POS Scan Avoidance Detection Using Dwell-Time Anomalies
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Solution Overview
Problem
Current POS systems are prone to sweethearting, where collusion between retail store personnel and customers results in items being checked out without scanning, leading to unpaid losses due to post-transaction detection methods.
Innovation Solution
A real-time scan avoidance prevention system using computer vision and machine learning to detect item entry, track motion parameters, determine dwell-time anomalies, and output notifications for suspicious activities, thereby preventing scan avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If post-transaction video monitoring is used to detect sweethearting, then detection capability is provided, but loss prevention is ineffective because detection occurs after the item has already been checked out
Solution Approach 1:
The system performs preliminary detection by analyzing video footage during the transaction process itself, rather than after completion. The processor continuously monitors the scanning area and detects items that enter without being scanned, enabling real-time intervention before the transaction is finalized and loss occurs.
Solution Approach 2:
The system implements feedback by providing real-time alerts to store personnel when scan avoidance is detected. The processor generates notifications based on detected anomalies in item movement patterns, enabling immediate corrective action during the transaction to prevent loss.
2Reliability
If real-time video analysis is implemented to detect scan avoidance, then loss prevention effectiveness is improved, but system complexity increases
Solution Approach 1:
The system uses video footage as an intermediary between the physical scanning process and the detection algorithm. By analyzing existing video feeds rather than directly interfacing with scanner hardware, the system achieves accurate detection while maintaining relative simplicity in system integration and deployment.
3Measurement precision
If continuous monitoring of all items is performed, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on detecting specific anomalies in item movement patterns rather than analyzing every single item transaction. By identifying unusual dwell times or movement characteristics that indicate scan avoidance, the system achieves effective detection with reduced processing requirements.
Data Source
AI summary
Example aspects include a method, apparatus and computer-readable medium of determining losses at a point of sale (POS) device, comprising receiving, by a processor from an imaging device, a video feed of a scanning area. The aspects further include detecting, by the processor, an entry of an item into the scanning area. Additionally, the aspects further include identifying, by the processor, one or more motion parameters of the item. Additionally, the aspects further include determining, by the processor, a dwell-time for the item based at least on the one or more motion parameters. Additionally, the aspects further include identifying, by the processor, a scan time anomaly for the item. Additionally, the aspects further include outputting a notification, by the processor, indicating a suspicious activity for the item, wherein the notification indicating the suspicious activity is based on the scan time anomaly.