Synthetic Image Training for Casino Chip Detection
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Solution Overview
Problem
Current methods for tracking gaming habits and security in casinos are labor-intensive, prone to errors, and lack comprehensive coverage due to reliance on manual observation, making them inefficient and costly.
Innovation Solution
A system utilizing neural networks trained with synthetically-generated images to automate the detection and classification of casino chips and other objects on a gaming table, allowing for real-time monitoring and improved accuracy through virtual simulation and computer vision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual observation and tracking methods are used, then implementation complexity is low, but productivity and coverage are limited
Solution Approach 1:
The patent replaces manual mechanical observation and tracking systems with an automated computer vision system using neural networks. The system uses cameras to capture images of gaming tables and automatically processes them through trained neural networks to detect chips, cards, and player actions, eliminating the need for manual tracking while significantly improving productivity and coverage.
Solution Approach 2:
The patent creates synthetic training images that replicate real casino table scenes with chips, cards, and lighting conditions. These synthetic copies are used to train neural networks, allowing the system to learn patterns from virtual representations rather than requiring extensive manual annotation of real game scenarios, thereby reducing the complexity of data collection and training.
2Measurement precision
If manual tracking methods are used, then device complexity is low, but measurement precision and accuracy are insufficient
Solution Approach 1:
The patent replaces imprecise manual tracking with automated computer vision technology. Neural networks process images captured by cameras to accurately detect and classify chips, cards, and player actions, providing superior measurement precision and detection accuracy compared to human observers while managing system complexity through automated processing.
Solution Approach 2:
The patent transforms the training process by changing from manual annotation to synthetic image generation. Synthetic images with known ground truth labels are created to train neural networks, enabling the system to achieve high detection accuracy through learned patterns from controlled virtual environments rather than relying on human annotators.
3Reliability
If comprehensive monitoring is implemented, then security and tracking coverage improve, but labor requirements and cost increase
Solution Approach 1:
The patent replaces labor-intensive manual security monitoring with automated neural network-based detection. The system uses cameras and trained neural networks to continuously monitor gaming tables for cheating, card counting, and other security issues, providing comprehensive coverage without requiring additional human security personnel, thereby improving reliability while maintaining labor efficiency.
4Measurement precision
If real-world training data is collected, then model accuracy improves, but time and labor for training increase
Solution Approach 1:
The patent performs preliminary action by generating synthetic training images before deploying the system to real casinos. These pre-generated synthetic images with known labels are used to train neural networks in advance, eliminating the need to collect and annotate real-world data during deployment and significantly reducing training time while maintaining model accuracy.
Solution Approach 2:
The patent creates synthetic copies of real casino table scenes to train neural networks. These virtual replicas include chips, cards, lighting conditions, and camera angles that mirror real environments, allowing the system to learn accurate detection patterns without requiring extensive time-consuming collection and annotation of actual gaming scenarios.
Data Source
AI summary
Disclosed are an example system and/or method to simulate a virtual scene with a virtual gaming table and a virtual object. The virtual gaming table and the virtual object are modeled within the virtual scene based on known information. In some instances, the known information comprises information associated with at least one of a physical gaming table corresponding to the virtual gaming table or a physical object corresponding to the virtual object. The example system and/or method further extract image data associated with an image of the virtual object rendered relative to the virtual gaming table within the virtual scene based on the known information. The image data is extracted in response to automated analysis of the image of the virtual object. The method and/or system further train a machine learning model using the extracted image data.