# US 12,217,537 B2 · Gaming activity monitoring systems and methods

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- Number: US 12,217,537 B2
- Document ID: US12217537B2
- Status: Granted
- Kind: B2
- Application number: 18/003,625
- Filed: 2021-06-10
- Published: 2023-07-27
- Granted: 2025-02-04
- Earliest claimed: 2020-06-30
- Assignees: Angel Group Co., Ltd.
- Applicants: Angel Group Co., Ltd.
- Inventors: Louis Quinn; Duc Dinh Minh Vo; Nhat Vo; Subhash Challa
- Web page: https://patentgrep.com/patent/US12217537B2

## CPC

- G06V 40/161: {Detection; Localisation; Normalisation}
- G06T 7/10: Segmentation; Edge detection (motion-based segmentation G06T7/215)
- G06T 7/73: using feature-based methods
- G06T 7/75: {involving models}
- G06V 10/82: using neural networks
- G06V 20/42: {of sport video content}
- G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69)
- G06V 20/60: Type of objects
- G06V 40/103: {Static body considered as a whole, e.g. static pedestrian or occupant recognition}
- G06V 40/171: {Local features and components; Facial parts (eye characteristics G06V40/18); Occluding parts, e.g. glasses; Geometrical relationships}
- G07F 17/3206: {Player sensing means, e.g. presence detection, biometrics}
- G07F 17/3223: {Architectural aspects of a gaming system, e.g. internal configuration, leader-follower, wireless communication}
- G07F 17/3234: {about the performance of a gaming system, e.g. revenue, diagnosis of the gaming system}
- G07F 17/3237: {about the players, e.g. profiling, responsible gaming, strategy/behavior of players, location of players}
- G07F 17/3239: {Tracking of individual players}
- G07F 17/3241: {Security aspects of a gaming system, e.g. detecting cheating, device integrity, surveillance (computer security G06F21/00)}
- H04N 7/183: {for receiving images from a single remote source}
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30201: Face
- G06T 2207/30232: Surveillance
- G06V 2201/10: Recognition assisted with metadata
- G07F 17/322: {Casino tables, e.g. tables having integrated screens, chip detection means}
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G07F: COIN-FREED OR LIKE APPARATUS (coin sorting G07D3/00; coin testing G07D5/00; {handling coins or paper currencies apart from payment activated apparatus G07D; payment architectures, schemes or protocols G06Q20/00})
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## Abstract

Embodiments relate to systems, methods and computer readable media for gaming monitoring. In particular, embodiments process images to determine presence of a gaming object on a gaming table in the images. Embodiments estimate postures of one or more players in the images and based on the estimated postures determine a target player associated with the gaming object among the one or more players.

## Claims

### Claim 1

A system for monitoring gaming activity in a gaming area comprising a gaming table, the system comprising:
at least one camera configured to capture images of the gaming area;
at least one processor configured to communicate with the at least one camera and a memory;
the memory storing instructions executable by the at least one processor to configure the at least one processor to:
determine presence of a first gaming object on the gaming table in a first image from a series of images of the gaming area captured by the at least one camera, the series of images comprising one or more images,
responsive to determining the presence of the first gaming object in the first image, process the first image to estimate postures of one or more players in the first image, and
based on the estimated postures, determine a first target player associated with the first gaming object among the one or more players.

### Claim 2 (depends on 1)

The system of claim 1, wherein the instructions are executable to configure the at least one processor to determine in the series of images an image region of a face of the first target player associated with the first gaming object.

### Claim 3 (depends on 1)

The system of claim 1, wherein processing the first image to estimate postures of one or more players in the first image comprises identifying one or more periphery indicator regions in the first image, each periphery indicator region corresponding to a distal hand periphery of one or more players.

### Claim 4 (depends on 3)

The system of claim 3, wherein each of the one or more periphery indicator regions corresponds to a distal left hand periphery or a distal right hand periphery.

### Claim 5 (depends on 1)

The system of claim 1, wherein the instructions are executable to configure the at least one processor to determine the first target player associated with the first gaming object by:
estimating a distance of each periphery indicator region from the first gaming object in the first image;
identifying a closest periphery indicator region based on the estimated distance; and
determining the first target player associated with the first gaming object based on the identified closest periphery indicator region.

### Claim 6 (depends on 5)

The system of claim 5, wherein processing the first image to estimate postures of one or more players in the first image comprises:
estimating a skeletal model of one or more players, and
determining the first target player associated with the first gaming object based on the estimated skeletal model of one or more players.

### Claim 7 (depends on 6)

The system of claim 6, wherein estimating the skeletal model of one or more players comprises estimating key points in the first image associated with one or more of: wrists, elbows, shoulders, neck, nose, eyes or ears of the one or more players.

### Claim 8 (depends on 1)

The system of claim 1, wherein the instructions are executable to further configure the at least one processor:
determine presence of a second gaming object on the gaming table in a second image from the series of images;
responsive to determining the presence of the second gaming object in the second image, process the second image to estimate postures of players in the second image;
based on the estimated postures, determine a second target player associated with the second gaming object among the players.

### Claim 9 (depends on 1)

The system of claim 1, wherein the first gaming object comprises any one of: a game object, a token, a currency note or a coin.

### Claim 10 (depends on 1)

The system of claim 1, wherein the memory comprises one or more posture estimation machine learning models trained to estimate postures of one or more players in the series of images.

### Claim 11 (depends on 10)

The system of claim 10, wherein the one or more posture estimation machine learning models comprise one or more deep learning artificial neural networks trained to estimate postures of one or more players in the captured images.

### Claim 12 (depends on 2)

The system of claim 2, wherein identifying a face of the first target player further comprises determining a vector representation of the image region of the face of the first target player using a face recognition machine learning model stored in the memory.

### Claim 13 (depends on 1)

The system of claim 1, wherein the instructions are executable to further configure the at least one processor to estimate a game object value associated with the first gaming object.

### Claim 14 (depends on 1)

The system of claim 1, wherein determining a presence of a first gaming object on the gaming table further comprises determining a position on the gaming table zone of the first gaming object.

### Claim 15 (depends on 1)

The system of claim 1, wherein the at least one processor is further configured to execute instructions stored in the memory to identify in the series of images a plurality of face regions corresponding to a face of the first target player associated with the first gaming object.

### Claim 16 (depends on 15)

The system of claim 15, wherein the instructions are executable to further configure the at least one processor to process the plurality of face regions to determine face orientation information of the first target player's face in each of the plurality of face regions.

### Claim 17 (depends on 16)

The system of claim 16, wherein the instructions are executable to further configure the at least one processor to process the face orientation information of the first target player's face in each of the plurality of face regions to determine a most head-on face region corresponding to the target player.