AI Chip Recognition Learning for Accurate Gaming Table Bets
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Solution Overview
Problem
Existing systems struggle to accurately recognize and learn the patterns of chips piled up during gaming, particularly in baccarat, leading to errors in determining chip numbers and kinds.
Innovation Solution
A chip recognizing and learning system utilizing a camera to record chip states, an artificial intelligence device for image analysis, and a teaching device to input correction data for learning, enhancing accuracy through iterative learning processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an artificial intelligence device is used to analyze chip images, then chip recognition accuracy is improved, but the system complexity increases
Solution Approach 1:
The system employs self-learning mechanisms where the artificial intelligence device automatically analyzes determination results, identifies errors, and improves its own performance without external intervention. The teaching device feeds back correction data that enables the AI to learn from its mistakes and enhance recognition accuracy autonomously.
Solution Approach 2:
The system implements a feedback loop where determination results are reviewed, errors are identified, and correction data is fed back to the artificial intelligence device. This continuous feedback mechanism allows the system to learn from its performance and progressively improve chip recognition accuracy.
2Measurement precision
If iterative learning with teaching data is implemented, then chip determination accuracy is improved, but the time required for processing increases
Solution Approach 1:
The system performs preliminary learning using teaching data that contains correction information before actual chip determination tasks. By pre-training and pre-learning on labeled data, the artificial intelligence device builds its recognition capabilities in advance, reducing the time needed for real-time determination.
Solution Approach 2:
The learning process continues continuously as the system processes chip images, with the artificial intelligence device constantly improving its models based on feedback. This continuous learning ensures that the system maintains high accuracy while efficiently processing new data without requiring separate batch learning cycles.
Data Source
AI summary
A chip recognizing and learning system includes: a game recording device configured to record a state of chips piled up on a gaming table as an image by a camera; a chip determining device including an artificial intelligence device configured to analyze the recorded image of the state of the chips to determine the numbers and kinds of chips bet by a player; and a teaching device configured to input, in a case where it is determined that there is a doubt for an error in a determining result of the chip determining device, the image used for determination of the chip determining device and the correct numbers and correct kinds of chips for the error as teaching data to the artificial intelligence device to allow the artificial intelligence device to perform learning.