Depth Camera Item Counting for Bagged Produce Checkout
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Solution Overview
Problem
Conventional checkout systems face inefficiencies and errors in determining item quantities, especially with produce in bags, leading to slowed transactions and potential fraud due to reliance on customer input or weight-based verification, which is inaccurate for varying item weights.
Innovation Solution
Employing a depth sensing camera to create a 3D map of items on a scale, projecting a plane to count individual blobs representing items, and verifying the count against customer input to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customers manually count items in bags during checkout, then item quantity can be determined, but transaction time increases and throughput decreases
Solution Approach 1:
The patent replaces the manual mechanical counting process with an automated optical system using cameras and image processing algorithms. The system captures images of items on the scale and automatically counts them through computer vision, eliminating the need for customers to manually count items while maintaining accurate quantity determination.
Solution Approach 2:
The system enables self-service by automatically determining item quantities without requiring customer intervention in the counting process. The automated recognition system processes images and provides quantity information independently, allowing customers to simply place items on the scale without manual counting.
2Ease of operation
If customers enter item quantities during self-checkout, then checkout process is simplified, but errors and fraud increase leading to shrinkage
Solution Approach 1:
The system provides automated feedback by capturing images of items on the scale and automatically verifying quantities. The image processing system provides real-time verification of item quantities entered by customers, allowing for immediate detection and correction of errors or fraud without requiring manual verification.
Solution Approach 2:
The patent replaces manual quantity entry and verification with automated optical recognition. Instead of relying on customer input that can be erroneous or fraudulent, the system uses camera-based automated identification to determine item quantities, providing more reliable and accurate counting.
3Illumination intensity
If conventional cameras are used to image items in bags, then visibility is provided, but accurate item counting is not possible due to lack of depth information
Solution Approach 1:
The patent transitions from two-dimensional conventional camera imaging to three-dimensional depth-based imaging. By using depth information from multiple camera perspectives or structured light, the system creates three-dimensional representations of items on the scale, enabling accurate counting even when items are in bags or overlapping, while maintaining good visual visibility.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances transaction efficiency by accurately counting items, reducing errors, and preventing fraud through automated verification of item quantities.
Implementation Method 1
A depth image is captured, a 3D map is generated for the items
Implementation Method 2
an infrared (IR) camera or sensor, which generates the depth image based on heat reflected off the items' surfaces
Data Source
AI summary
A depth image of items on a scale are captured. Depth information for the depth image is processed to produce a three-dimensional (3D) map of the depth information. The items are identified as blobs within the 3D map. A plane is generated and projected into the 3D map and each separate blob that intersects the plane is counted to determine an item quantity for the items depicted in the original depth image. The item quantity is provided back to a terminal for item quantity verification of the items during a checkout transaction at the terminal. In an embodiment, the plane is projected onto the 3D map and animated to move from a top of the 3D map to a bottom of the 3D map while the separate blobs are counted for the item quantity determination as the plane moves.