350  from camera  320  triggers computer  350  to capture images using cameras  361 - 367 . In this embodiment, the images captured by first camera  320  and supplemental cameras  361 - 367  can be synchronized in time. In one embodiment, first camera  320  sends a synchronization signal to computer  350  before capturing data. In this case, all cameras of  FIG. 3  capture images or a video stream at the same time. The synchronized images can be used to determine game play states as discussed in more detail below. In one embodiment, raw video stream received by computer  350  is processed by an IPE to derive game data. The game data trigger the cameras  361 - 367  to capture unobstructed images of player betting circles. 
     In one embodiment, image processing and data processing is performed by processors within the system of  FIGS. 1-3 . The image processing derives information from captured images. The data processing processes the data derived from the information. 
     In an embodiment wherein a blackjack game is monitored, the first and supplemental cameras of systems  100 ,  200  or  300  may capture images and/or a video stream of a blackjack table. The images are processed to determine the different states in the blackjack game, the location, identification and quantity of chips and cards, and actions of the players and the dealer. 
       FIG. 4  illustrates a method  400  for monitoring a game. A calibration process is performed at step  410 . The calibration process can include system equipment as well as game parameters. System equipment may include cameras, software and hardware associated with a game monitor system. In one embodiment, elements and parameters associated with the game environment, such as reference images, and information regarding cards, chips, Region of Interest (ROIs) and other elements, are captured during calibration. An embodiment of a method for performing calibration is discussed in more detail below with respect to  FIG. 4   
     In one embodiment, a determination that a new game is to begin is made by detecting input from a game administrator, the occurrence of an event in the game environment, or some other event. Game administrator input may include a game begin or game reset input at input device  230  of  FIG. 2 . 
     Next, the game monitoring system determines whether a new game has begun. In one embodiment, a state machine is maintained by the game monitoring system. This is discussed in more detail below with respect to  FIG. 27 . In this case, the state machine determines at step  420  whether the game state should transition to a new game at step  420 . The game state machine and detecting the beginning of a new game is discussed in more detail below. If a new game is to begin, operation continues to step  430 . Otherwise, operation remains at step  420 . 
     Game monitoring begins at step  430 . In one embodiment, game monitoring includes capturing images of the game environment, processing the images, and triggering an event in response to capturing the images. In an embodiment wherein a game of blackjack is monitored, the event may be initiating card recognition, chip recognition, detecting the actions of a player or dealer, or some other event. Game monitoring is discussed in more detail below. The current game is detected to be over at step  440 . In a blackjack game, the game is detected to be over once the dealer has reconciled the player&#39;s wager and removed the cards from the gaming surface. Operation then continues to step  410  wherein the game system awaits the beginning of the next game. 
     In one embodiment, the calibration and game monitoring process both occur within the same game environment.  FIG. 5A  illustrates an embodiment of a top view of a blackjack game environment  500 . In one embodiment, blackjack environment  500  is an example of an image captured by first camera  110  of FIG.  1 . The images are then processed by a system of the present invention. Blackjack environment  500  includes several ROIs. An ROI, Region of Interest, is an area in a game environment that can be captured within an image or video stream by one or more cameras. The ROI can be processed to provide information regarding an element, parameter or event within the game environment. Blackjack environment  500  includes card dispensed holder  501 , input device  502 , dealer maintained chips  503 , chip tray  504 , card shoe  505 , dealt card  506 , player betting area  507 , player wagered chips  508 ,  513 , and  516 , player maintained chips  509 , chip stack center of mass  522 , adapted card ROI  510 ,  511 ,  512 , initial card ROI  514 , wagered chip ROI  515 , insurance bet region  517 , dealer card ROI  518 , dispensed card holder ROI  519 , card shoe ROI  520 , chip tray ROI  521 , chip well ROI  523 , representative player regions  535 , cameras  540 ,  541 ,  542 ,  543 ,  544 ,  545  and  546  and player maintained chip ROI  550 . Input device  502  may be implemented as a touch screen graphical user interface, magnetic card reader, some other input device, and/or combination thereof. Player card and chip ROIs are illustrated in more detail in  FIG. 5B . 
     Blackjack environment  500  includes a dealer region and seven player regions (other numbers of player regions can be used). The dealer region is associated with a dealer of the blackjack game. The dealer region includes chip tray  504 , dealer maintained chips  503 , chip tray ROI  521 , chip well ROI  523 , card dispensed holder  501 , dealer card ROI  518 , card shoe  505  and card shoe ROI  520 . A player region is associated with each player position. Each player region (such as representative player region  535 ) includes a player betting area, wagered chip ROI, a player initial card ROI, and adapted card ROIs and chip ROIs associated with the particular player, and player managed chip ROI. Blackjack environment  500  does not illustrate the details of each player region of system  500  for purposes of simplification. In one embodiment, the player region elements are included for each player. 
     In one embodiment, cameras  540 - 546  can be implemented as supplemental cameras of systems  100 ,  200  or  300  discussed above. Cameras  540 - 546  are positioned to capture a portion of the blackjack environment and capture images in a direction from the dealer towards the player regions. In one embodiment, cameras  540 - 546  can be positioned on the blackjack table, above the blackjack table but below a first camera of system  100 ,  200  or  300 , or in some other position that captures an image in the direction of the player regions. Each of cameras  540 - 546  captures a portion of the blackjack environment as indicated in  FIG. 5A  and discussed below in  FIG. 5B . 
     Player region  535  of  FIG. 5A  is illustrated in more detail in  FIG. 5B . Player region  535  includes most recent card  560 , second most recent card  561 , third most recent card  562 , fourth most recent card (or first dealt card)  563 , adapted card ROIs  510 ,  511 , and  512 , initial card ROI  514 , chip stack  513 , cameras  545  and  546 , player maintained chips  551 , player maintained chips ROI  550 , and player betting area  574 . Cameras  545  and  546  capture a field of view of player region  535 . Though not illustrated, a wagered chip ROI exists around player betting area  574 . The horizontal field of view for cameras  545  and  546  has an angle φ c2  and φ c1 , respectively. These FOVs may or may not overlap. Although the vertical FOV is not shown, it is proportional to the horizontal FOV by the aspect ration of the sensor element of the camera. 
     Cards  560 - 563  are placed on top of each other in the order they were dealt to the corresponding player. Each card is associated with a card ROI. In the embodiment illustrated, the ROI has a shape of a rectangle and is centered at or about the centroid of the associated card. Not every edge of each card ROI is illustrated in player region  535  in order to clarify the region. In player region  535 , most recent card  560  is associated with ROI  510 , second most recent card  561  is associated with ROI  511 , third most recent card  562  is associated with ROI  512 , and fourth most recent card  563  is associated with ROI  514 . In one embodiment, as each card is dealt to a player, an ROI is determined for the particular card. Determination of card ROIs are discussed in more detail below. 
       FIG. 5C  illustrates another embodiment of a blackjack game environment  575 . Blackjack environment  500  includes supplemental cameras  580 ,  581 ,  582 ,  583 ,  584 ,  585  and  586 , marker positions  591 , drop box  590 , dealer up card ROI  588 , dealer hole card ROI  587 , dealer hit card ROI  589 , initial player card ROI  592 , subsequent player card ROI  593 , dealer up card  595 , dealer hole card  596 , dealer hit card  594 , chip well separation regions  578  and  579 , and chip well ROI  598  and  599 . Although dealer hit cards ROIs can be segmented, monitored, and processed, for simplicity they are not shown here. 
     As in blackjack environment  500 , blackjack environment  575  includes seven player regions and a dealer region. The dealer region is comprised of the dealer card ROIs, dealer cards, chip tray, chips, marker positions, and drop box. Each player region is associated with one player and includes a player betting area, wagered chip ROI, a player card ROI, and player managed chip ROI. Although one player can be associated with more than one player region. As in blackjack environment  500 , not every element of each player region is illustrated in  FIG. 5C  in order to simplify the illustration of the system. 
     In one embodiment, supplemental cameras  580 - 586  of blackjack environment  575  can be used to implement the supplemental cameras of systems  100 ,  200  or  300  discussed above. Cameras  580 - 586  are positioned to capture a portion of the blackjack environment and capture images in the direction from the player regions towards the dealer. In one embodiment, cameras  580 - 586  can be positioned on the blackjack table, above the blackjack table but below a first camera of system  100 ,  200  or  300 , or in some other direction towards the dealer from the player regions. In another embodiment, the cameras  580 - 586  can be positioned next to a dealer and directed to capture images in the direction of the players. 
       FIG. 6  illustrates an embodiment of a method for performing a calibration process  650  as discussed above in step  410  of  FIG. 4 . Calibration process  650  can be used with a game that utilizes playing pieces such as cards and chips, such as blackjack, or other games with other playing pieces as well. 
     In one embodiment, the calibration phase is a learning process where the system determines the features and size of the cards and chips as well as the lighting environment and ROIs. Thus, in this manner, the system of the present invention is flexible and can be used for different gaming systems because it “learns” the parameters of a game before monitoring and capturing game play data. In one embodiment, as a result of the calibration process in a blackjack game, the parameters that are generated and stored include ROI dimensions and locations, chip templates, features and sizes, an image of an empty chip tray, an image of the gaming surface with no cards or chips, and card features and sizes. The calibration phase includes setting first camera and supplemental camera parameters to best utilize the system in the current environment. These parameters are gain, white balancing, and shutter speed among others. Furthermore, the calibration phase also maps the space of the first camera to the space of the supplemental cameras. This space triangulation identifies the general regions of the chips or other gaming pieces, thus, minimizes the search area during the recognition process. The space triangulation is described in more detail below. 
     Method  650  begins with capturing and storing reference images of cards at step  655 . In one embodiment, this includes capturing images of ROIs with and without cards. In the reference images having cards, the identity of the cards is determined and stored for use in comparison of other cards during game monitoring. Step  655  is discussed in more detail below with respect to  FIG. 7A . Next, reference images of wagering chips are captured and stored at step  665 . Capturing and storing a reference image of wagering chips is similar to that of a card and discussed in more detail below with respect to  FIG. 8A . Reference images of a chip tray are then captured and stored at step  670 . 
     Next, in one embodiment, reference images of play surface regions are captured at step  675 . In this embodiment, the playing surface of the gaming environment is divided into play surface regions. A reference image is captured for each region. The reference image of the region can then be compared to an image of the region captured during game monitoring. When a difference is detected between the reference image and the image captured during game monitoring, the system can determine an element and/or action causing the difference. An example of game surface  900  divided into play surface regions is illustrated in  FIG. 10 . Game surface  1000  includes a series of game surface regions  1010  includes three rows and four columns of regions. Other numbers of rows and columns, or shapes of regions in addition to rectangles, such as squares, circles and other shapes, can be used to capture regions of a game surface.  FIG. 10  is discussed in more detail below. 
     Triangulation calibration is then performed at step  680 . In one embodiment, multiple cameras are used to triangulate the position of player card ROIs, player betting circle ROIs, and other ROIs. The ROIs may be located by recognition of markings on the game environment, detection of chips, cards or other playing pieces, or by some other means. Triangulation calibration is discussed in more detail below with respect to  FIGS. 9A and 9B . Game ROIs are then determined and stored at step  685 . The game ROIs may be derived from reference images of cards, chips, game environment markings, calibrated settings in the gaming system software or hardware, operator input, or from other information. Reference images and other calibration data are then stored at step  690 . Stored data may include reference images of one or more cards, chips, chip trays, game surface regions, calibrated triangulation data, other calibrated ROI information, and other data. 
       FIG. 7A  illustrates an embodiment of a method  700  for performing card calibration as discussed above at step  655  of method  650 . Method  700  begins with capturing an empty reference image I eref  of a card ROI at step  710 . In one embodiment, the empty reference image is captured using an first camera of systems  100 ,  200 , or  300 . In one embodiment, the empty reference image I eref  consists of an image of a play environment or ROI where one or more cards can be positioned for a player during a game, but wherein none are currently positioned. Thus, in the case of a blackjack environment, the empty reference image is of the player card ROI and consists of an entire or portion of a blackjack table without any cards placed at the particular portion captured. Next, a stacked image I stk  is captured at step  712 . In one embodiment, the stacked image is an image of the same ROI or environment that is “stacked” in that it includes cards placed within one or more card ROIs. In one embodiment, the cards may be predetermined ranks and suits at predetermined places. This enables images corresponding to the known card rank and suit to be stored. An example of a stacked image I stk    730  is illustrated in  FIG. 7B . Image  730  includes cards  740 ,  741 ,  742 ,  743 ,  744 ,  745 , and  746  located at player ROIs. Cards  747 ,  748 ,  749 ,  750  and  751  are located at the dealer card ROI. Cards  740 ,  741 ,  742 ,  743 , and  747  are all a rank of three, while cards  744 ,  745 , and  746  are all a rank of ace. Cards  748 ,  749 ,  750  and  751  are all ten value cards. In one embodiment, cards  740 - 751  are selected such that the captured image(s) can be used to determine rank calibration information. This is discussed in more detail below. 
     After the stacked image is captured, a difference image I diff  comprised of the absolute difference between the empty reference image I eref  and the stacked image I stk  is calculated at step  714 . In one embodiment, the difference between the two images will be the absolute difference in intensity between the pixels comprising the cards in the stacked image and those same pixels in the empty reference image. 
     Pixel values of I diff  are binarized using a threshold value at step  716 . In one embodiment, a threshold value is determined such that a pixel having a change in intensity greater than the threshold value will be assigned a particular value or state. Noise can be calculated and removed from the difference calculations before the threshold value is determined. In one embodiment, the threshold value is derived from the histogram of the difference image. In another embodiment, the threshold value is typically determined to be some percentage of the average change in intensity for the pixels comprising the cards in the stacked image. In this case, the percentage is used to allow for a tolerance in the threshold calculation. In yet another embodiment, the threshold is determined from the means and the standard deviations of a region of I eref  or I stk  with constant background Once the threshold calculation is determined, all pixels for which the change of intensity exceeded the threshold will be assigned a value. In one embodiment, a pixel having a change in intensity greater than the threshold is assigned a value of one. In this case, the collection of pixels in I diff  with a value of one is considered the threshold image or the binary image I binary . 
     After the binarization is performed at step  716 , erosion and dilation filters are performed at step  717  on the binary image, I binary , to remove “salt-n-pepper noise”. The clustering is performed on the binarized pixels (or threshold image) at step  718 . Clustering involves grouping adjacent one value pixels into groups. Once groups are formed, the groups may be clustered together according to algorithms known in the art. Similar to the clustering of pixels, groups can be clustered or “grouped” together if they share a pixel or are within a certain range of pixels from each other (for example, within three pixels from each other). Groups may then be filtered by size such that groups smaller then a certain area are eliminated (such as seventy five percent of the area of a known card area). This allows groups that may be a card to remain. 
     Once the binarized pixels have been clustered into groups, the boundary of the card is scanned at step  720 . The boundary of the card is generated using the scanning method described in method  1400 . Once the boundary of the card is scanned, the length, width, and area of the card can be determined at step  721 . In one embodiment where known card rank and suit is placed in the gaming environment during calibration, within the card&#39;s boundary, the mean and standard deviation of color component (red, green, blue, if color camera is used) or intensity (if monochrome camera is used) of the pips of a typical card is estimated along with the white background in step  722 . The mean value of the color components and/or intensity of the pip are used to generate thresholds to binarize the interior features of the card. Step  724  stores the calibrated results for use in future card detection and recognition. In one embodiment, the length, width and area are determined in units of pixels. Table 1a and 1b below shows a sample of calibrated data for detected cards using a monochrome camera with 8 bits/pixel. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Card Calibration Data, Size and pip area 
               
