Casino Chip Fraud Detection Through Multi-Camera Image Analysis
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Solution Overview
Problem
Existing casino fraud detection systems face challenges in accurately determining chip amounts due to blind spots and overlapping chips, and cannot detect sophisticated fraud methods like card squeegee or dealer-player conspiracies.
Innovation Solution
A fraud detection system using a game recording apparatus, image analyzing apparatus, and control device with artificial intelligence and deep learning capabilities to analyze chip and card movements, determine win/lose results, and compare actual vs. expected chip amounts to detect fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surveillance cameras are used to monitor chip movements, then fraud detection capability is improved, but blind spots and overlapping chips cause measurement precision to deteriorate
Solution Approach 1:
The system segments the monitoring task by using multiple cameras positioned at different locations (above the table, at player positions, and at the dealer position) to capture chip movements from various angles, thereby eliminating blind spots and improving measurement precision while maintaining fraud detection capability
Solution Approach 2:
The system introduces an intermediary image analysis apparatus that processes images from multiple cameras, uses template matching to identify chips, and calculates chip amounts by analyzing overlapping regions, thereby resolving the measurement precision problem while preserving the fraud detection capability provided by surveillance cameras
2Device complexity
If traditional image analysis is used to count chips, then system complexity is reduced, but the ability to detect sophisticated fraud methods like card squeegee deteriorates
Solution Approach 1:
The system implements feedback by continuously comparing actual chip movements captured by cameras against expected chip movements calculated from game results, and by using image analysis feedback to identify anomalies such as card squeegee fraud, thereby enhancing detection capability without excessive complexity
Solution Approach 2:
The system achieves multi-functionality by using the same image capture and analysis infrastructure to detect multiple types of fraud including chip tampering, card squeegee, and dealer-player conspiracies, thereby improving reliability without proportionally increasing device complexity
3Measurement precision
If multiple cameras are deployed to eliminate blind spots, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system segments the imaging task across multiple cameras positioned strategically (above the table, at player positions, and at the dealer position), allowing each camera to focus on specific areas and reducing the total number of cameras needed while eliminating blind spots and improving measurement precision
Solution Approach 2:
The system uses template matching to create digital copies of chip images, allowing the image analysis apparatus to identify and count chips by comparing captured images against templates, thereby improving measurement precision without requiring excessive computational complexity
Data Source
AI summary
A fraud detection system which detects fraud in a game of performing collection and redemption of chips in accordance with a win or lose result includes a camera which captures an image of chips contained in a chip tray of a dealer, an image analyzing apparatus which analyses the image captured by the camera to detect an amount of the chips contained in the chip tray, a card distribution device which determines a win or lose result of a game, and a control device which compares the win or lose result of the game and the amount of the chips contained in the chip tray before and after collection and redemption of the chips to detect fraud.