Store Shelf Inventory Tracking Using Image-Based Stock Detection
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Solution Overview
Problem
Existing inventory management systems struggle to efficiently track and manage product inventory within stores, leading to issues such as out-of-stock conditions and inefficient allocation of shelf space, which can impact sales and customer satisfaction.
Innovation Solution
A computer system utilizing fixed cameras and mobile robotic systems to autonomously navigate and capture images of inventory structures, applying computer vision techniques to detect product units, derive stock conditions, and match these conditions with back-of-store inventory to predict and implement changes in restocking schedules, product assignments, and shelf space allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inventory tracking methods are used, then system complexity is low, but productivity and measurement precision deteriorate
Solution Approach 1:
The patent replaces manual mechanical inventory tracking with an automated computer vision system using cameras, processors, and algorithms to detect product units, track inventory levels, and generate restocking alerts, thereby dramatically improving productivity while accepting increased system complexity
Solution Approach 2:
The system creates visual copies (images) of inventory structures captured by cameras, processes these copies through computer vision algorithms to extract inventory information, and uses these processed copies for tracking and analysis without physically handling the actual products
2Productivity
If automated inventory tracking systems are implemented, then productivity improves, but device complexity increases
Solution Approach 1:
The patent segments the inventory tracking system into distinct functional modules: image capture by cameras, image processing and product detection by processors, inventory level calculation, and restocking alert generation, allowing each component to be optimized independently while maintaining overall system productivity
Solution Approach 2:
The computer vision system performs multiple functions including detecting product units, tracking inventory levels, identifying out-of-stock conditions, and generating restocking alerts, thereby improving productivity across multiple inventory management tasks while using a single integrated system
3Reliability
If real-time inventory monitoring is implemented, then reliability improves, but use of energy increases
Solution Approach 1:
The system implements periodic inventory monitoring by capturing images at scheduled intervals or triggered by specific events, rather than continuous monitoring, thereby maintaining reliable inventory tracking while reducing energy consumption of the cameras and processing systems
Solution Approach 2:
The computer vision system automatically detects inventory levels and generates restocking alerts without human intervention, ensuring reliable real-time monitoring while minimizing the energy required for manual checking and reducing overall system operational energy
4Measurement precision
If computer vision techniques are used to detect product units, then measurement precision improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent replaces manual visual inspection with computer vision algorithms that automatically analyze camera images to detect product units, measure inventory levels, and identify product characteristics, thereby achieving high measurement precision while the system handles the detection complexity
Solution Approach 2:
The system enhances measurement precision by adjusting various parameters including image resolution, lighting conditions, camera angles, and algorithm sensitivity thresholds, allowing optimal detection accuracy for different inventory scenarios while managing detection complexity through parameter optimization
Data Source
AI summary
A method includes: accessing a first image captured at a first time; deriving a first in-stock condition of the slot at the first time based on product units of a product type occupying a slot, depicted in the first image, at the first time based on features detected in the first image; accessing a second image captured at a second, later time; deriving a second out-of-stock condition of the slot at the second time based on features detected in the second image; accessing a back-of-store inventory status of the product type at the second time; and triggering an increase in quantity of facings of the product type at the slot based on a) the first in-stock condition at the slot at the first time, b) the first out-of-stock condition at the slot at the second time, and c) presence of back-of-store inventory of the product type at the second.