            
           
           
               
               
               
               
               
               
            
               
                 Length, 
                 Width, 
                 Area(Diamond) 
                 Area(Heart) 
                 Area(Spade) 
                 Area(Club) 
               
               
                 pix 
                 Pix 
                 Pixel sq. 
                 Pixel sq. 
                 Pixel sq. 
                 Pixel sq. 
               
               
                   
               
               
                 89 
                 71 
                 235 
                 245 
                 238 
                 242 
               
               
                 90 
                 70 
                 240 
                 240 
                 240 
                 240 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 1b 
               
             
            
               
                   
               
               
                 Card Calibration Data, mean intensity 
               
            
           
           
               
               
               
               
               
            
               
                 White background 
                 Diamond 
                 Heart 
                 Spade 
                 Club 
               
               
                   
               
               
                 245 
                 170 
                 170 
                 80 
                 80 
               
               
                   
               
            
           
         
       
     
       FIG. 8A  illustrates a method for performing chip calibration as discussed above at step  665  of method  650 . Method  800  begins with capturing an empty reference image I eref  of a chip ROI at step  810  using a first camera. In one embodiment, the empty reference image I eref  consists of an image of a play environment or chip ROI where one or more chips can be positioned for a player during a game, but wherein none are currently positioned. Next, a stacked image I stk  for the chip ROI is captured at step  812 . In one embodiment, the stacked image is an image of the same chip ROI except it is “stacked” in that it includes wagering chips. In one embodiment, the wagering chips may be a known quantity and denomination in order to store images corresponding to specific quantities and denomination. After the stacked image is captured, the difference image I diff  comprised of the difference between the empty reference image I eref  and the stacked image I stk  is calculated at step  814 . Step  814  is performed similarly to step  714  of method  700 . Binarization is then performed on difference image I diff  at step  816 . Erosion and dilation operations at step  817  are perform next to remove “salt-n-pepper” noise. Next, clustering is performed on the binarized image, I binary  at step  818  to generate pixel groups. Once the binarized pixels have been grouped together, the center of mass for each group, area, and diameter are calculated and stored at step  820 . Steps  816 - 818  are similar to steps  716 - 718  of method  700 . 
     The calibration process discussed above operates on the images captured by a first camera. The following calibration process operates on images captured by one or more supplemental camera.  FIG. 8B  illustrates an embodiment of a method  840  for performing a calibration process. First, processing steps are performed to cluster an image at step  841 . In one embodiment, this includes capture I eref , determine I diff , perform binarization, erosion, dilation and clustering. Thus, step  841  may include the steps performed in steps  810 - 818  of method  800 . The thickness, diameter, center of mass, and area are calculated at distances d for chips at step  842 . In one embodiment, a number of chips are placed at different distances within the chip ROI. Images are captured of the chips at these different distances. The thickness, diameter and area are determined for a single chip of each denomination at each distance. The range of the distances captured will cover a range in which the chips will be played during an actual game. 
     Next, the chips are rotated by an angle θ R  to generate an image template at step  844 . After the rotation, a determination is made as to whether the chips have been rotated 360 degrees or until the view of the chip repeats itself at step  846 . If the chips have not been rotated 360 degrees, operation continues to step  844 . Otherwise, the chip calibration data and templates are stored at step  848 . 
       FIG. 8C  illustrates an example of a top view of a chip calibration image  850 . Image  850  illustrates chip  855  configured to be rotated at an angle θ R . FIG.  8 D illustrates a side view image  860  of chip  855  of  FIG. 8C . Image  860  illustrates the thickness T and diameter D of chip  855 . Images captured at each rotation are stored as templates. From these templates, statistics such as means and variance for each color are calculated and stored as well. In one embodiment, chip templates and chip thickness and diameter and center of mass are derived from a supplemental camera captured image similar to image  860  and the chip area, diameter, and perimeter is derived form a first camera captured image similar to image  850 . The area, thickness and diameter as a function of the coordinate of the image capturing camera are calculated and stored. An example of chip calibration parameters taken from a calibration image of first camera and supplemental camera are shown below in Table 2a and Table 2b respectively. Here the center of mass of the gaming chip in Table 2a corresponds to the center of mass of Table 2b. In one embodiment the mentioned calibration process is repeated to generate a set of more comprehensive tables. Therefore, once the center of mass of the chip stack is known from the first camera space, the calculated thickness, diameter, and area of the chip stack as seen by the supplemental camera is known by using Table 3 and Table 2a. For example, the center of mass of the chip stack, in the first camera space is (160,600). The corresponding coordinates in the supplemental camera space is (X1c,Y1c) as shown in Table 3. Using Table 2a, the calculated thickness, diameter, and area of the chip at position (X1c,Y1c) are 8, 95, and 768 respectively. 
     
