Shelf Object Status Detection via Image Registration
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Solution Overview
Problem
In retail environments, manual detection of product status issues such as restocking and misplacement on shelves is labor-intensive and error-prone due to the fluid nature of inventory and variable imaging conditions like lighting, which complicates accurate machine-generated status detection.
Innovation Solution
A method and apparatus for object status detection using a mobile automation system equipped with image and depth sensors to capture shelf data, register images to a common frame of reference, identify gaps, and compare with reference data to generate status notifications for out-of-stock, low stock, or misplaced products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual detection methods are used for product status issues, then labor intensity is high and errors are frequent, but the system is simple and flexible
Solution Approach 1:
The patent replaces manual mechanical detection with an automated imaging system that uses cameras and image processing algorithms to detect product status issues, eliminating human labor while improving detection reliability
Solution Approach 2:
The system performs self-detection of product status by automatically capturing images, processing them to identify gaps and misplacements, and generating status notifications without requiring human intervention
2Extent of automation
If imaging systems are used for status detection, then detection automation is improved, but accuracy is reduced due to variable lighting and fluid inventory
Solution Approach 1:
The system captures multiple images at different times and registers them to a common frame of reference before analysis, preparing the data in advance to handle variable lighting and inventory changes
Solution Approach 2:
The patent dynamically adjusts the detection process by capturing images at multiple time points and using temporal information to distinguish between permanent status issues and temporary variations caused by changing inventory conditions
3Measurement precision
If multiple images are captured and registered to common frame of reference, then detection accuracy is improved, but processing time and complexity increase
Solution Approach 1:
The patent segments the shelf into multiple regions and identifies gaps between products within each region, then consolidates these segmented results to determine overall product status, improving accuracy while managing processing complexity
Data Source
AI summary
A method of object status detection for objects supported by a shelf, from shelf image data, includes: obtaining a plurality of images of a shelf, each image including an indication of a gap on the shelf between the objects; registering the images to a common frame of reference; identifying a subset of the gaps having overlapping locations in the common frame of reference; generating a consolidated gap indication from the subset; obtaining reference data including (i) identifiers for the objects and (ii) prescribed locations for the objects within the common frame of reference; based on a comparison of the consolidated gap indication with the reference data, selecting a target object identifier from the reference data; and generating and presenting a status notification for the target product identifier.