Casino Chip Fraud Detection Through Multi-Angle Imaging and RFID
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing casino fraud detection systems face challenges in accurately determining chip amounts due to blind spots and chip overlap, and are unable to detect sophisticated fraud methods like card slanting and 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 positions, types, and amounts, and compare them against win/lose results to detect fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surveillance cameras are used to monitor chip movement, then fraud detection capability is improved, but measurement precision deteriorates due to blind spots and chip overlap
Solution Approach 1:
The system segments the monitoring task by using multiple cameras positioned at different angles to capture chip images from various perspectives. This segmentation allows the system to overcome blind spots and accurately determine chip amounts even when chips overlap, as each camera provides a different view that complements the others.
Solution Approach 2:
The system introduces an intermediary RFID tag attached to each chip that stores identification information. This intermediary enables automatic recognition of chip amounts without relying solely on visual inspection, thereby improving measurement precision while maintaining fraud detection capability through the combination of RFID data and image analysis.
2Productivity
If RFID tags are attached to chips for automatic recognition, then chip amount recognition speed is improved, but device complexity increases
Solution Approach 1:
The system merges RFID technology with existing surveillance camera infrastructure. The RFID tags are integrated into standard casino chips, and the RFID reading function is combined with the image processing system, allowing simultaneous acquisition of chip identification data and visual verification without requiring entirely separate systems.
Solution Approach 2:
The RFID tags automatically provide chip identification information when chips are placed on the gaming table, eliminating the need for manual counting or complex visual analysis. The system self-recognizes chip amounts through automatic RFID reading, improving productivity while keeping the added complexity minimal.
3Reliability
If image analysis is performed on recorded game progress, then fraud detection accuracy is improved, but loss of time increases due to processing requirements
Solution Approach 1:
The system performs preliminary actions by capturing and pre-processing images continuously during the game, rather than analyzing images only after the game ends. This allows the system to have pre-processed image data ready for rapid fraud detection analysis, reducing the time loss associated with post-game processing.
Solution Approach 2:
The system replaces complex mechanical image analysis with AI-based automated analysis that can quickly process images and identify fraudulent patterns. This substitution significantly reduces processing time while maintaining or improving fraud detection accuracy through intelligent pattern recognition.
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.