       
         
           
               
             
               
                 TABLE 2a 
               
             
            
               
                   
               
               
                 Wagered chip features as seen from the first camera 
               
            
           
           
               
               
               
            
               
                   
                 Center of Mass 
                 Chip Features 
               
            
           
           
               
               
               
               
               
            
               
                 X 
                 Y 
                 Perimeter 
                 Diameter 
                 Area 
               
               
                   
               
               
                 160 
                 600 
                 80 
                 25 
                 490 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 2b 
               
             
            
               
                   
               
               
                 Wagered chip features as seen from the supplemental camera 
               
            
           
           
               
               
               
            
               
                   
                 Center of Mass 
                 Chip Features 
               
            
           
           
               
               
               
               
               
            
               
                 X 
                 Y 
                 Thickness 
                 Diameter 
                 Area 
               
               
                   
               
               
                 X1c 
                 Y1c 
                 8 
                 95 
                 768 
               
               
                   
               
            
           
         
       
     
     Chip tray calibration as discussed above with respect to step  670  of method  650  may be performed in a manner similar to the card calibration process of method  700 . A difference image I diff  is taken between an empty reference image I eref  and the stacked image I stk  of the chip tray. The difference image, Idiff, is bounded by the Region of Interest of the chip well, for example  523  of  FIG. 5A . In one embodiment, the stacked image may contain a predetermined number of chips in each row or well within the chip tray, with different wells having different numbers and denominations of chips. Each well may have a single denomination of chips or a different denomination. The difference image is then subjected to binarization and clustering. In one embodiment, the binary image is subject to erosion and dilation operation to remove “salt-n-pepper” noise prior to the clustering operation. As the clustered pixels represent a known number of chips, parameters indicating the area of pixels corresponding to a known number of chips as well as RGB values associated with the each denomination can be stored. 
     Triangulation calibration during the calibration process discussed above with respect to step  680  of method  650  involves determining the location of an object, such as a gaming chip. The location may be determined using two or more images captured of the object from different angles. The coordinates of the object within each image are then correlated together.  FIGS. 9A and 9B  illustrate images of two stacks of chips  920  and  930  captured by two different cameras. A top view camera captures an image  910  of  FIG. 9  having the chip stacks  920  and  930 . For each chip stack, the positional coordinate is determined for each stack as illustrated. In particular, chip stack  920  has positional coordinates of (50, 400) and chip stack  930  has positional coordinates of (160, 600). Image  950  of  FIG. 9B  includes a side view of chip stacks  920  and  930 . For each stack, the bottom center of the chip stack is determined and stored. 
     Table 3 shows Look-Up-Table (LUT) of a typical mapping of positional coordinates of first camera to those of supplemental cameras for wagering chip stacks  920  and  930  of  FIGS. 9A and 9B . The units of the parameters of Table 3 are in pixels. In one embodiment, the mentioned calibration process is repeated to generate a more comprehensive space mapping LUT. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Space mapping Look-Up-Table (LUT) 
               
            
           
           
               
               
               
               
               
            
               
                   
                 First camera chip 
                   
                 Supplemental camera chip 
                   
               
               
                   
                 Coordinates (input) 
                   
                 coordinates (output) 
               
            
           
           
               
               
               
               
               
            
               
                   
                 X 
                 Y 
                 X 
                 Y 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 50 
                 400 
                 X2c 
                 Y2c 
               
               
                   
                 160 
                 600 
                 X1c 
                 Y1c 
               
               
                   
                   
               
            
           
         
       
