In-Store Imaging Calibration Using Fixture Coverage Feedback
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Solution Overview
Problem
Existing imaging systems in retail environments require manual calibration of viewing parameters, which is prone to errors and can be disrupted by accidental movement, leading to inadequate image capture for computer-vision-based inventory management.
Innovation Solution
An automated method for controlling an imaging device's viewing parameters, including focal length and direction, using a neural network to determine fixture coverage scores and adjust settings to ensure optimal image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual calibration of viewing parameters is performed, then the imaging device can be set up, but human errors occur and image quality is compromised
Solution Approach 1:
The imaging device automatically performs calibration by capturing images, determining fixture coverage scores, and adjusting viewing parameters without human intervention. The system serves itself by using its own captured images to evaluate and optimize its viewing parameters, eliminating reliance on manual calibration and its associated human errors.
Solution Approach 2:
The system captures images, evaluates them using a neural network to determine fixture coverage scores, and uses this feedback to automatically adjust viewing parameters. This closed-loop feedback mechanism ensures continuous optimization of image quality and eliminates the one-time manual calibration approach that is prone to errors.
2Area of stationary object
If cameras are moved to new locations, then coverage of fixtures can be improved, but viewing parameters become misaligned and image quality deteriorates
Solution Approach 1:
After moving a camera to a new location, the system automatically performs preliminary calibration actions by capturing images and adjusting viewing parameters before actual inventory monitoring begins. This ensures the camera is properly aligned and optimized for its new position, preventing image quality deterioration.
Solution Approach 2:
The imaging device automatically recalibrates itself after being moved by capturing images of the fixture and adjusting its viewing parameters based on the determined fixture coverage score, eliminating the need for manual realignment and ensuring continuous optimal performance.
3Reliability
If automated adjustment of viewing parameters is implemented, then human error is reduced, but system complexity increases
Solution Approach 1:
The patent replaces manual mechanical calibration operations with an automated digital system that uses image capture, neural network evaluation, and automatic parameter adjustment. This substitution of mechanical/manual processes with automated digital control reduces human error while the integration into existing imaging devices keeps added complexity manageable.
Data Source
AI summary
The invention relates to a method of controlling an imaging device having at least one controllable viewing parameter and arranged to capture images of an articles placement fixture, comprising the set-up steps of:successively capturing images (CAPTx) of the articles placement fixture with the imaging device, wherein said capturing comprises controlling the at least one controllable viewing parameter to be modified between two successive image captures;determining (Sfc) for each of the successively captured images a fixture coverage score;selecting (SLC) a reference image as the image from among the successively captured images having the best fixture coverage score;setting (SET) the at least one controllable viewing parameter to be the one used when capturing the reference image.