Machine Vision Tracking for Shopping Container Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Shoppers often face challenges in managing their shopping containers, such as needing additional space or support when their current container is full or too heavy to push or tow.
Innovation Solution
A system utilizing imaging assemblies to track the location of users with containers, detect the contents and condition of the containers, and generate notifications to autonomous mobile robots (AMRs) to provide assistance, such as additional containers, when needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a shopper uses a single container for shopping, then the container must be large enough to hold all items, but this makes the container too heavy to push or tow
Solution Approach 1:
The system divides the shopping task into multiple containers instead of using one large container. The imaging assembly detects when a container is full or too heavy, and the system provides additional containers to the shopper, effectively segmenting the total shopping load across multiple lighter containers that are easier to maneuver.
2Device complexity
If a shopper uses a single container for shopping, then fewer containers need to be managed, but this requires the container to be large enough to hold all items which makes it too heavy to push or tow
Solution Approach 1:
The system automatically monitors container status using imaging assemblies and detects when containers are full or too heavy. Instead of requiring the shopper to manually assess container status, the system self-monitors and automatically provides additional containers when needed, reducing the management burden on the shopper.
Solution Approach 2:
The system continuously tracks container status through imaging assemblies and provides real-time feedback to both shoppers (via notifications about full containers) and employees (via alerts when additional containers are needed). This feedback loop enables dynamic container management without increasing shopper burden.
3Reliability
If the system continuously monitors container status using imaging assemblies, then timely assistance can be provided, but this increases system complexity and energy consumption
Solution Approach 1:
The system proactively identifies containers that are becoming full or too heavy before they become problematic, using imaging assemblies to detect early signs of capacity issues. This preliminary detection allows the system to provide assistance before the shopper encounters difficulties, improving reliability without requiring overly complex real-time intervention mechanisms.
Data Source
AI summary
Systems and methods utilizing machine vision for tracking and assisting an individual within a venue are provided herein. The method tracks a location of an individual associated with a container and detects at least one of the container or at least one object within the container present in captured first image data. The method identifies at least one of the at least one object or a region of interest associated with the container and determines, based on the identification, at least one of a value of at least one attribute of the at least one object or first and second sub-areas of the region of interest. The method determines whether at least one of the value of the at least one attribute is greater than a first threshold or a ratio of the first and second sub-areas is less than a second threshold and generates and transmits a notification to a device based on the determination.