     
     In one embodiment, the calibrations for cards, chips, and trip tray are performed for a number of regions in an M×N matrix as discussed above at step  655 ,  665 , and  670  in method  650 . Step  686  of method  650  localizes the calibration data of the game environment.  FIG. 10  illustrates a game environment divided into a 3×5 matrix. The localization of the card, chip, and chip tray recognition parameters in each region of the matrix improves the robustness of the gaming table monitoring system. This allows for some degree of variations in ambient setting such as lighting, fading of the table surface, imperfection within the optics and the imagers. Reference parameters can be stored for each region in a matrix, such as image quantization thresholds, playing object data (such as card and chip calibration data) and other parameters. 
     Returning to method  400  of  FIG. 4 , operation of method  400  remains at step  420  until a new game begins. Once a new game begins, game monitoring begins at step  430 . Game monitoring involves the detection of events during a monitored game which are associated with recognized game elements. Game elements may include game play pieces such as cards, chips, and other elements within a game environment. Actions are then performed in response to determining a game event. In one embodiment, the action can include transitioning from one game state within a state machine to another. An embodiment of a state machine for a black jack game is illustrated in  FIG. 27  and discussed in more detail below. 
     In one embodiment, a detected event may be based on the detection of a card.  FIG. 11  illustrates an embodiment of a method  1100  for performing card recognition during game monitoring. The card recognition process can be performed for each player&#39;s card ROI. First, a difference image I diff  is generated as the difference between a current card ROI image I roi (t) for the current time t and the empty ROI reference image I eref  for the player card ROI at step  1110 . In another embodiment, the difference image I diff  is generated as the difference between the current card ROI image and a running reference image, I rref  where I rref  is the card ROI of the I eref  within which the chip ROI containing the chip is pasted. An example I rref  is illustrated in  FIG. 5C . I rref  is the card ROI  593  of I eref  within which the chip ROI  577  is pasted. This is discussed in more detail below. The current card ROI image I roi (t) is the most recent image captured of the ROI by a particular camera. In one embodiment, each player&#39;s card ROI is tilted at an angle corresponding to the line from the center of mass of the most recent detected card to the chip tray as illustrated in  FIG. 5A-B . This makes the ROI more concise and requires processing of fewer pixels. 
     Next, binarization, erosion and dilation filtering and segmentation are performed at step  1112 . In one embodiment, step  1112  is performed in the player&#39;s card ROI. Step  1112  is discussed in more detail above. 
     The most recent card received by a player is then determined. In one embodiment, the player&#39;s card ROI is analyzed for the most recent card. If the player has only received one card, the most recent card is the only card. If several cards have been placed in the player card ROI, than the most recent card must be determined from the plurality of cards. In one embodiment, cards are placed on top of each other and closer to the dealer as they are dealt to a player. In this case, the most recent card is the top card of a stack of cards and closest to the dealer. Thus, the most recent card can be determined by detecting the card edge closest to the dealer. 
     The edge of the most recently received card is determined at step  1114 . In one embodiment, the edge of the most recently received card is determined to be the edge closest to the chip tray. If the player card ROI is determined to be a rectangle and positioned at an angle θ C  in the x,y plane as shown in  FIG. 5B , the edge may be determined by picking a point within the grouped pixels that is closest to each of the corners that are furthest away from the player, or closest to the dealer position. For example, in  FIG. 5B , the corners of the most recent card placed in ROI  510  are corners  571  and  572 . 
     Once the most recent card edge is detected, the boundary of the most recent card is determined at step  1116 . In one embodiment, the line between the corner pixels of the detected edge is estimated. The estimation can be performed using a least square method or some other method. The area of the card is then estimated from the estimated line between the card corners by multiplying a constant by the length of the line. The constant can be derived from a ratio of card area to card line derived from a calibrated card. The estimated area and area to perimeter ratio is then compared to the card area and area to perimeter ratio determined during calibration during step  1118  from an actual card. A determination is made as to whether detected card parameters match the calibration card parameters at step  1120 . If the estimated values and calibration values match within some threshold, the card presence is determined and operation continues to step  1122 . If the estimated values and calibration values do not match within the threshold, the object is determined to not be a card at step  1124 . In one embodiment, the current frame is decimated at step  1124  and the next frame with the same ROI is analyzed. 
     The rank of the card is determined at step  1122 . In one embodiment, determining card rank includes binarizing, filtering, clustering and comparing pixels. This is discussed in more detail below with respect to  FIG. 12 . 
       FIG. 12  illustrates an embodiment of a method for determining the rank of a detected card as discussed with respect to step  1122  of method  1100  of  FIG. 11 . Using the card calibration data in step  724 , the pixels within the card boundary are binarized at step  1240 . After binarization of the card, the binarized difference image is clustered into groups at step  1245 . Clustering can be performed as discussed above. The clustered groups are then analyzed to determine the group size, center and area in units of pixels at step  1250 . The analyzed groups are then compared to stored group information retrieved during the calibration process. The stored group information includes parameters of group size, center and area of rank marks on cards detected during calibration. 
     A determination is then made as to whether the comparison of the detected rank parameters and the stored rank parameters indicates that the detected rank is a recognized rank at step  1260 . In one embodiment, detected groups with parameters that do not match the calibrated group parameters within some margin are removed from consideration. Further, a size filter may optionally be used to remove groups from being processed. If the detected groups are determined to match the stored groups, operation continues to step  1265 . If the detected groups do not match the stored groups, operation may continue to step  1250  where another group of suspected rank groupings can be processed. In another embodiment, if the detected group does not match the stored group, operation ends and not further groups are tested. In this case, the detected groups are removed from consideration as possible card markings. Once the correct sized groups are identified, the groups are counted to determine the rank of the card at step  1265 . In one embodiment, any card with over nine groups is considered a rank of ten. 
     In another embodiment, a card may be detected by determining a card to be valid card and then determining card rank using templates. An embodiment of a method  1300  for detecting a card and determining card rank is illustrated in  FIG. 13 . Method  13  begins with determining the shape of a potential card at step  1310 . Determining card shape involves tracing the boundary of the potential card using an edge detector, and is discussed in more detail below in  FIG. 14 . Next, a determination is made as to whether the potential card is a valid card at step  1320 . The process of making this determination is discussed in more detail below with respect to  FIG. 18 . If the potential card is valid card, the valid card rank is determined at step  1330 . This is discussed in more detail below with respect to  FIG. 20 . If the potential card is not a valid card as determined at step  1320 , operation of method  1300  ends at step  1340  and the potential card is determined not to be a valid card. 
       FIG. 14  illustrates a method  1400  for determining a potential card shape as discussed at step  1310  of method  1300 . Method  1400  begins with generating a cluster of cards within a game environment at steps  1410  and  1412 . These steps are similar to steps  1110  and  1112  of method  1100 . In one embodiment, for a game environment such as that illustrated in  FIG. 5A , subsequent cards dealt to each player are placed on top of each other and closer to a dealer or game administrator near the chip tray. As illustrated in  FIG. 5B , most recent card  560  is placed over and closest to the chip tray than cards  561 ,  562  and  563 . Thus, when a player is dealt more than one card, an edge point on the uppermost card (which is also closest to the chip tray) is selected. 
     The edge point of the of the card cluster can be detected at step  1415  and illustrated in  FIG. 15 . In  FIG. 15 , line L 1  is drawn from the center of a chip tray  1510  to the centroid of the quantized card cluster  1520 . An edge detector (ED) can be used to scan along line L 1  at one pixel increments to perform edge detection operations, yielding GRAD(x,y)=pixel(x,y)−pixel(x 1 ,y 1 ). GRAD(x,y) yields a one when the edge detector ED is right over an edge point (illustrated as P 1  in  FIG. 15 ) of the card, and yields zero otherwise. Other edge detectors/operators, such as a Sobel filter, can also be used on the binary or gray scale difference image to detect the card edge as well. 
     After an edge point of a card is detected, trace vectors are generated at step  1420 . A visualization of trace vector generation is illustrated in  FIGS. 15-16 .  FIG. 16  illustrates two trace vectors L 2  and L 3  generated on both sides of a first trace vector L 1 . Trace vectors L 2  and L 3  are selected at a distance from first trace vector L 1  that will not place them off the space of the most recent card. In one embodiment, each vector is placed between one-eighth and one-fourth of the length of a card edge to either side of the first trace vector. In another embodiment, L 2  may be some angle in the counter-clockwise direction relative L 1  and L 3  may be the same angle in the clockwise direction relative to L 1 . 
     Next, a point is detected on each of trace vectors L 2  and L 3  at the card edge at step  1430 . In one embodiment, an ED scans along each of trace vectors L 2  and L 3 . Scanning of the edge detector ED along line L 2  and line L 3  yields two card edge points P 2  and P 3 , respectively, as illustrated in  FIG. 16 . Trace vectors T 2  and T 3  are determined as the directions from the initial card edge point and the two subsequent card edge points associated with trace vectors L 2  and L 3 . Trace vectors T 2  and T 3  define the initial opposite trace directions. 
     The edge points along the contour of the card cluster are detected and stored in an (x,y) array of K entries at step  1440  and illustrated with  FIG. 17 . As illustrated in  FIG. 17 , at each trace location, an edge detector is used to determine card edge points for each trace vector along the card edge. Half circles  1720  and  1730  having a radius R and centered at point P 1  are used to form an ED scanning path that intersects the card edge. Half circle  1720  scan path is oriented such that it crosses trace vector T 2 . Half circle  1730  scan path is oriented such that it crosses trace vector T 3 . In one embodiment, the edge detector ED starts scanning clockwise along scan path  1720  and stops scanning at edge point E 2 _ 0 . In another embodiment, the edge detector ED scans two opposite scanning directions starting from the midpoint (near point E 2 _ 0 ) of path  1720  and ending at edge point E 2 _ 0 . This reduces the number of scans required to locate an edge point. Once an edge point is detected, a new scan path is defined as having a radius extending from the edge point detected on the previous scan path. The ED will again detect the edge point in the current scan path. For example, in  FIG. 17 , a second scan path  1725  is derived by forming a radius around the detected edge point E 2 _ 0  of the previous scan path  1720 . The ED will detect edge point E 2 _ 1  in scan path  1725 . In this manner, the center of a half circle scan path moves along the trace vector T 2 , R pixels at a time, and is oriented such that it is bisected by the trace vector T 2  (P 1 , E 2 _ 0 ). Similarly, but in opposite direction, an ED process traces the card edge in the T 3  direction. When the scan paths reach the edges of the card, the ED will detect an edge on adjacent sides of the card. One or more points may be detected for each of these adjacent edges. Coordinates for these points are stored along with the first-detected edge coordinates. 
     The detected card cluster edge points are stored in an (x,y) array of K entries in the order they are detected. The traces will stop tracing when the last two edge points detected along the card edge are within some distance (in pixels) of each other or when the number of entries exceeds a pre-defined quantity. Thus, coordinates are determined and stored along the contour of the card cluster. A scan path in the shape of a half circle is used for illustration purposes only. Other operators and path shapes or patterns can be used to implement an ED scan path to detect card edge points. 
     Returning to method  1300 , after determining potential card shape, a determination is made at step  1320  as to whether the potential card is valid. An embodiment of a method  1800  for determining whether a potential card is valid, as discussed above at step  1320  of method  1300 , is illustrated in  FIG. 18 . Method  1800  begins with detecting the corner points of the card and vectors extending from the detected corner points at step  1810 . In one embodiment, the corners and vectors are derived from coordinate data from the (x,y) array of method  1400 .  FIG. 19  illustrates an image of a card  1920  with corner and vector calculations depicted. The corners are calculated as (x,y) k2  and (x,y) k3 . The corners may be calculated by determining the two vectors radiating from the vertex are right angles within a pre-defined margin. In one embodiment, the pre-defined margin at step  1810  may be a range of zero to ten degrees. The vectors are derived by forming lines between the first point (x,y) k2  and and two n th  points away in opposite direction from the first point (x,y) k2+n  and (x,y) k2−n . As illustrated in  FIG. 19 , for corners (x,y) k2  and (x,y) k3 , the vectors are generated with points (x,y) k2−n  and (x,y) k2+n , and (x,y) k3−n , and (x,y) k3+n , respectively. Thus a corner at (x,y) k2  is determined to be valid if the angle A k2  between vectors V k2  and V k2+  is a right angle within some pre-defined margin. A corner at (x,y) k3  is determined to be valid if the angle A k3  between vectors V k3  and V k3+  is a right angle within some pre-defined margin. Step  1810  concludes with the determination of all corners and vectors radiating from corners in the (x,y) array generated in method  1400 . 
     As illustrated in  FIG. 19 , vectors v k2 + and v k2  form angle A k2  and vectors v k3+  and v k3  form angle A k3 . If both angles A k2  and A k3  are detected to be about ninety degrees, or within some threshold of ninety degrees, then operation continues to step  1830 . If either of the angles is determined to not be within a threshold of ninety degrees, operation continues to step  1860 . At step  1860 , the blob or potential card is determined to not be a valid card and analysis ends for the current blob or potential card if there are no more adjacent corner set to evaluate. 
     Next, the distance between corner points is calculated if it has not already been determined, and a determination is made as to whether the distance between the corner points matches a stored card edge distance at step  1830 . A stored card distance is retrieved from information derived during the calibration phase or some other memory. In one embodiment, the distance between the corner points can match the stored distance within a threshold of zero to ten percent of the stored card edge length. If the distance between the corner points matches the stored card edge length, operation continues to step  1840 . If the distance between the adjacent corner points does not match the stored card edge length, operation continues to step  1860 . 
     A determination is made as to whether the vectors of the non-common edge at the card corners are approximately parallel at step  1840 . As illustrated in  FIG. 19 , the determination would confirm whether vectors v k2  and v k3+  are parallel. If the vectors of the non-common edge are approximately parallel, operation continues to step  1850 . In one embodiment, the angle between the vectors can be zero (thereby being parallel) within a threshold of zero to ten degrees. If the vectors of the non-common edge are determined to not be parallel, operation continues to step  1860 . 
     At step  1850 , the card edge is determined to be a valid edge. In one embodiment, a flag may be set to signify this determination. A determination is then made as to whether more card edges exist to be validated for the possible card at step  1860 . In one embodiment, when there are no more adjacent corner points to evaluate for possible card, operation continues to step  1865 . In one embodiment, steps  1830 - 1850  are performed for each edge of a potential card or card cluster under consideration. If more card edges exist to be validated, operation continues to step  1830 . In one embodiment, steps  1830 - 1850  are repeated as needed for the next card edge to be analyzed. If no further card edges are to be validated, operation continues to step  1865  wherein the determination is made if the array of edge candidates stored in  1850  is empty or not. If the array of edge candidates is empty, the determination is made at step  1880  that the card cluster does not contain a valid card. Otherwise, a card is determined to be a valid card by selecting an edge that is closest to the chip tray from an array of edge candidates stored in  1850 . 
     After the card is determined to be valid in method  1300 , the rank of the valid card is determined at step  1330 . In one embodiment, card rank can be performed similar to the process discussed above in method  1200  during card calibration. In another embodiment, masks and pip constellations can be used to determine card rank. A method  2000  for determining card rank using masks and pip constellations is illustrated in  FIG. 20 . First, the edge of the card closest to the chip tray is selected as the base edge for the mask at step  2005 .  FIG. 21  illustrates an example of a mask  2120 , although other shape and size of mask can be used. The mask is binarized at step  2010 . Next, the binarized image is clustered at step  2020 . In one embodiment, the erosion and dilation filtering are operated on the binarized image prior to clustering at step  2020 . A constellation of card pips is generated at step  2030 . A constellation of card pips is a collection of clustered pixels representing the rank of the card. An example of a constellation of card pips is illustrated in  FIG. 21 . The top most card of image  2110  of  FIG. 21  is a ten of spades. The constellation of pips  2130  within the mask  2120  includes the ten spades on the face of the card. Each spade is assigned an arbitrary shade by the clustering algorithm. 
     Next, a first reference pip constellation is then selected at step  2050 . In one embodiment, the first reference pip constellation is chosen from a library, a list of constellations generated during calibration and/or initialization, or some other source. A determination is then made as to whether the generated pip constellation matches the reference pip constellation at step  2060 . If the generated constellation matches the reference constellation, operation ends at step  2080  where the card rank is recognized. If the constellations do not match, operation continues to step  2064 . 
     A determination is made as to whether there are more reference pip constellations to compare at step  2064 . If more reference pip constellations exist that can be compared to the generated pip constellation, then operation continues to step  2070  wherein the next reference pip constellation is selected. Operation then continues to step  2060 . If no further reference pip constellations exist to be compared against the generated constellation, operation ends at step  2068  and the card is not recognized. Card rank recognition as provided by implementation of method  2000  provides a discriminate feature for robust card rank recognition. In another embodiment, rank and/or suit of the card can be determined from a combination of the partial constellation or full constellation and/or a character at the corners of the card. 
     In another embodiment, the chip tray balance is recognized well by well.  FIG. 22B  illustrates a method  2260  for recognizing contents of a chip tray by well. First, one or more wells is recognized to have a stable ROI asserted for those wells at step  2260 . In one embodiment, the stable ROI is asserted for a chip well when the two neighboring well delimiters ROI are stable. A stable event for a specified ROI is defined as the sum of difference of the absolute difference image is less than some threshold. The difference image, in this case, is defined as the difference between the current image and previous image or previous n th  image for the ROI under consideration. For example,  FIG. 5C  illustrates a chip well ROI  599  and the two neighboring well delimiters ROI  578  and  579 . When sum of the difference between the current image and the previous image or previous n th  image in ROI  578  and  579  yields a number that is less than some threshold, then a stable event is asserted for the well delimiters ROI  578  and  579 . In one embodiment, the threshold is in the range of 0 to one-fourth the area of the region of interest. In another embodiment, threshold is based on the noise statistics of the camera. Using the metrics just mentioned, the stable event for ROI  599  is asserted at step  2260 . Next, a difference image is determined for the chip tray well ROI at step  2262 . In one embodiment, the difference image I diff  is calculated as the absolute difference of the current chip tray well region of interest image I roi (t) and the empty reference image I Eref . The clustering operation is performed on the difference image at step  2266 . In one embodiment, erosion and dilation operations are performed prior to the clustering operation. 
     After clustering at step  2266 , reference chip tray parameters are compared to the clustered difference image at step  2268 . The comparison may include comparing the rows and columns of chips to corresponding chip pixel area and height of known chip quantities within a chip well. The quantity chips present in the chip tray wells are then determined at step  2270 . 
     In one embodiment, chips can be recognized through template matching using images provided by one or more supplemental cameras in conjunction with an overhead or top view camera. In another embodiment, chips can be recognized by matching each color or combination of colors using images provided by one or more supplemental cameras in conjunction with the first camera or top view camera.  FIG. 23  illustrates a method  2300  for detecting chips during game monitoring. Method  2300  begins with determining a difference image between a empty reference image, I Eref  of a chip ROI and the most recent image I roi(t)  of a chip ROI image at step  2310 . Next, the difference image is binarized and clustered at step  2320 . In one embodiment, the erosion and dilation operations are performed on the binarized image prior to clustering. The presence and center of mass of the chips is then determined from the clustered image at step  2330 . In one embodiment, the metrics used to determine the presence of the chip are the area and area to diameter. Other metrics can be used as well. As illustrated in  FIG. 24A , clustered pixel group  2430  is positioned within a game environment within image  2410 . In one embodiment, the (x,y) coordinates of the center clustered pixel group  2425  can be determined within the game environment positioning as indicated by a top view camera. In some embodiment, the distance between the supplemental camera and clustered group is determined. Once the image of the chips is segmented and the clustered group center of mass, in the top view camera space, is calculated at step  2330 . Once the center of mass of the chip stack is known, the chip stack is recognized using the images captured by one or more supplemental cameras at step  2340 . The conclusion of step  2340  assigns chip denomination to each recognized chips of the chip stack. 
       FIG. 24B  illustrates a method  2440  for assigning chip denomination and value to each recognized chip as discussed above in step  2340  of method  2300 . First, an image of the chip stack to analyze is captured with the supplemental camera  2420  at step  2444 . Next, initialization parameters are obtained at step  2446 . The initialization parameters may include chip thickness, chip diameter, and the bottom center coordinates of the chip stack from Table 3 and Table 2b. Using the space mapping LUT, Table 3, the coordinates of the bottom center of the chip stack as viewed by the supplemental camera are obtained by locating the center of mass of the chip stack as viewed from the top level camera. Using Table 2b, the chip thickness and chip diameter are obtained by locating the coordinates of the bottom center of the chip stack. With these initialization parameters, the chip stack ROI of the image captured by the supplemental camera is determined at step  2447 .  FIG. 25  illustrates an example image of a chip corresponding to an ROI captured at step  2447 . The bottom center of the chip stack  2510  is (X1c,Y1c+T/2). X1c and Y1c were obtained from Table 3 in step  2446 . The ROI in which the chip stack resides is defined by four lines. The vertical line A 1  is defined by x=X1c−D/2 where D is the diameter of the chip obtained from Table 2b. The vertical line A 2  is determined by x=X1c+D/2. The top horizontal line is y=1. The bottom horizontal line is y=Y1c−T/2 where T is the thickness of the chip obtained from Table 2b. 
     Next, the RGB color space of the chip stack ROI is then mapped into color planes at step  2448 . Mapping of the chip stack RGB color space into color planes P k  at step  2448  can be implemented as described below. 
     
