Classifications

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Definitions

• the at least one processor is further configured to determine a presence of one or more players in the series of images of the gaming area.
• Figure 9 is another image of an example gaming environment illustrating part of a method of gaming monitoring according to some embodiments.
• Figure 10 is another image of an example gaming environment illustrating part of a method of gaming monitoring according to some embodiments;
• Figure 11 is a block diagram of a gaming monitoring system according to some embodiments;
• a camera capturing image data at a resolution of 1080p at 30 frames per second may generate image data at a rate of 2.5mbps (megabits per second), for example.
• each gaming environment may be provided with two cameras, and the total image data may be generated at a rate of 5 gbps, for example.
• the image data may also comprise data from a camera capturing images in an image spectrum not visible to the naked eye (infrared spectrum for example).
• the image data may also comprise data from a depth sensor or a depth camera or a 3D camera capturing depth or 3D scene information of a gaming environment.
• the additional sources of image data may further increase the volume and velocity of image data generated from surveillance of gaming environments.
• the method of gaming monitoring may comprise: receiving at an upstream computing device from a computing device image data of images captured in the gaming environment and corresponding timestamp information.
• the upstream computing device may be configured to process the received image data to detect a game object and an image region corresponding to the detected game object.
• the upstream computing device may be configured to process the image region corresponding to the identified game object to determine a game object attribute.
• various modules implemented in the memory 124 may incorporate one or more variants of convolutional neural networks (CNNs), a class of deep neural networks to perform the various image processing operations for gaming monitoring.
• CNNs comprise various hidden layers of neurons between an input layer and an output layer to that convolve an input to produce the output through the various hidden layers of neurons.
• the pose estimation module 125 comprises executable program code to process one or more images of players in a gaming environment to identify postures of the one or more players.
• Each identified posture may comprise a location of a region in an image corresponding to a specific body part of a player.
• the identified body parts may comprise left or right hands, left or right wrists, a left or right distal- hand periphery in an image, or a face.
• the pose estimation module 125 may incorporate a pose analysis or estimation framework for pose estimation.
• the framework of the pose estimation module 125 may map pixels in a 2D image corresponding to a human to a 3D model of a surface of a human body.
• the framework of the pose estimation module 125 may identify pixels in an image corresponding to a human and map each pixel to a 3D surface model of a human body.
• the framework of the pose estimation module 125 may enable the identification of a more complete representation of the posture of persons in an image.
• the pose estimation module processes the first image to determine a pose or posture of one or more players present in the first image.
• the determined pose may include one or more key points associated with various specific body parts of the players.
• the key points may include a key point associated with a distal hand periphery, including a left hand or right-hand periphery or both.
• Pose estimation may also include the estimation of a distinct skeletal model of the players. Identification of the distinct skeletal model may enable the association of a face region in the first image with a hand region of the same player.
• Figure 7 also illustrates a bounding box 718 identified by the person detection neural network 159 of the object detection module 123 around the face of the player 750.
• the coordinates of the bounding box 718 may be determined by processing an image to extrapolate facial key points (key points corresponding to one or more of eyes, nose, mouth, ears of a person) determined by the key point estimation neural network 152.
• the identification of the skeletal model 715 allows the association of the distal hand key point 712 corresponding to the distal hand periphery of the player 750 with the bounding box 718 around the face of the player, thereby allowing the association of the one or more game objects in the bounding box 710 with the face of the player 750 in the bounding box 718.
• the camera system 1104 may comprise two or more cameras to sufficiently cover various angles around the gaming table 1102 to adequately capture images of gaming activity of patrons and their faces.
• the camera system 1104 may comprise at least one panoramic camera configured to capture images at a capture angle of 180 degrees or greater than 180 degrees.
• Some embodiments may incorporate a MobotixTM S16 DualFlex camera or a JabraTM PanaCast camera, for example.
• Each camera of the camera system 110 may continuously capture images at a resolution of 6144 x 2048 pixels or 3840 x 2160 pixels or 2592x1944 pixels or 2048x1536 pixels or 1920x1080 pixels or 1280x960, for example.
• These listed image resolution examples are non-limiting and other imaging resolutions may be incorporated by the embodiments to perform gaming activity monitoring.
• the on-premise gaming monitoring server 1110 may comprise a face orientation determination module 1289.
• the face orientation determination module 1289 comprises program code to process image data relating to images of faces captured by the camera system 1104 and detected by the object detection module 123 of the edge gaming monitoring computing device 1106.
• the face orientation determination module 1289 determines orientation information of a face in image data corresponding to a face.
• this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these.
• This disclosure contemplates mass storage 1706 taking any suitable physical form.
• Storage 1706 may include one or more storage control units facilitating communication between processor 1702 and storage 1706, where appropriate.
• storage 1706 may include one or more storages 1706.
• I/O interface 1708 may include one or more device or software drivers enabling processor 1702 to drive one or more of these I/O devices.
• I/O interface 1708 may include one or more I/O interfaces 1708, where appropriate.

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• Image Analysis (AREA)
• Closed-Circuit Television Systems (AREA)
• Pinball Game Machines (AREA)
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• 2021
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