Self-Checkout Video Analysis for Theft Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current self-checkout systems in stores face challenges in preventing product loss due to intentional abnormal customer behavior, such as scanning only one of multiple products or failing to scan items altogether, which requires human intervention and resource allocation.
Innovation Solution
A video analysis-based self-checkout apparatus equipped with cameras, a product identifier, and a customer behavior determinator using an artificial neural network to differentiate between normal and abnormal behaviors, including facial expression and heart rate analysis, to generate control signals and store video evidence for fraud detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If self-checkout devices are introduced to reduce operational resources, then labor costs and operational efficiency are improved, but product loss and theft increase due to lack of human oversight
Solution Approach 1:
The patent replaces manual monitoring (mechanical human oversight) with an automated video analysis system using cameras and AI algorithms. The system captures video footage, detects customer behaviors, and automatically identifies abnormal patterns such as failing to scan products or carrying unscanned items, thereby substituting human labor with an automated detection mechanism that prevents product loss while maintaining operational efficiency
Solution Approach 2:
The patent introduces video analysis technology as an intermediary between the customer and the checkout system. The video capture unit and behavior detection algorithm act as intermediaries that monitor customer actions without requiring direct human intervention, enabling automatic detection of abnormal behaviors and facilitating timely alerts to store personnel when theft or loss is suspected
2Loss of substance
If manual monitoring is increased to prevent product loss, then product loss decreases, but operational costs and resource requirements increase
Solution Approach 1:
The patent enables the checkout system to monitor and detect abnormal behaviors autonomously without requiring continuous human oversight. The video analysis system automatically captures footage, processes it through AI algorithms, and generates alerts when abnormal patterns are detected, allowing the system to serve itself in the monitoring function and eliminating the need for additional operational resources
Solution Approach 2:
The patent replaces manual monitoring activities with an automated video analysis system. Instead of deploying additional staff to monitor customers, the system uses cameras and AI algorithms to automatically detect and report abnormal behaviors, thereby preventing product loss without increasing operational resource requirements
3Loss of substance
If video analysis with AI is implemented to detect abnormal behavior, then product loss prevention improves, but device complexity and implementation cost increase
Solution Approach 1:
The patent divides the video analysis system into distinct functional modules: video capture units positioned at strategic locations, behavior detection algorithms that analyze specific patterns, and alert generation systems that notify personnel. This segmentation allows each component to perform its function independently and simplifies the overall system architecture, making implementation more manageable despite the advanced capabilities
Solution Approach 2:
The patent designs the video analysis system to perform multiple functions: capturing video footage, detecting various types of abnormal behaviors (failing to scan, carrying unscanned items), generating alerts, and providing evidence for investigations. This multi-functionality consolidates what would otherwise require multiple separate systems into a single integrated solution, reducing overall complexity
Data Source
AI summary
The present disclosure relates to a self-checkout apparatus. The self-checkout apparatus comprises: a product recognition table which is provided with a product identification zone and on which a product to be identified is located; a first camera which is arranged so that a photographing direction is toward the product identification zone and which obtains a video of the product in the product identification zone by photographing the product identification zone; a second camera which obtains a video of a surveillance area by photographing the surveillance area including the product recognition table and the inside of a store; and a product identifier which detects an identification code assigned to the product from the video of the product obtained by the first camera and interprets the detected identification code to output an identification result of the product captured by the first camera.