Image Processing for Retail Planogram Compliance Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Retailers face challenges in ensuring item placement compliance with planograms due to misplacement of items, which leads to increased costs, inaccurate inventory counts, and customer frustration, as existing methods lack efficient automation for verifying correct placement and inventory levels.
Innovation Solution
The implementation of image processing techniques and trained machine learning processes to determine item placement compliance by capturing images of retail locations, segmenting relevant portions, matching them with stored templates, and generating alerts for misplacement, thereby reducing labor costs and improving inventory accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to verify item placement compliance, then labor costs and time consumption increase, but automation and efficiency remain low
Solution Approach 1:
The patent replaces manual mechanical inspection methods with an automated image processing system that uses cameras to capture shelf images, processes them through machine learning models to identify items and their locations, and automatically compares them against planogram requirements. This substitution of mechanical human labor with an automated optical and computational system directly resolves the contradiction by dramatically improving verification efficiency while eliminating time loss associated with manual methods
Solution Approach 2:
The system enables self-service by allowing the image processing system to automatically perform verification, identification, and compliance checking without human intervention. The machine learning model autonomously processes images, identifies items, determines their locations, and generates compliance reports, making the system self-sufficient and eliminating the need for manual labor in the verification process
2Measurement precision
If comprehensive image processing is applied to entire images, then accuracy improves, but processing power and time requirements increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the retail shelf environment into distinct regions of interest, such as individual shelves, product categories, or specific item locations. The image processing system focuses analysis on these segmented portions rather than processing entire images, which maintains identification accuracy for relevant items while significantly reducing computational power and time requirements by eliminating processing of unnecessary areas
Solution Approach 2:
The system implements local quality by applying different processing intensities to different regions of the image based on their importance. High-accuracy processing is applied to regions containing target items that require precise identification, while lower-processing regions receive minimal analysis. This selective approach maintains measurement precision where needed while reducing overall energy consumption
Data Source
AI summary
This application relates to automated processes for determining item placement compliance within retail locations. For example, a computing device may obtain an image of a fixture within a store. The image may be captured by a camera with a field of view directed at the fixture. The computing device may apply a segmentation process to the image to determine a portion of the image. Further, the computing device may determine a correlation between the portion of the image and each of a plurality of item image templates. Each item image template may include an image of an item the retail location sells in the retail location. The computing device may determine, based on the correlations, one of the plurality of item image templates and its corresponding item. The computing device may then determine whether the item should be located at the fixture based on a planogram.