Store Shelf Imaging System for Automated 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 continuous motion store profile generation system using a mobile base with image capture devices and a control unit that captures images of product display units, extracts product-related data, and generates a spatial layout of product locations, enabling automatic packaging of signage in the correct order for stores.
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 processing algorithms to automatically identify and record product positions, replacing the need for manual observation and recording by store employees.
Solution Approach 2:
The system enables self-service documentation by having the imaging system automatically capture, process, and record product location data without human intervention. The image processing algorithms autonomously identify products and their positions, eliminating the need for manual data entry and processing.
2Productivity
If automated imaging systems are used to capture product locations, then data collection speed increases, but the system becomes sensitive to variations in product packaging, orientation, and lighting conditions
Solution Approach 1:
The system adjusts imaging parameters such as lighting conditions, exposure time, and camera angles to optimize image quality under varying store conditions. The image processing algorithms adapt to different lighting scenarios and product orientations by modifying processing parameters dynamically.
Solution Approach 2:
The system incorporates feedback mechanisms where image processing results are continuously evaluated and used to adjust subsequent imaging and processing parameters. If image quality is insufficient due to lighting or orientation variations, the system can trigger re-imaging or adjust processing algorithms to compensate.
3Loss of information
If multiple image capture devices are used to cover entire product display units, then complete product location data is obtained, but the system complexity and data processing requirements increase
Solution Approach 1:
The system divides the product display units into multiple zones or sections, each captured by dedicated image capture devices. This segmentation allows comprehensive coverage of the entire display while organizing data processing into manageable segments, reducing overall system complexity.
Solution Approach 2:
The image capture devices are designed to perform multiple functions: capturing product images, determining their own locations using sensors, and providing spatial information. This multi-functionality reduces the need for separate systems and simplifies the overall architecture.
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.