Shelf Image Tracking for Product Mis-Stock Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current stock tracking methods struggle to accurately and efficiently monitor the placement and orientation of products on store shelves, leading to inefficiencies in restocking and inventory management.
Innovation Solution
A method utilizing a mobile robotic system to capture images of store shelves, combined with computer vision techniques and a database of template images, to detect and verify the presence, position, and orientation of products, and generate restocking prompts based on deviations from a planogram.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual stock tracking methods are used, then operational simplicity is maintained, but measurement precision and productivity deteriorate
Solution Approach 1:
The patent replaces manual mechanical inspection methods with an automated mobile robotic system equipped with cameras and computer vision algorithms. The robotic system autonomously navigates store aisles, captures images of product shelves, and uses image processing to detect product placement, orientation, and stock levels, thereby eliminating manual tracking while achieving high measurement precision.
Solution Approach 2:
The patent creates digital copies (images) of physical product shelves and uses computer vision algorithms to analyze these copies. Template matching techniques compare captured images against reference templates to identify product positions and orientations, enabling accurate tracking without direct physical measurement while maintaining system simplicity through software-based solutions.
2Productivity
If automated image-based tracking is implemented, then measurement precision and productivity improve, but device complexity and energy consumption increase
Solution Approach 1:
The patent pre-processes and stores template images of products in a database before actual tracking operations. During runtime, the system retrieves relevant templates and performs rapid matching against captured shelf images, significantly reducing processing time compared to real-time template generation or exhaustive search methods.
Solution Approach 2:
The patent focuses image processing resources on specific regions of interest (shelf areas containing products) rather than processing entire images uniformly. The system identifies and analyzes only relevant portions of captured images where products are located, reducing overall processing time while maintaining detection accuracy for critical stock tracking areas.
Data Source
AI summary
One variation of a method for tracking placement of products in a store includes: accessing an image recorded by a mobile robotic system within a store; detecting a shelf in a region of the image; based on an address of the shelf, retrieving a list of products assigned to the shelf by a planogram of the store; retrieving a set of template images—from a database of template images—defining visual features of products specified in the list of products; extracting a set of features from the region of the image; determining that a unit of the product is mis-stocked on the shelf in response to deviation between the set of features and features in a template image, in the set of template images, representing the product; and in response to determining that the unit of the product is mis-stocked on the shelf, generating a restocking prompt for the product.