Gaming Table Chip Monitoring via Image Analysis
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Solution Overview
Problem
Casinos face challenges in accurately monitoring betting activities at gaming tables, particularly in distinguishing between different types of chips and tracking bets in real-time, due to varying chip designs and chaotic gaming environments, which can lead to unfair advantages and revenue losses.
Innovation Solution
A system comprising an imaging component and processor that captures images of the gaming table, filters background data, identifies chip points of interest using histogram descriptors, and classifies chips based on their arrangement and identity, enabling efficient data transmission and storage of bet monitoring information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is captured and transmitted for bet monitoring, then monitoring accuracy is improved, but data transmission volume and processing time increase
Solution Approach 1:
The system extracts only the essential information from captured images - specifically chip locations, quantities, and denominations - and transmits this structured data instead of the entire image. This extraction principle reduces data transmission volume while preserving monitoring accuracy, as only relevant betting information needs to be communicated to the monitoring system.
Solution Approach 2:
The image processing is segmented into distinct stages: capture, processing, and analysis. The imaging component captures images, the processor handles image processing to identify chips, and the system transmits only the extracted betting data. This segmentation allows each component to specialize, improving overall efficiency while reducing the data burden on transmission and storage systems.
2Measurement precision
If detailed chip classification is performed, then chip identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The system applies different processing approaches to different aspects of chip identification. It uses color analysis to determine chip denomination, shape recognition to identify chip type, and position detection to locate chips on the table. By applying specialized local analysis methods to specific chip characteristics rather than uniform complex processing, the system achieves high identification accuracy while managing processing complexity.
Solution Approach 2:
The system changes parameters such as color thresholds, shape tolerance ranges, and classification criteria to accurately distinguish between different chip types and denominations. By adjusting these parameters based on the specific imaging conditions and chip characteristics, the system maintains high identification accuracy without requiring overly complex processing algorithms.
3Speed
If real-time monitoring is implemented, then security response time is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by capturing images at specific trigger moments - when chips are placed on the table or when betting actions occur. Rather than continuously processing all images, the system triggers capture and processing only when relevant events happen, enabling real-time monitoring of betting activities while significantly reducing computational resource consumption compared to continuous full-frame processing.
Solution Approach 2:
The system extracts only the critical betting information from captured images - chip locations, quantities, and values - and transmits this condensed data for further processing. This extraction approach enables real-time monitoring response by focusing computational resources on identifying and transmitting only the essential betting data rather than processing and transmitting complete image sequences, thereby reducing overall computational resource consumption.
Data Source
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Figure 2
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 filter out the background, establish a two dimensional grid of points of interests and corresponding histograms for classifying the one or more chips through identifying a dominant classification of each row in the grid of points of interests.