Storefront Device Merchandise Recognition via Positional Action Linking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information processing devices in storefronts face challenges in accurately recognizing merchandise that has been taken by a customer or returned to a shelf, leading to low merchandise recognition precision.
Innovation Solution
A storefront device equipped with first and second position information acquisition units, an action detection unit, and a person specifying unit, which acquire and analyze position information and movement actions to accurately identify the person performing the action on the merchandise, thereby improving recognition precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional information processing devices are used in storefronts, then device complexity is reduced, but merchandise recognition precision deteriorates
Solution Approach 1:
The system divides the recognition task into multiple specialized components: first position information acquisition unit for detecting person positions, second position information acquisition unit for detecting merchandise positions, action detection unit for identifying movement actions, and person specifying unit for linking actions to persons. This segmentation allows each component to focus on a specific aspect, improving overall recognition precision while managing complexity through functional decomposition.
Solution Approach 2:
The system introduces position information as an intermediary element that mediates between detecting person actions and identifying which person performed the action. By acquiring and analyzing position information from multiple sources, the system creates a bridge that enables accurate merchandise recognition without requiring direct complex interaction between all detection components.
2Measurement precision
If multiple position information acquisition units are deployed, then merchandise recognition precision is improved, but device complexity increases
Solution Approach 1:
The system segments the position information acquisition function into two specialized units: a first position information acquisition unit for detecting person positions and a second position information acquisition unit for detecting merchandise positions. This segmentation allows each unit to be optimized for its specific detection task, improving overall recognition precision while managing complexity through clear functional separation.
Solution Approach 2:
The position information acquisition units serve multiple functions within the system: they detect positions of both persons and merchandise, provide spatial data for action detection, and enable person specification through positional relationships. This multi-functionality improves recognition precision without proportionally increasing complexity, as the same basic detection mechanisms serve multiple purposes.
Data Source
AI summary
First position information indicating positions of approaching people nearing merchandise is acquired. Second position information indicating a position of a subject person who has stretched an arm out towards the merchandise among the approaching people, is detected. A movement action performed on the merchandise is detected. An ID of a person corresponding to the subject person who performed the detected movement action is specified based on a positional relationship between the first position information and the second position information.