Store Shelf Imaging System for Accurate Product Location Data
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Solution Overview
Problem
Current methods for documenting product locations on store shelves are manual, time-consuming, and prone to inaccuracies due to variations in product packaging, orientation, and lighting conditions, making it difficult for retail chains to automatically collect and organize product location data across stores.
Innovation Solution
A store profile generation system using a mobile base equipped with high and low resolution image capture devices and a master control unit to automatically identify product locations and generate a spatial layout of store shelves, including barcode recognition and image-based accuracy corrections to ensure accurate product placement and signage ordering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to document product locations on store shelves, then flexibility in handling various product packaging and orientations is maintained, but the process becomes time-consuming and prone to inaccuracies
Solution Approach 1:
The patent replaces manual mechanical documentation methods with an automated imaging system that uses cameras to capture product locations. The system uses image capture devices mounted on mobile bases to photograph shelves, and computer vision algorithms to automatically identify and locate products, replacing the manual process of physically measuring and recording product positions.
Solution Approach 2:
The patent creates digital copies of product locations through imaging. Instead of manually recording physical positions, the system captures images of shelves and uses these image copies to extract product location information. The imaging system creates a digital representation of the physical shelf state, which can then be processed automatically to determine product locations.
2Productivity
If automated imaging systems are used to collect product location data, then data collection speed is improved, but the system becomes complex and difficult to implement across diverse store layouts
Solution Approach 1:
The patent designs the imaging system to be universal and adaptable to different store layouts. The mobile bases can navigate various aisle configurations, and the image processing algorithms are designed to handle diverse product packaging, orientations, and shelf arrangements. This multi-functionality allows the same system to work across different retail environments without requiring custom implementation for each store.
Solution Approach 2:
The patent employs dynamic image processing that can adapt to varying conditions. The system uses multiple images taken from different positions and angles, and dynamically adjusts processing parameters based on the captured data. The image stitching and product identification algorithms are designed to handle dynamic variations in lighting, product orientation, and shelf configuration.
3Measurement precision
If multiple high-resolution images are captured to ensure accurate product identification, then measurement precision is improved, but the amount of data to be processed increases significantly
Solution Approach 1:
The patent extracts only the essential information from the captured images. Instead of processing and storing all image data, the system uses image processing algorithms to identify and extract specific features such as product barcodes, labels, and spatial positions. This extraction process converts large volumes of raw image data into compact, structured product location data that is sufficient for the intended application.
Solution Approach 2:
The patent segments the image processing task into distinct stages. The system first captures multiple images, then segments them by identifying individual products within the images. Each product is processed separately to extract its location and identification information. This segmentation approach allows the system to handle large data volumes by breaking down the processing into manageable, independent tasks.
Data Source
AI summary
A store profile generation system includes a mobile base and an image capture assembly mounted on the base. The assembly includes at least one image capture device for acquiring images of product display units in a retail environment. A control unit acquires the images captured by the at least one image capture device at a sequence of locations of the mobile base in the retail environment. The control unit extracts product-related data from the acquired images and generates a store profile indicating locations of products and their associated tags throughout the retail environment, based on the extracted product-related data. The store profile can be used for generating new product labels for a sale in an appropriate order for a person to match to the appropriate locations in a single pass through the store.