Planogram Generation Using Computer Vision and Transaction Data
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Solution Overview
Problem
In retail environments, accurate item identification and planogram data generation are challenging due to limited availability, suspect accuracy, and absence of data, especially in frictionless stores and asset protection scenarios.
Innovation Solution
The system uses overhead cameras with computer vision to analyze gestures and combine transaction data with image data from cameras and weight sensors to determine the probability of item locations, approximating planogram data by tracking customer interactions and correlating shelf locations with purchased items over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional planogram data collection methods are used, then data availability is limited and accuracy is suspect, but implementing overhead camera systems with computer vision increases device complexity and cost
Solution Approach 1:
The system uses computer vision algorithms to automatically analyze camera images and identify item locations without human intervention. The machine learning models self-train on collected data to improve accuracy over time, eliminating the need for manual planogram data collection and processing while maintaining high measurement precision.
Solution Approach 2:
The patent replaces traditional manual or mechanical data collection methods with optical sensing (cameras) and computational processing (computer vision). This substitution transforms physical observation into digital analysis, enabling automated item location identification with higher accuracy while the system complexity is managed through software-based solutions.
2Loss of information
If manual planogram data collection is performed, then data availability is limited, but automating data collection through cameras and sensors increases system complexity
Solution Approach 1:
The overhead camera system serves multiple functions: it captures images for item location identification, tracks customer gestures, monitors product interactions, and provides data for both planogram generation and customer behavior analysis. This multi-functionality maximizes data availability while justifying the system complexity through diverse operational benefits.
Solution Approach 2:
The system continuously collects data from cameras and weight sensors throughout store operations, creating an ongoing stream of planogram information. This continuous data collection ensures comprehensive information availability without requiring periodic manual interventions, maintaining system complexity only during initial setup and ongoing processing.
3Measurement precision
If probability-based item identification is implemented, then item location accuracy improves, but the computational processing requirements increase
Solution Approach 1:
The system pre-processes and stores image data and gesture information as it is collected, organizing data into structured formats suitable for probability calculations. By preparing data in advance and maintaining organized databases of customer interactions and item locations, the system reduces the computational burden during probability calculations, lowering energy consumption while maintaining high measurement precision.
4Measurement precision
If customer gesture analysis is used to identify items, then item identification accuracy improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent replaces complex manual gesture analysis with automated computer vision algorithms that process camera images to detect and interpret customer gestures. The system uses machine learning models to recognize gesture patterns and correlate them with item locations, transforming difficult measurement tasks into automated visual processing that improves accuracy while managing detection complexity through software solutions.
Data Source
AI summary
Disclosed are systems and methods for generating a planogram of a store. The systems and methods may include receiving a plurality of images and transaction data. At least one of the plurality of images may include user data representing a user proximate the product. The transaction data may be associated with a completed purchase of the product. Based on the transaction data and the user data, a probability of the user retrieving the product may be determined and used to generate the planogram of the store.