Video Metadata Extraction for Fraudulent POS Return Detection
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Solution Overview
Problem
Retail stores face challenges in detecting fraudulent customer returns at point-of-sale (POS) activities due to the impracticality of continuous human supervision of video feeds, as existing intelligent video monitoring systems require significant CPU resources and are inefficient in analyzing large amounts of video data in real-time.
Innovation Solution
A video monitoring system that captures and analyzes metadata from video streams to detect suspicious activities, such as fraudulent POS returns, by generating metadata about object positions, sizes, and movements, allowing for efficient analysis of transactions without the need for continuous real-time video analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time video analysis is performed to detect fraudulent POS returns, then fraud detection capability is improved, but CPU resource consumption increases significantly
Solution Approach 1:
The patent extracts only the essential information from video data by generating metadata that describes events and objects without analyzing the complete video stream. This selective extraction of relevant information reduces CPU resource consumption while maintaining fraud detection capability.
Solution Approach 2:
The system performs preliminary action by generating metadata about video content in advance, before fraud detection analysis is needed. This pre-processing step creates a condensed representation of video data that can be analyzed efficiently without requiring intensive real-time video processing.
2Reliability
If continuous human supervision of video feeds is implemented, then fraud detection accuracy is improved, but operational complexity and costs increase
Solution Approach 1:
The system implements self-service by automatically generating and analyzing metadata to detect fraudulent activities without requiring continuous human supervision. The automated metadata analysis system performs fraud detection independently, reducing operational complexity while maintaining detection accuracy.
3Measurement precision
If complete video data is analyzed for fraud detection, then detection thoroughness is improved, but data processing time and bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential information from video data by generating metadata that describes events and objects without analyzing the complete video stream. This selective extraction reduces data processing time and bandwidth requirements while maintaining detection thoroughness through targeted metadata analysis.
4Speed
If real-time video monitoring is implemented, then fraud detection responsiveness is improved, but system resource consumption increases
Solution Approach 1:
The system changes parameters by transforming video data into metadata with different characteristics - smaller size, structured format, and event-oriented content. This parameter transformation enables efficient processing that maintains responsiveness while reducing system resource consumption.
Data Source
AI summary
Video data from sources, such as cameras, is analyzed to create metadata descriptive of the events and objects occurring in the video. This metadata, which consists of data about the video, is then analyzed on a transaction basis to determine if a suspicious activity, such as a fraudulent Point of Sale (POS) return event, has occurred in relation to a transaction.