Casino Table Vision Monitoring for Accurate Wager Detection
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Solution Overview
Problem
Existing systems for monitoring table games in casinos are expensive, inaccurate, and ineffective due to the fast-paced and visually noisy operating environment, making it difficult to track player behavior and game events accurately.
Innovation Solution
A machine learning-driven system using high-resolution cameras and neural networks, specifically convolutional neural networks, to identify and analyze game objects and events on a gaming table, including playing cards and wager objects, by employing region proposal and detection networks for precise object recognition and value estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional surveillance systems and sensors are used to monitor table games, then installation and maintenance costs are reduced, but measurement precision and reliability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical sensors embedded in chips with a computer vision system using cameras and machine learning algorithms. The system captures images of the gaming table and uses neural networks to automatically detect and track game objects, players, and events, eliminating the need for complex physical sensor installations while achieving high measurement precision.
Solution Approach 2:
The patent creates a visual copy (image) of the gaming table state and processes this digital representation using machine learning models. Instead of directly sensing physical objects, the system captures optical information and uses computational algorithms to interpret game objects, events, and player positions, achieving accurate monitoring without direct physical contact with gaming elements.
2Loss of information
If random sampling by casino floor operators is used, then device complexity is reduced, but loss of information about activity and betting levels occurs
Solution Approach 1:
The system performs automated self-monitoring of the entire gaming table environment continuously. The camera system captures all visible activity, and the machine learning algorithms automatically process this data to identify game objects, track player behavior, and detect betting levels without requiring manual intervention or sampling by operators, ensuring complete information capture.
Solution Approach 2:
The patent implements continuous monitoring of the gaming table through uninterrupted image capture and real-time processing. The system operates continuously to track all game events, player movements, and betting activities without gaps, ensuring that no information is lost due to random sampling or intermittent observation.
3Measurement precision
If sensors in casino chips are used, then measurement precision for tracking wager objects is improved, but device complexity and installation costs increase
Solution Approach 1:
The patent replaces physical sensors embedded within casino chips with an external optical detection system. Instead of using electronic sensors inside chips to transmit signals, the system uses cameras to capture images of chips on the table and machine learning algorithms to identify and track them, achieving the same tracking function with simpler overall system architecture.
Solution Approach 2:
The patent introduces an intermediary (the camera image) between the physical chip and the detection system. Instead of direct electronic communication from chip sensors, the system uses visual information as an intermediary to identify and track wager objects, simplifying the physical sensor requirements while maintaining tracking precision through computational analysis of the intermediary data.
Data Source
Figure 1
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Figure 4A
AI summary
Embodiments relate to systems and methods for gaming monitoring. In particular, embodiments relate to systems and methods for gaming monitoring based on machine learning processes configured to analyse captured images to identify or detect game objects and game events to monitor games.