Transaction Image Auditing via Surveillance Extraction
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Solution Overview
Problem
Current point of sale systems lack effective integration of video surveillance and cloud computing technologies to enhance transaction reporting and inventory management, leading to inefficiencies in monitoring and auditing retail transactions.
Innovation Solution
A method and system that utilizes video surveillance equipment to record and filter transaction-related images, categorize them based on transaction characteristics, and create transaction data files for comparison with point of sale data, leveraging cloud computing for storage and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video surveillance equipment is used to record all transactions, then monitoring coverage is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system extracts only relevant transaction images from continuous video surveillance footage using image processing algorithms. This extraction process isolates key moments (transactions) from the bulk video data, reducing storage requirements while maintaining monitoring accuracy. The relevant images are then tagged and categorized for efficient retrieval and analysis.
Solution Approach 2:
Instead of storing entire video files, the system creates selective image copies of transaction moments. These copied images serve as sufficient evidence for auditing purposes without requiring the full video data, significantly reducing storage complexity while preserving the ability to verify transactions.
2Loss of information
If all video footage is stored and analyzed, then auditing completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary filtering and tagging of images as they are captured, organizing them by transaction characteristics before auditing begins. This pre-processing creates a structured dataset with metadata tags, enabling rapid retrieval and analysis during actual audits without requiring comprehensive review of all footage.
Solution Approach 2:
The system extracts and isolates only the transaction-relevant portions of video data, separating them from unrelated footage. This extraction creates a condensed dataset that maintains auditing completeness while dramatically reducing the time and computational resources needed for review.
3Loss of information
If image filtering and categorization are performed, then data relevance is improved, but processing complexity increases
Solution Approach 1:
The system replaces manual image review with automated image processing algorithms that filter and categorize transaction images based on visual characteristics. This substitution of mechanical/algorithmic processing for human analysis improves data relevance while the automation reduces the perceived complexity burden on operators.
Solution Approach 2:
The image processing system automatically tags and categorizes images based on their visual content and transaction characteristics without requiring manual intervention. This self-service capability filters relevant data while the standardized automated process manages processing complexity through consistent algorithmic application.
Data Source
AI summary
An image auditing method and system may be provided. Video surveillance equipment may be operated in coordination with at least one digital computer across network architecture. The video surveillance equipment may provide a computer with footage of a transactions occurring in a target area. The footage may be filtered into at least one image of a transaction. The image may be categorized based on at least one transaction characteristic. The transaction data may be compared to transaction records from a point of sale and analyzed for any discrepancies.