Automated Planogram Construction from Shelf Images
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Solution Overview
Problem
Current methods for checking the placement of products in retail shops are manual and costly, making it inefficient to ensure the correct positioning of products on shelves across multiple locations.
Innovation Solution
An automated method for constructing a planogram using image recognition techniques, integrating specific features and artificial learning to detect and classify products, including context-based object recognition, probabilistic filtering, and co-occurrence maps to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checking methods are used to verify product placement, then measurement precision can be maintained, but productivity decreases and costs increase
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system that captures shelf images and automatically detects product placements. The system uses computer vision algorithms to identify products, their positions, and orientations, eliminating the need for human employees to physically check each shelf while maintaining verification accuracy.
Solution Approach 2:
The system creates digital copies (images) of the physical shelf arrangements and analyzes these copies to verify product placement. Instead of manually inspecting each shelf, the system captures visual representations and processes them computationally to detect products, their positions, and orientations, thereby improving efficiency while maintaining precision.
2Device complexity
If standard image recognition algorithms are used for product detection, then device complexity is reduced, but measurement precision deteriorates due to incomplete and erroneous results
Solution Approach 1:
The patent applies different image processing techniques to different regions and aspects of the shelf images. It uses specific algorithms for detecting products, shelves, and their spatial relationships, with tailored processing for each element type. This localized approach allows standard algorithms to be applied where appropriate while using specialized processing where needed, maintaining simplicity while improving precision.
Solution Approach 2:
The system divides the image analysis task into separate modules: product detection, shelf detection, position determination, and orientation calculation. Each module handles a specific aspect of the problem independently, allowing the use of standard, well-tested algorithms for each segment while achieving overall high precision through the coordinated operation of these segmented functions.
Data Source
AI summary
A method for automatically constructing a planogram from photographs of shelving, replacing laborious manual construction includes the following steps: a step (1) in which the images are received, a step (2) in which the images are assembled, a step (3) in which the structure is automatically constructed, a step (4) in which the products are automatically detected, and a step (5) in which the products are positioned in the structure. The product detection step (4) enhances traditional image recognition techniques, using artificial learning techniques to incorporate characteristics specific to the planograms. This product detection step (4) also includes at least two successive classification steps, namely: an initialization step (41) with detection of product categories; and a classification step (42) with the classification of the products themselves, each of these steps including a first image recognition step, followed by a statistical filtering step based on the characteristics specific to the planograms.