Gaming Table Chip Imaging for Accurate Bet Classification
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Solution Overview
Problem
Existing systems struggle to accurately and efficiently monitor betting activities at gaming tables, particularly in diverse casino environments with varying chip designs and lighting conditions, leading to challenges in tracking chip quantities, types, and arrangements, which can result in unfair advantages and financial losses for casinos.
Innovation Solution
A device is configured to capture images of gaming table surfaces, process chip data through histogram analysis and geometric characteristics, and transmit compressed data locally for efficient monitoring, using multiple channels and machine-learning algorithms to classify and quantify chips, even in chaotic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual monitoring methods are used to track betting activities, then operational flexibility is maintained, but accuracy and reliability of chip tracking deteriorate due to human error
Solution Approach 1:
The patent replaces manual visual inspection and physical chip handling with an automated optical imaging system. Cameras capture images of chips on the gaming table, and image processing algorithms automatically identify, classify, and track chip positions, values, and movements. This substitution eliminates human error in chip tracking while maintaining system flexibility through programmable monitoring rules.
Solution Approach 2:
The system creates digital copies (images) of physical chips and their arrangements on the gaming table. These image copies are then processed to extract chip information without physically handling the actual chips. This copying approach enables accurate tracking while preserving the original chip positions and preventing interference with the gaming process.
2Loss of information
If comprehensive image processing is performed on all captured images, then complete betting activity data is obtained, but data transmission time and processing overhead increase
Solution Approach 1:
The system extracts only the essential betting activity information from captured images, such as chip positions, values, and movements, rather than transmitting or processing entire images. Image processing algorithms identify and extract relevant features (chip boundaries, colors, patterns) and convert them into compact data representations. This extraction reduces data volume significantly while preserving all necessary betting activity information.
Solution Approach 2:
The image processing pipeline is segmented into distinct stages: image capture, pre-processing (noise reduction, enhancement), feature extraction (chip identification, classification), and data compilation. Each segment processes only necessary information for its specific function, avoiding redundant computation. This segmentation enables efficient processing while maintaining complete betting activity tracking.
3Measurement precision
If multiple image channels and processing steps are used to classify chips accurately, then chip classification precision improves, but device complexity increases
Solution Approach 1:
The system employs adaptive image processing that dynamically adjusts processing intensity and methods based on detected conditions. For example, if chips are clearly visible and well-lit, minimal processing is applied. If lighting is poor or chips are partially obscured, the system automatically enhances images and applies more sophisticated processing algorithms. This dynamic approach maintains high classification accuracy while reducing unnecessary processing complexity in favorable conditions.
Solution Approach 2:
The system changes processing parameters such as image resolution, color space transformations, and histogram analysis depth based on the specific monitoring scenario and chip characteristics. Different chip types (different values, colors, patterns) trigger different processing parameter sets. This parameter adaptation enables accurate classification of diverse chips while avoiding the need for a single overly complex processing pipeline that would handle all possible scenarios with maximum complexity.
Data Source
AI summary
A platform, device and process for capturing images of the surface of a gaming table and determining the quantity, identity, and arrangement of chips bet at a gaming table. Image data is captured corresponding to the one or more chips positioned in at least one betting area on a gaming surface of the respective gaming table and the data is processed to use a machine learning based approach to classify the chips using a combination of a depth information channel and at least one of a red channel, the green channel, and the blue channel.