Self-Service Checkout Fraud Detection via Visual Article Counting
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Solution Overview
Problem
Self-service checkout systems face challenges in detecting unfair actions, such as inadvertent mistakes or intentional cheating, without the practicality of installing weight sensors due to high implementation costs, especially in large or widespread stores.
Innovation Solution
An information processing program and apparatus that captures image data using a camera, processes it with a machine learning model trained for article identification and storage recognition, enabling the detection of unfair actions by counting articles and comparing scanned items with those interacted with by the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weight sensors are installed to detect unfair actions, then detection accuracy is improved, but implementation cost increases significantly
Solution Approach 1:
The patent replaces the mechanical weight sensing system with an optical imaging system combined with machine learning algorithms. The camera captures images of articles on the conveyor belt, and the machine learning model identifies and counts articles based on visual features, eliminating the need for expensive weight sensors while maintaining detection capability.
Solution Approach 2:
The patent creates a visual copy (image) of the physical article placement on the conveyor belt. By capturing and analyzing images of the articles, the system creates a digital representation that can be processed to detect unfair actions, replacing the need for direct physical measurement through weight sensors.
2Reliability
If machine learning models are used for article identification, then unfair action detection capability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning model with extensive article images before deployment. The model learns to recognize various article types, patterns, and characteristics in advance, enabling rapid identification during actual checkout operations without requiring complex real-time processing.
Solution Approach 2:
The patent optimizes processing speed by adjusting parameters such as image resolution, processing batch size, and model complexity. By finding the optimal balance between model accuracy and processing speed, the system achieves reliable unfair action detection while maintaining acceptable processing times for self-service checkout operations.
Data Source
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AI summary
An information processing apparatus (100) obtains image data in which a predetermined area in front of an accounting machine, which is used by a user to register an article and pay the bill, is captured. The information processing apparatus (100) inputs the image data in a machine learning model (104) that is trained to identify an article and a storage for the article, and obtains the output result. The information processing apparatus (100) refers to the article and the storage specified in the output result, and identifies the action taken by the user with respect to the article.