Gaming Activity Tracking With Dynamic Multi-Camera Calibration
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Solution Overview
Problem
Existing methods for tracking gaming activity rely heavily on human review of video data, which is prone to errors and inefficiencies due to obstructions, changing illumination, and limited computing and networking resources, making it difficult to accurately detect gaming infractions and maintain fair play.
Innovation Solution
A system of multiple video cameras operating in a common coordinate and color system, using machine learning to track gaming activities and objects, with dynamic calibration and redundancy to overcome obstructions and environmental changes, and incorporating infrared imaging for additional insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple video cameras are used to track gaming activities, then measurement precision and reliability improve, but device complexity and cost increase
Solution Approach 1:
The system divides the gaming table into multiple zones (betting area, dealer area, player areas) and assigns specific cameras to monitor particular zones. This segmentation allows the system to achieve high detection precision for specific gaming actions while managing overall system complexity through modular camera placement and dedicated monitoring functions.
Solution Approach 2:
Multiple video cameras are merged into a unified monitoring system with a central processor that integrates data from all camera sources. This combining approach improves overall detection accuracy and provides redundant verification for gaming infractions, while the centralized processing architecture manages complexity by consolidating analysis functions.
2Productivity
If automated video analysis is implemented to detect infractions, then productivity and reliability improve, but device complexity and computing resources increase
Solution Approach 1:
The system performs preliminary processing of video data by extracting key features (object detection, motion tracking, zone identification) at the camera or edge device level before transmitting processed information to the central system. This preliminary action reduces the computing burden on central servers while maintaining high productivity in infraction detection through efficient data preparation.
Solution Approach 2:
Manual review of gaming activities by employees is replaced with automated computer vision algorithms that analyze video feeds in real-time. This substitution dramatically improves productivity in detecting infractions such as chip dumping, card marking, or dealer errors, while the use of specialized machine learning models optimizes computing resource utilization.
3Measurement precision
If cameras are positioned to cover all gaming surfaces, then measurement precision improves, but obstructions from players and dealers increase
Solution Approach 1:
The system transitions from single-plane camera positioning to multi-dimensional camera placement, including overhead cameras mounted on table structures, wall-mounted cameras at various heights and angles, and potentially even 360-degree camera arrangements. This dimensional approach allows cameras to capture gaming activities from multiple perspectives simultaneously, maintaining measurement precision while minimizing obstructions from players and dealers who occupy the table surface plane.
4Measurement precision
If high-resolution video data is processed in real-time, then measurement precision improves, but bandwidth requirements and processing time increase
Solution Approach 1:
The system extracts only the essential and relevant information from high-resolution video data for real-time analysis, such as detecting specific gestures (hit, stand, double down), tracking chip movements in betting zones, and identifying card handling actions. Non-essential visual details are excluded from real-time processing, maintaining detection accuracy for gaming infractions while significantly reducing bandwidth requirements and processing time. Full-resolution data is retained for later review if needed.
Data Source
AI summary
A system for monitoring gaining activities associated with a gaming surface, the system including a gaming equipment having the gaining surface and a display system connected to the gaming equipment, such as an improved limit sign. The system also includes device camera connected on the display system having a first field of view tracking, for example, betting markers, gaining tokens, a gaming participant and, the gaming surface. The system can interoperate with other camera systems to establish a common coordinate space for coordinated image processing and machine learning based on model representations of a spatial space using the common coordinate space. The calibration can occur dynamically to automatically adjust for camera reconfigurations.