       
         
           
             
               P 
               k 
             
             = 
             
               
                 
                   1 
                 
                 
                   
                     
                       I 
                       ⁡ 
                       
                         ( 
                         
                           x 
                           , 
                           y 
                         
                         ) 
                       
                     
                     = 
                     
                       C 
                       k 
                     
                   
                 
               
               
                 
                   0 
                 
                 
                   else 
                 
               
             
           
         
       
       
      
       C 
       k 
       ≡r 
       k 
       ±nσ 
       rk 
       ∩g 
       k 
       ±nσ 
       gk 
       ∩b 
       k 
       ±nσ 
       bk  
      
         
         
           
             where r k , g k , and b k  are mean red, green, and blue component of color k, σ rk  is the standard deviation of red component of color k, σ gk  is the standard deviation of green component of color k, σ bk  is the standard deviation of the blue component of color k, n is an integer, 4) obtain normalized correlation coefficient for each color. 
           
         
       
    
       FIG. 26A  illustrates an example of a chip stack image  2650  in RGB color space that is mapped into P k  color planes. The ROI is generated for the chip stack. The ROI is bounded by four lines—x=B 1 , x=B 2 , y=1, y=Y2c+T/2.  FIG. 26  B-D illustrates the mapping of a chip stack  2650  into three color planes P 0    2692 , P 1    2694 , and P 2    2696 . The pixels with value of “1”  2675  in the color plane P 0  represent the pixels of color C 0    2670  in the chip stack  2650 . The pixels with value of “1”  2685  in the color plane P 1  represent the pixels of color C,  2680  in the chip stack  2650 . The pixels with value of “1”  2664  in the color plane P 2  represent the pixels of color C 2    2650  in the chip stack  2650 . 
     A normalized correlation coefficient is then determined for each mapped color P k  at step  2450 . The pseudo code of an algorithm to obtain the normalized correlation coefficient for each color, cc k , is illustrated below. The four initialized parameters—diameter D, thickness T, bottom center coordinate (x2c,y2c)—are obtained from Table 3 and Table 2b.  FIG. 8D  illustrates an image of a chip having the vertical lines x1 and x2 using a rotation angle, θ r . The y1 and y2 parameters are the vertical chip boundary generated by the algorithm. The estimated color discriminant window is formed with x1, x2, y1, and y2. A Distortion function may map a barrel distortion view or pin cushion distortion view into the correct view as known in the art. A new discriminant window  2610  compensates for the optical distortion. In one embodiment, where optical distortion is minimal the DistortionMap function may be bypassed. The sum of all pixels over the color discriminant window divided by the area of this window yields an element in the ccArray k (r,y). The ccArray k (r,y) is the correlation coefficient array for color k with size Y dither  by MaxRotationIndex. In one embodiment, Y dither  is some fraction of chip thickness, T. The cc k (r m ,y m ) is the maximum corrrelation coefficient for color k, and is located at (r m ,y m ) in the array. Of all the mapped colors C k , the ccValue represents the highest correlation coefficient for a particular color. This color or combination thereof corresponds to a chip denomination. 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                   
                 Initialize D, T, x2c, y2=Y2c, EnterLoop 
               
               
                   
                 While EnterLoop 
               
            
           
           
               
               
            
               
                   
                 for y = −Y dither /2:Y dither /2 
               
            
           
           
               
               
            
               
                   
                 for r = 1:MaxRotationIndex 
               
            
           
           
               
               
            
               
                   
                 for k = 1:NumOfColors 
               
            
           
           
               
               
            
               
                   
                 [x1 x2] = Projection(theta(r)); 
               
               
                   
                 y1 = y2−T+y; 
               
               
                   
                 Region = DistortionMap(x1,x2,y1,y2); 
               
               
                   
                 ccArray k (r,y) = 
               
               
                   
                 sum(P k (Region))/(Area of Region); 
               
            
           
           
               
               
            
               
                   
                 end k, end r, end y 
               
               
                   
                 cc k (r m ,y m ) = max(ccArray k (r,y); 
               
