Interior Video Analytics Using Cross-Context Inference Fusion
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Solution Overview
Problem
Current video-feed-based systems in retail operations operate separately and fail to perform comprehensive real-time analyses that leverage various types of information gleaned from collected video data, missing opportunities to enhance customer experience, optimize storage structure usage, and maximize profits.
Innovation Solution
A system and method that integrates machine learning and computer vision to generate real-time interior analytics by combining video feeds from multiple contexts, including storage structure and customer interaction, to provide comprehensive insights such as employee assignments, loss prevention alerts, and predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple video feed-based systems are deployed to monitor different aspects (storage structure, customer interaction, loss prevention), then comprehensive data collection is achieved, but the systems operate separately and fail to perform integrated real-time analyses
Solution Approach 1:
The patent merges multiple separate video feed-based systems (storage structure monitoring, customer interaction analysis, loss prevention) into a single integrated analytics platform. The system combines video feeds from multiple cameras, integrates various inference engines, and consolidates data processing into one unified system that performs comprehensive real-time analyses across all monitoring aspects simultaneously.
Solution Approach 2:
The analytics platform is designed with multi-functionality to handle diverse monitoring tasks through a single system. It simultaneously performs storage structure inference, customer interaction analysis, employee behavior monitoring, and loss prevention detection using shared hardware resources (video capture devices, processing units) and integrated software modules, eliminating the need for separate dedicated systems for each function.
2Productivity
If separate video-feed-based systems are used for different monitoring contexts, then system simplicity is maintained, but opportunities to enhance customer experience, optimize storage structure usage, and maximize profits are missed
Solution Approach 1:
The system combines multiple video feeds from different contexts (storage structure cameras, customer interaction cameras, loss prevention cameras) into a unified analytics platform that processes all data streams simultaneously. This integration enables cross-contextual analyses such as correlating customer behavior with product availability and storage status, generating comprehensive business insights that separate systems cannot provide.
Solution Approach 2:
The analytics platform implements feedback mechanisms where insights from integrated analysis are used to trigger real-time actions. For example, when the system detects low storage capacity combined with high customer interest in a product category, it can automatically generate restocking alerts or adjust inventory allocation. This closed-loop feedback enables the system to actively optimize business operations based on synthesized data from all monitoring contexts.
Data Source
AI summary
A system may be configured to generate real-time analytics using machine learning and computer vision. In some aspects, the system may receive a storage structure video frame and an interaction video frame, determine a storage structure inference based on the storage structure video frame, and determine an interaction inference based on the interaction video frame. Further, the system may determine that the storage structure inference and the interaction inference correspond to a common time period and common location, and generate analytics information based on the storage structure inference and the interaction inference.