Retail Video Behavior Scoring for Automated Shoplifting Detection
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Solution Overview
Problem
Retail environments face significant costs and inefficiencies in monitoring and preventing shoplifting due to the limitations of existing security measures, including the high cost of personnel, the ineffectiveness of electronic article surveillance devices, and the difficulty in monitoring large areas with video surveillance.
Innovation Solution
An automated system for detecting suspect behaviors using non-intrusive, automated behavioral modeling that tracks individuals in a retail environment, assigns penalty points to suspicious activities, and triggers alerts when a behavior score exceeds a threshold, utilizing video sensors and machine learning algorithms to identify and track potential shoplifting actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security staff are employed to monitor and prevent shoplifting, then loss prevention effectiveness is improved, but operational costs increase significantly
Solution Approach 1:
The patent replaces the mechanical system of human security staff with an automated computer vision system that uses video sensors, machine learning models, and behavioral analysis algorithms to detect and respond to shoplifting activities, thereby reducing operational costs while maintaining or improving loss prevention effectiveness
Solution Approach 2:
The system enables self-service loss prevention by automatically monitoring, analyzing, and responding to suspicious behaviors without human intervention, with the automated system serving itself to detect threats, generate alerts, and provide evidence for criminal proceedings
2Measurement precision
If electronic article surveillance devices are deployed, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the detection functionality from complex electronic article surveillance devices and integrates it into a streamlined computer vision system that processes video data directly, eliminating the need for additional electronic tags and complex detection hardware while maintaining high detection capability
Solution Approach 2:
The video sensor system serves multiple functions including customer monitoring, shoplifting detection, evidence collection, and behavioral analysis, replacing the need for specialized electronic surveillance devices with a universal visual monitoring system
3Area of stationary object
If video surveillance cameras are used to monitor large retail areas, then coverage area is improved, but monitoring difficulty and personnel requirements increase
Solution Approach 1:
The patent replaces the manual monitoring mechanism with an automated machine learning system that processes video footage from multiple cameras simultaneously, using behavioral modeling and pattern recognition to identify suspicious activities without requiring human operators to review extensive video data
Solution Approach 2:
The system introduces an intermediary layer of automated analysis between the video cameras and security personnel, where machine learning models pre-process and filter video data to highlight only potentially suspicious behaviors, making the monitoring task manageable and reducing personnel requirements
Data Source
AI summary
A method and apparatus for the identification of suspect behaviour in a retail environment, the method comprising: detecting a person in a frame of said stream of video data; extracting a set of activities of the identified person from the stream of video data; assigning a numeric value to each extracted activity in the set of extracted activities, said numeric value representative of a threat level of the activity; accumulating said numeric values to provide a behaviour score; and identifying a behaviour as being suspect when the behaviour score reaches a target threshold value associated with the behaviour.