Gaming Table Bet Recognition for Accurate Chip Detection
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Solution Overview
Problem
Existing systems struggle to accurately and efficiently track betting activities at gaming tables, particularly due to variations in chip designs, lighting conditions, and obstructions, leading to inaccuracies and increased management overhead.
Innovation Solution
A system comprising client hardware devices with imaging components and sensors that capture and preprocess image data from gaming tables, a game monitoring server for data aggregation, and a front-end interface for real-time data display, utilizing machine-vision techniques and adaptable feature recognition to identify chip values and betting patterns, even under diverse conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems are used to track betting activities, then system simplicity is maintained, but measurement precision and reliability deteriorate due to variations in chip designs, lighting conditions, and obstructions
Solution Approach 1:
The system divides the monitoring task into multiple specialized components: imaging components for capturing chip images, sensors for detecting chip positions and weights, lighting components for illumination, and processing systems for data analysis. Each component focuses on a specific aspect of chip detection, improving overall measurement precision while distributing system complexity across modular units.
Solution Approach 2:
The monitoring system is designed to handle multiple types of chips with varying designs, lighting conditions, and table configurations using a unified multi-functional platform. The system can adapt to different chip types, table layouts, and environmental conditions through configurable parameters and machine learning algorithms, maintaining versatility without requiring separate specialized systems for each scenario.
2Reliability
If multiple sensors and imaging components are deployed to capture comprehensive betting data, then measurement precision and reliability improve, but device complexity and management overhead increase
Solution Approach 1:
The system combines multiple sensing modalities (imaging, weight detection, positional sensing) into an integrated monitoring platform that processes data from all sources simultaneously. By merging these components into a unified system with centralized processing, the architecture reduces management overhead while maintaining the reliability benefits of multi-sensor deployment.
Solution Approach 2:
The system implements a hierarchical structure where individual chip-level detection capabilities are nested within table-level monitoring, which is in turn nested within casino-floor-wide surveillance. This nested architecture allows comprehensive data collection at multiple levels while managing complexity through layered processing and aggregation.
3Productivity
If real-time processing of image data is implemented to provide immediate betting information, then productivity and responsiveness improve, but use of energy and computational resources increase
Solution Approach 1:
The system performs preliminary processing of image data at the source, including initial chip detection, basic image filtering, and feature extraction, before transmitting processed results to central systems. This preliminary action reduces the computational burden on central processors and enables faster real-time responses while minimizing energy consumption at centralized data centers.
Solution Approach 2:
The monitoring system uses periodic triggering based on game events (deal, bet placement, card reveal) rather than continuous processing. Image capture and analysis are activated at specific moments in the game cycle, reducing overall computational energy consumption while maintaining real-time responsiveness when betting activities occur.
Data Source
AI summary
System, processes and devices for monitoring betting activities using bet recognition devices and a server. Each bet recognition device has an imaging component for capturing image data for a gaming table surface. The bet recognition device receives calibration data for calibrating the bet recognition device. A server processor coupled to a data store processes the image data received from the bet recognition devices over the network to detect, for each betting area, a number of chips and a final bet value for the chips.