               
                   
                 [Color ccValue] = max(cc k ); 
               
               
                   
                 if ccValule &gt; Threshold 
               
            
           
           
               
               
            
               
                   
                 y 2  = y 2  − T + y m   
               
               
                   
                 EnterLoop = 1; 
               
            
           
           
               
               
            
               
                   
                 else 
               
            
           
           
               
               
            
               
                   
                 EnterLoop = 0; 
               
            
           
           
               
               
            
               
                   
                 end (if) 
               
            
           
           
               
               
            
               
                   
                 End (while) 
               
               
                   
               
            
           
         
       
     
     In another embodiment, the chip recognition may be implemented by a normalized correlation algorithm. A normalized correlation with self delineation algorithm that may be used to perform chip recognition is shown below: 
     
       
         
           
             
               
                 ncc 
                 c 
               
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             wherein ncc c (u,v) is the normalized correlation coefficient, f c (x,y) is the image size x and y, fbar u,v  is the mean value at u,v, t c (x,y) is the template size of x and, tbar is the mean of the template, and c is color (1 for red, 2 for green, 3 for blue.) The chip recognition self delineation algorithm may be implemented in code as shown below: 
           
         
       
    
     
       
         
           
               
               
             
               
                   
               
             
            
               
                   
                 while EnterLoop = 1 
               
            
           
           
               
               
            
               
                   
                 do v − vNominal −1 
               
            
           
           
               
               
            
               
                   
                 x = x + 1; 
               
               
                   
                 do u = 2 
               
            
           
           
               
               
            
               
                   
                 y = y + 1 
               
               
                   
                 ccRed(x,y) = ncc (f,tRed); 
               
               
                   
                 ccGreen(x,y) = ncc (f,tGreen); 
               
               
                   
                 ccPurple(x,y) = ncc (f,tPurple); 
               
            
           
           
               
               
            
               
                   
                 until u = xMax − xMin −D1 
               
            
           
           
               
               
            
               
                   
                 until v = vNominal +1; 
               
               
                   
                 [cc Chip U V] = max (ccRed, ccGreen, ccPurple); 
               
               
                   
                 vNominal = vNominal − T1 − V; 
               
               
                   
                 x,y = 0 
               
               
                   
                 if cc &lt; Threshold 
               
            
           
           
               
               
            
               
                   
                 EnterLoop = 0 
               
            
           
           
               
               
            
               
                   
                 end 
               
            
           
           
               
               
            
               
                   
                 end 
               
               
                   
               
            
           
         
       
     
