Three-Branch Action Recognition Model for Retail Shrinkage
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Solution Overview
Problem
Traditional retail loss prevention methods, such as human surveillance and electronic article surveillance, are costly and ineffective due to limited human attention span and vulnerability to shrinkage in self-checkout and cashier-less systems, necessitating an automated solution for real-time action recognition in retail environments.
Innovation Solution
A three-branch architecture machine learning model incorporating knowledge distillation for action recognition, which integrates actor and scene knowledge through a Cross Branch Integration module and Action Knowledge Graph, enabling accurate identification of actions like shoplifting and automated responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human surveillance is used to monitor video footage for shoplifting, then detection capability is improved, but labor cost and operational complexity increase significantly
Solution Approach 1:
The surveillance system performs self-service by automatically detecting and analyzing shoplifting behaviors through AI algorithms. The system independently processes video footage, identifies suspicious actions, and generates alerts without requiring continuous human monitoring, thereby maintaining high detection capability while eliminating the need for manual surveillance operations
Solution Approach 2:
The patent replaces the mechanical human monitoring system with an automated computer vision system. Instead of human operators watching multiple screens, the system uses deep learning models and action recognition algorithms to automatically analyze video feeds, substituting human perceptual and cognitive functions with computational processes that maintain detection accuracy while reducing operational complexity
2Measurement precision
If multiple security personnel are deployed to monitor different monitors simultaneously, then detection coverage is improved, but human attention limitations reduce effectiveness
Solution Approach 1:
The AI-based surveillance system performs multiple detection functions simultaneously through a single integrated platform. The system can analyze multiple video feeds, detect various types of suspicious behaviors (shoplifting, theft, unusual activities), and generate comprehensive security coverage without being limited by human attention spans or the need for multiple personnel
Solution Approach 2:
The automated system provides continuous uninterrupted monitoring of all video feeds without the breaks, distractions, or fatigue that affect human operators. The system maintains constant detection coverage across all monitors simultaneously, ensuring no suspicious activities are missed due to human attention limitations
3Productivity
If automated action recognition is implemented, then labor cost is reduced, but system complexity increases
Solution Approach 1:
The surveillance system is segmented into modular functional components: video input modules, action recognition modules, alert generation modules, and integration interfaces with existing security infrastructure. This segmentation allows the complex automated system to be deployed incrementally and integrated with existing systems, reducing the perceived complexity while maintaining labor efficiency benefits
Solution Approach 2:
The system acts as an intermediary between video surveillance data and security personnel responses. It processes and interprets video content, then presents processed information (alerts, notifications, analyzed data) to security staff, simplifying their task from active monitoring to response execution. This intermediary role justifies the system complexity by demonstrating tangible labor efficiency improvements
Data Source
AI summary
This disclosure includes technologies for action recognition in general. The disclosed system may automatically detect various types of actions in a video, including reportable actions that cause shrinkage in a practical application for loss prevention in the retail industry. Further, appropriate responses may be invoked if a reportable action is recognized. In some embodiments, a three-branch architecture may be used in a machine learning model for action and/or activity recognition. The three-branch architecture may include a main branch for action recognition, an auxiliary branch for learning/identifying an actor (e.g., human parsing) related to an action, and an auxiliary branch for learning/identifying a scene related to an action. In this three-branch architecture, the knowledge of the actor and the scene may be integrated in two different levels for action and/or activity recognition.