     In the code above, tRed, tGreen, tPurple are templates in the library, f is the image, ncc is the normalized correlation function, max is the maximum function, T is the thickness of the template, D is the diameter of the template, U,V is the location of the maximum correlation coefficient, and cc is the maximum correlation coefficient. 
     To implement this algorithm, the system recognizes chips through template matching using images provided by the supplemental cameras. To recognize the chips in a particular players betting circle, an image is captured by a supplemental camera that has a view of the player&#39;s betting circle. The image can be compared to chip templates stored during calibration. A correlation efficient is generated for each template comparison. The template associated with the highest correlation coefficient (ideally a value of one) is considered the match. The denomination and value of the chips is then taken to be that associated with the template. 
       FIG. 27  illustrates an embodiment of a game state machine for implementing game monitoring. States are asserted in the game state machine  2700 . During game monitoring, transition between game states occurs based on the occurrence of detected events. In one embodiment, transition between states  2704  and  2724  occurs for each player in a game. Thus, several instances of states  2704 - 2924  may occur after each other for the number of players in a game. 
       FIG. 28  illustrates one embodiment for detecting a stable region of interest. In one embodiment, state transitions for the state diagram  2700  of  FIG. 27  are triggered by the detection of a stable region of interest. First, a current image I c  of a game environment is captured at step  2810 . Next, the current image is compared to the running reference image at step  2820 . A determination is then made whether the running reference image is the same image as the current image. If the current is equal to the running reference image, then an event has occurred and a stable ROI state is asserted at step  2835 . If the current image is not equal to the running reference image, then the running reference image is set equal to the current image, and operation returns to step  2810 . In another embodiment, the running reference image I rref  can be set to the nth previous image I roi (t-n) where n is an integer as step  2840 . In another embodiment step  2820  can be replaced by the absolute difference image, I diff =|I c −I rref |. The summation of I diff  is calculated over the ROI. Step  2830  is now replaced with another metric. If the summation of I diff  image is less than some threshold, then the stable ROI state is asserted at step  2835 . In one embodiment, the threshold may be some proportionately related to the area of the ROI under consideration. In another embodiment, the I diff  is binarized and spatially filtered with erosion and dilation operations. This binarized image is then clustered. A contour trace, as described above, is operated on the binarized image. In this embodiment, step  2830  is replaced with a shape criteria test. If the contour of the binarized image pass the shape criteria test, then the stable event is asserted at step  2835 . 
     State machine  2700  begins at initialization state  2702 . Initialization may include equipment calibration, game administrator tasks, and other initialization tasks. After initialization functions are performed, a no chip state  2704  is asserted. Operation remains at the no chip state  2704  until a chip is detected for the currently monitored player. After chips have been detected, first card hunt state  2708  is asserted. 
       FIG. 29  illustrates an embodiment of a method  2900  for determining whether chips are present. In one embodiment, method  2900  implements the transition from state  2704  to state  2706  of  FIG. 27 . First, a chip region of interest image is captured at step  2910 . Next, the chip region of interest difference image is generated by taking the absolute difference of the chip region of interest of the current image I roi (t) and the empty running reference image I Eref  at step  2920 . Binarization and clustering are performed to the chip ROI difference image at step  2930 . In another embodiment, erosion and dilation operations are performed prior to clustering. A determination is then made whether clustered features match a chip features at step  2940 . If clustered features do not map the chip features, then operation continues to step  2980  where no wager is detected. At step  2980 , where no wager is detected, no transition will occur as a result of the current images analyzed at states  2704  of  FIG. 27 . If the cluster features match the chip features at step  2940 , then operation continues to step  2960 . 
     A determination is made as to whether insignificant one value pixels exist outside the region of wager at step  2960 . In one embodiment, insignificant one value pixels include any group of pixels caused by noise, camera equipment, and other factors inherent to a monitoring system. If significant one value pixels exist outside the region of wager, then operation continues to step  2980 . If significant one value pixels do not exist outside the region of wager at step  2960 , then the chip present state is asserted at step  2970 . In one embodiment step  2960  is bypassed such that if the cluster features match those of the chip features at step  2940 , the chip present state is asserted at step  2970 . 
     Returning to state machine  2700 , at first card hunt state  2708 , the system is awaiting detection of a card for the current player. Card detection can be performed as discussed above. Upon detection of a card, a first card present state  2710  is asserted. This is discussed in more detail with respect to  FIG. 32 . After the first card present state  2710  is asserted, the system recognizes the card at first card recognition state  2712 . Card recognition can be performed as discussed above. 
       FIG. 30  illustrates an embodiment of a method  3000  for determining whether to assert a first card present state. The current card region of interest (ROI) image is captured at step  3010 . Next, a card ROI difference image is generated at step  3020 . In one embodiment, the card ROI difference image is generated as the difference between a running reference image and the current ROI image. In a prefer embodiment, the running reference image is the card ROI of the empty reference image with the chip ROI cut out and replaced with the chip ROI containing the chip as determined at step  2970 . Binarization and clustering are performed to the card ROI difference image at step  3030 . In one embodiment, erosion and dilation are performed prior to clustering. Binarization and clustering can be performed as discussed in more detail above. Next, a determination is made as to whether cluster features of the difference image match the features of a card at step  3040 . This step is illustrated in method  1300 . In one embodiment, the reference card features are retrieved from information stored during the calibration phase. If cluster features do not match the features of the reference card, operation continues to step  3070  where no new card is detected. In one embodiment, a determination that no new card is detected indicates no transition will occur from state  2708  to state  2710  of  FIG. 27 . If cluster features do match a reference card at step  3040 , operation continues to step  3050 . 
     A determination is made as to whether the centroid of the cluster is within the some radius threshold from the center of the chip ROI at step  3050 . If the centroid is within the radius threshold, then operation continues to step  3060 . If the centroid is not within the radius threshold from the center of the chip ROI, then operation continues to step  3070  where a determination is made that no new card is detected. At step  3060 , a first card present event is asserted, the card cluster area is stored, and the card ROI is updated. In one embodiment, the assertion of the first card present event triggers a transition from state  2708  to state  2710  in the state machine diagram of  FIG. 27 . In one embodiment, the card ROI is updated by extending the ROI by a pre-defined number of pixels from the center of the newly detected card towards the dealer. In one embodiment this pre-defined number is the longer edge of the card. In another embodiment the pre-defined number may be 1.5 times the longer edge of the card. 
     Returning to state machine  2700 , once the first card has been recognized, second card hunt state  2714  will be asserted. While in this state, a determination is made as to whether or not a second card has been detected with method  3050   FIG. 30A . Steps  3081 ,  3082 , and  3083  are similar to steps  3010 ,  3020 ,  3030  of method  3000 . Step  3086  compares the current cluster area to the previous cluster area C 1 . If the current cluster area is greater than the previous cluster area by some new card area threshold, then a possible new card has been delivered to the player. Operation continues to step  3088  which is also illustrated in method  1300 . Step  3088  determines if the features of the cluster match those of the reference card. If so, operation continues to step  3092 . The 2 nd  card or nth card is detect to be valid at step  3092 . The cluster area is stored. The card ROI is updated. Once a second card is detected, a second card present state  2716  is asserted. Once the second card is determined to be present at state  2716 , the second card is recognized at second card recognition state  2718 . Split state  2720  is then asserted wherein the system then determines whether or not a player has split the two recognized cards with method  3100 . If a player does split the cards recognized for that player, operation continues to second card hunt state  2714 . If the player does not decide to split his cards, operation continues to Step  2722 . A method for implementing split state  2718  is discussed in more detail below. 
       FIG. 31  illustrates an embodiment of method  3100  for asserting a split state. In one embodiment, method  3100  is performed during split state  2720  of state diagram machine  2700 . A determination is made as to whether the first two player cards have the same rank at step  3110 . If the first two player cards do not have the same rank, then operation continues to step  3150  where no split state is detected. In one embodiment, a determination that no split state exists causes a transition from split state  2720  to state  2722  within  FIG. 27 . If the first two player cards have the same rank, a determination is made as to whether two clusters matching a chip template are detected at step  3120 . In one embodiment, this determination detects whether an additional wager has been made by a user such that two piles of chips have been detected. This corresponds to a stack of chips for each split card or a double down bet. If two clusters are not determined to match a chip template at step  3120 , operation continues to step  3150 . If two clusters are detected to match chip templates at step  3120 , then operation continues to step  3130 . If the features of two more clusters are found to match the features of the reference card, then the split state is asserted at step  3140 . Here the center of mass for cards and chips are calculated. The original ROI is now split in two. Each ROI now accommodates one set of chip and card. In one embodiment, asserting a split state triggers a transition from split state  2720  to second card hunt state  2724  within state machine diagram  2700  of  FIG. 27 . And the state machine diagram  2700  is duplicated. Each one representing one split hand. For each split card, the system will detect additional cards dealt to the player one card at a time. 
     The state machine determines whether the current player has a score of twenty-one at state  2722 . The total score for a player is maintained as each detected card is recognized. If the current player does have twenty-one, an end of play state  2726  is asserted. In another embodiment, the end of play state is not asserted when a player does have 21. If a player does not have twenty-one, an Nth card recognition state  2724  is asserted. Operations performed while in Nth card recognition state are similar to those performed while at second card hunt state  2714 , 2 nd  card present state  2716  and 2 nd  card recognition state  2718  in that a determination is made as to whether an additional card is received and then recognized. 
     Once play has ended for the current player at Nth card recognition state  2724 , then operation continues to end of play state  2726 . States  2704  through  2726  can be implemented for each player in a game. After the end of play state  2726  has been reached for every player in a game, state machine  2700  transitions to dealer up card detection state  2728 . 
       FIG. 32  illustrates an embodiment of a method  3200  for determining an end of play state for a return player. In one embodiment, the process of method  3200  can be performed during implementation of states  2722  through states  2726  of  FIG. 27 . First, a determination is made as to whether a player&#39;s score is over 21 at step  3210 . In one embodiment, this determination is made during an Nth card recognition state  2724  of  FIG. 27 . If a player&#39;s score is over 21, the operation continues to step  3270  where an end of play state is asserted for the current player. If the player&#39;s score is not over 21, the system determines whether the player&#39;s score is equal to 21 at step  3220 . This determination can be made at state  2722  of  FIG. 27 . If the player&#39;s score is equal to 21, then operation continues to step  3270 . If the player&#39;s hand value is not equal to 21, then the system determines whether a player has doubled down and taken a hit card at step  3120 . In one embodiment, the system determines whether a player has only been dealt two cards and an additional stack of chips is detected for that player. In on embodiment step  3220  is bypassed to allow a player with an ace and a rank  10  card to double down. 
     If a player has doubled down and taken a hit card at step  3230 , operation continues to step  3270 . If the player has not doubled down and received a hit card, a determination is made as to whether next player has received a card at step  3240 . If the next player has received a card, then operation continues to step  3270 . If the next player has not received a card, a determination is made at step  3250  as to whether the dealer has turned over a hole card. If the dealer has turned over a hole card at step  3250 , the operation continues to step  3270 . If the dealer has not turned over a hole card at step  3250 , then a determination is made that the end of play for the current player has not yet been reached at step  3260 . 
     In one embodiment, end of play state is asserted when either a card has been detected for next player, a split for the next player, or a dealer hole card is detected. In this state, the system recognizes that a card for the dealer has been turned up. Next, up card recognition state  2730  is asserted. At this state, the dealer&#39;s up card is recognized. 
     Returning to state machine  2700 , a determination is made as to whether the dealer up card is recognized to be an ace at state  2732 . If the up card is recognized to be an ace at state  2732 , then insurance state  2734  is asserted. The insurance state is discussed in more detail below. If the up card is not an ace, dealer hole card recognition state  2736  is asserted. 
     After insurance state  2734 , the dealer hole card state is asserted. After dealer hole card state  2736  has occurred, dealer hit card state  2738  is asserted. After a dealer plays out house rules, a payout state  2740  is asserted. Payout is discussed in more detail below. After payout  2740  is asserted, operation of the same machine continues to initialization state  2702 . 
       FIG. 33  illustrates an embodiment of a method  3300  from monitoring dealer events within a game. In one embodiment, steps  3380  through  3395  of method  3300  correspond to states  2732 ,  2734 , and  2736  of  FIG. 27 . A determination is made that a stable ROI for a dealer up card is detected at step  3310 . Next, the dealer up-card ROI difference image is calculated at step  3320 . In one embodiment, the dealer up-card ROI difference image is calculated as the difference between the empty reference image of the dealer up-card ROI and a current image of the dealer up-card ROI. Next, binarization and clustering are performed on the difference image at step  3330 . In one embodiment, erosion and dilation are performed prior to clustering. A determination is then made as to whether the clustered group derived from the clustering process is identified as a card at step  3340 . Card recognition is discussed in detail above. If the clustered group is not identified as a card at step  3340 , operation returns to step  3310 . If the clustered group is identified as a card, then operation continues to step  3360 . 
     In one embodiment, asserting a dealer up card state at step  3360  triggers a transition from state  2726  to state  2728  of  FIG. 27 . Next, a dealer card is then recognized at step  3370 . Recognizing the dealer card at step  3370  triggers the transition from state  2728  to state  2730  of  FIG. 27 . A determination is then made as to whether the dealer card is an ace at step  3380 . If the dealer card is detected to be an ace at step  3380 , operation continues to step  3390  where an insurance event process is initiated. If the dealer card is determined not to be an ace, dealer hole card recognition is initiated at step  3395 . 
       FIG. 34  illustrates an embodiment of a method  3400  for processing dealer cards. A determination is made that a stable ROI exists for a dealer hole card ROI at step  3410 . Next the hole card is detected at step  3415 . In one embodiment, identifying the hole card includes performing steps  3320 - 3350  of method  3300 . A hole card state is asserted at step  3420 . In one embodiment, asserting hole card state at step  3420  initiates a transition to state  2736  of  FIG. 27 . A hole card is then recognized at step  3425 . A determination is then made as to whether the dealer hand satisfies house rules at step  3430 . In one embodiment, a dealer hand satisfies house rules if the dealer cards add up to at least 17 or a hard 17. If the dealer hand does not satisfy house rules at step  3430 , operation continues to step  3435 . If the dealer hand does satisfy house rules, operation continues to step  3438  where the dealer hand play is complete. 
     A dealer hit card ROI is calculated at step  3435 . Next, the dealer hit card ROI is detected at step  3440 . A dealer hit card state is then asserted at step  3435 . A dealer hit card state assertion at step  3445  initiates a transition to state  2738  of  FIG. 27 . Next, the hit card is recognized at step  3450 . Operation of method  3400  then continues to step  3430 . 
       FIG. 35  illustrates an embodiment of a method  3500  for determining the assertion of a payout state. In one embodiment, method  3500  is performed while state  2738  is asserted. First, a payout ROI image is captured at step  3510 . Next, the payout ROI difference image is calculated at step  3520 . In one embodiment, the payout ROI difference image is generated as the difference between a running reference image and the current payout ROI image. In this case the running reference image is the image captured after the dealer hole card is detected and recognized at step  3425 . Binarization and clustering are then performed to the payout ROI difference image at step  3530 . Again, erosion and dilation may be optionally be implemented to remove “salt-n-pepper” noise. A determination is then made as to whether the clustered features of the difference image match those of a gaming chip at step  3540 . If the clustered features do not match at a chip template, operation continues to step  3570  where no payout is detected for that user. If the clustered features do match those of gaming chip, then a determination is made at step  3550  as to whether the centroid of the clustered group is within the payout wager region. If the centroid of the clustered group is not within a payout wager region, operation continues to step  3570 . If the centroid is within the wager region, a determination is made as to whether significant one value pixels exist outside the region of wager at step  3550 . If significant one value pixels exist outside the region of wager, operation continues to step  3570 . If significant one value pixels do not exist outside the region of wager, then operation continues to step  3560  where a new payout event is asserted. 
     The transition from payout state  2738  to init state  2702  occurs when cards in the active player&#39;s card ROI are detected to have been removed. This detection is performed by comparing the empty reference image to the current image of the active player&#39;s card ROI. 
     The state machine in  FIG. 27  illustrates the many states of the game monitoring system. A variation of the illustrated state may be implemented. In one embodiment, the state machine  2700  in  FIG. 27  can be separated into the dealer hand state machine and the player hand state machine. In another embodiment some states may be deleted from one or both state machines while additional states may be added to one or both state machines. This state machine can then be adapted to other types of game monitoring, including baccarat, craps, or roulette. The scope of the state machine is to keep track of game progression by detecting gaming events. Gaming events such as doubling down, split, payout, hitting, staying, taking insurance, surrendering, can be monitored and track game progression. These gaming events, as mentioned above, may be embedded into the first camera video stream and sent to DVR for recording. In another embodiment, these gaming events can trigger other processes of another table games management. 
     Data Analysis 
     Once the system of the present invention has collected data from a game, the data may be processed in a variety of ways. For example, data can be processed and presented to aid in game security, player and game operator progress and history, determine trends, maximize the integrity and draw of casino games, and a wide variety of other areas. 
     In one embodiment, data processing includes collecting data and analyzing data. The collected data includes, but is not limited to, game date, time, table number, shoe number, round number, seat number, cards dealt on a per hand basis, dealer&#39;s hole card, wager on a per hand basis, pay out on per hand basis, dealer ID or name, and chip tray balance on a per round basis. One embodiment of this data is shown in Table 6. Data processing may result in determining whether to “comp” certain players, attempt to determine whether a player is strategically reducing the game operator&#39;s take, whether a player and game operator are in collusion, or other determinations. 
     
       
         
           
               
             
               
                 TABLE 6 
               
             
            
               
                   
               
               
                 Data collected from image processing  
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 seat 
                 Cards 
                   
                   
                   
                 Dealer 
                 Tray 
               
               
                 Date 
                 Time 
                 table # 
                 Shoe# 
                 rd# 
                 # 
                 (hole) 
                 Wager 
                 Insurance 
                 Payout 
                 ID 
                 Balance 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 Oct. 10, 1913 
                 1:55:26 
                 pm 
                 1 
                 1 
                 1 
                 Dlr 
                 10-(6)-9 
                   
                   
                   
                 Xyz 
                 $2100 
               
               
                 Oct. 10, 2003 
                 1:55:26 
                 pm 
                 1 
                 1 
                 1 
                 2 
                 10-2-4 
                 $50 
                   
                 $50 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:55:26 
                 pm 
                 1 
                 1 
                 1 
                 5 
                 10-10 
                 $50 
                   
                 $50 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:55:26 
                 pm 
                 1 
                 1 
                 1 
                 7 
                 9-9 
                 $50 
                   
                 $50 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:755:27 
                 pm 
                 1 
                 1 
                 2 
                 Dlr 
                 10-(9) 
                   
                   
                   
                 Xyz 
                 $1950 
               
               
                 Oct. 10, 2003 
                 1:755:27 
                 pm 
                 1 
                 1 
                 2 
                 2 
                 10-10 
                 $50 
                   
                 $50 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:755:27 
                 pm 
                 1 
                 1 
                 2 
                 5 
                 10-6-7 
                 $50 
                   
                 ($50) 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:755:27 
                 pm 
                 1 
                 1 
                 2 
                 7 
                 A-10 
                 $50 
                   
                 $75 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:855:28 
                 pm 
                 1 
                 1 
                 3 
                 Dlr 
                 A-(10) 
                   
                   
                   
                 Xyz 
                 $1875 
               
               
                 Oct. 10, 2003 
                 1:855:28 
                 pm 
                 1 
                 1 
                 3 
                 2 
                 10-9 
                 $50 
                 $25 
                   0 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:855:28 
                 pm 
                 1 
                 1 
                 3 
                 5 
                 9-9 
                 $50 
                   
                 ($50) 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:855:28 
                 pm 
                 1 
                 1 
                 3 
                 7 
                 A-8 
                 $50 
                   
                 ($50) 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:955:29 
                 pm 
                 1 
                 1 
                 4 
                 D 
                 6-(5)-9 
                   
                   
                   
                 Xyz 
                 1975 
               
               
                 Oct. 10, 2003 
                 1:955:30 
                 pm 
                 1 
                 1 
                 4 
                 2 
                 A-5-2 
                 $50 
                   
                 ($50) 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 1:955:30 
                 pm 
                 1 
                 1 
                 4 
                 2 
                 10-5-10 
                 $50 
                   
                 ($50) 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 2:01:29 
                 pm 
                 1 
                 1 
                 5 
                 D 
                 5-(5)-9 
                   
                   
                   
                 Xyz 
                 1925 
               
               
                 Oct. 10, 2003 
                 2:01:30 
                 pm 
                 1 
                 1 
                 5 
                 2 
                 A-5-5 
                 $50 
                   
                  50 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 2:01:30 
                 pm 
                 1 
                 1 
                 5 
                 3 
                 10-5-10 
                 $50 
                   
                 ($50) 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 2:02:29 
                 pm 
                 1 
                 1 
                 6 
                 D 
                 9-(10) 
                   
                   
                   
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 2:02:30 
                 pm 
                 1 
                 1 
                 6 
                 2 
                 8-4-8 
                 $50 
                   
                  50 
                 Xyz 
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                   
                 split 
                   
                   
                 6 
                 2 
                 8-10 
                 $50 
                   
                  (50) 
                 Xyz 
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 Oct. 10, 2003 
                 2:02:30 
                 pm 
                 1 
                 1 
                 6 
                 3 
                 10-5-10 
                 $50 
                   
                 ($50) 
                 Xyz 
                   
               
               
                 Oct. 10, 2003 
                 2:03:29 
                 pm 
                 1 
                 1 
                 7 
                 D 
                 7-(3)-9 
                   
                   
                   
                 Xyz 
                 1825 
               
               
                 Oct. 10, 2003 
                 2:03:30 
                 pm 
                 1 
                 1 
                 7 
                 2 
                 8-2-10 
                 $150 
                   
                 150 
                 Xyz 
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                   
                 Split, 
                   
                   
                 7 
                 2 
                   
                 $150 
                   
                 150 
                 Xyz 
                   
               
               
                   
                 double 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                   
                 Split 
                   
                   
                   
                 2 
                 8-7-10 
                 $150 
                   
                 (150) 
                   
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 Oct. 10, 2003 
                 2:03:30 
                 pm 
                 1 
                 1 
                 7 
                 3 
                 10-5-10 
                 $50 
                   
                 ($50) 
                 Xyz 
                   
               
               
                   
               
            
           
         
       
     
     Table 6 includes information such as date and time of game, table from which the data was collected, the shoe from which cards were dealt, rounds of play, player seat number, cards by the dealer and players, wagers by the players, insurance placed by players, payouts to players, dealer identification information, and the tray balance. In one embodiment, the time column of subsequent hand(s) may be used to identify splits and/or double down. 
     The event and object recognition algorithm utilizes streaming videos from first camera and supplemental cameras to extract playing data as shown in Table 6. The data shown is for blackjack but the present invention can collect game data for baccarat, crabs, roulette, paigow, and other table games. Also, the chip tray balance will be extracted on a “per round” basis. 
     Casinos often determine that certain players should receive compensation, or “comps”, in the form of casino lodging so they will stay and gamble at their casino. One example of determing a “comp” is per the equation below:
 
Player Comp=average bet*hands/hour*hours played*house advantage*re-investment %.
 
     In one embodiment, a determination can be made regarding player comp using the data in Table 6. The actual theoretical house advantage can be determined rather than estimated. Theoretical house advantage is inversely related to theoretical skill level of a player. The theoretical skill level of a player will be determined from the player&#39;s decision based on undealt cards and the dealer&#39;s up card and the player&#39;s current hand. The total wager can be determined exactly instead of estimated as illustrated in Table 7. Thus, based on the information in Table 6, an appropriate compensation may be determined instantaneously for a particular player. 
     Casinos are also interested in knowing if a particular player is implementing a strategy to increase his or her odds of winning, such as counting cards in card game. Based on the data retrieved from Table 6, player ratings can be derived and presented for casino operators to make quick and informed decisions regarding a player. An example of player rating information is shown in Table 7. 
     
       
         
           
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                 Player Ratings 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 Theoretical 
                   
                   
                   
                   
               
               
                   
                   
                   
                 Total 
                 House 
                 Theoretical 
                 Actual 
                   
                   
               
               
                 Date 
                 Player 
                 Duration 
                 Wagered 
                 Advantage 
                 Win 
                 Win 
                 Comp 
                 Counting 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Jan. 1, 2003 
                 1101 
                 2 h 30 m 
                 $1000 
                 −2 
                 −200 
                 −1000 
                 0 
                 Probable 
               
               
                 Jan. 1, 2003 
                 1102 
                 2 h 30 m 
                 $1000 
                 1 
                 100 
                 500 
                 50 
                 No 
               
               
                   
               
            
           
         
       
     
     Other information that can be retrieved from the data of Table 6 includes whether or not a table needs to be filled or credited with chips or whether a winnings pick-up should be made, the performance of a particular dealer, and whether a particular player wins significantly more at a table with a particular dealer (suggesting player-dealer collusion). Table 8 illustrates data derived from Table 6 that can be used to determine the performance of a dealer. 
     
       
         
           
               
             
               
                 TABLE 8 
               
             
            
               
                   
               
               
                 Dealer Performance 
               
            
           
           
               
               
               
            
               
                   
                 Dealer 1101 
                 Dealer 1102 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Elapsed Time 
                 60 min 
                 60 min 
               
               
                   
                 Hands/Hr 
                 100 
                 250 
               
               
                   
                 Net 
                 −500 
                 500 
               
               
                   
                 Short 
                 100 
                 0 
               
               
                   
                 Errors 
                 5 
                 0 
               
               
                   
                   
               
            
           
         
       
     
     A player wager as a function of the running count can be shown for both recreational and advanced players in a game. An advanced user will be more likely than a recreational user to place higher wagers when the running count gets higher. Other scenarios that can be automatically detected include whether dealer dumping occurred (looking at dealer/player cards and wagered and reconciled chips over time), hole card play (looking a player&#39;s decision v. the dealer&#39;s hole card), and top betting (a difference between a players bet at the time of the first card and at the end of the round). 
     The present invention provides a system and method for monitoring players in a game, extracting player and game operator data, and processing the data. In one embodiment, the present invention captures the relevant actions and/or the results of relevant actions of one or more players and one or more game operators in game, such as a casino game. The system and methods are flexible in that they do not require special gaming pieces to collect data. Rather, the present invention is calibrated to the particular gaming pieces and environment already in used in the game. The data extracted can be processed and presented to aid in game security, player and game operator progress and history, determine trends, maximize the integrity and draw of casino games, and a wide variety of other areas. The data is generally retrieved through a series of cameras that capture images of game play from different angles. 
     The foregoing detailed description of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. The described embodiments were chosen in order to best explain the principles of the invention and its practical application to thereby enable others skilled in the art to best utilize the invention in various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims appended hereto.