In-Store Imaging Parameter Adjustment for Shelf Coverage Recovery
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Solution Overview
Problem
Existing imaging systems in retail environments suffer from human-error-prone calibration processes and accidental misalignment, leading to inadequate image capture for inventory management applications.
Innovation Solution
An automated method for controlling an imaging device's viewing parameters, such as direction of sight and focal length, using neural networks 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 is performed by personnel using mobile devices, then initial camera setup can be completed, but human errors occur during calibration and image quality becomes insufficient
Solution Approach 1:
The system performs self-calibration by automatically capturing multiple images at different viewing parameters, evaluating fixture coverage scores through image processing, and selecting optimal parameters without human intervention. The camera system serves itself to calibrate, eliminating reliance on personnel operations.
Solution Approach 2:
The system implements feedback loops where captured images are evaluated against coverage criteria, and viewing parameters are adjusted based on evaluation results. This closed-loop control ensures image quality requirements are met through automatic correction of calibration deviations.
2Productivity
If cameras are installed and fixed in sales areas, then real-time monitoring of shelves is enabled, but cameras may be accidentally moved by clients causing misalignment
Solution Approach 1:
The system continuously monitors image quality and detects deviations from optimal viewing parameters. When misalignment is detected, the system automatically adjusts camera position or parameters to restore optimal imaging conditions, compensating for accidental movements.
Solution Approach 2:
The camera system transitions from a static fixed installation to a dynamically adjustable system that can automatically reposition or adjust viewing parameters in response to detected misalignment, maintaining operational effectiveness despite position changes.
3Area of stationary object
If multiple cameras are calibrated manually, then coverage of sales area is achieved, but calibration time and human resources increase
Solution Approach 1:
Each camera performs automatic self-calibration independently, capturing images and adjusting parameters without requiring personnel intervention. This eliminates the time-consuming manual calibration process while enabling deployment of multiple cameras across the sales area.
Solution Approach 2:
The calibration process is divided into independent automated tasks for each camera, allowing parallel calibration of multiple devices simultaneously, thereby reducing total calibration time while achieving comprehensive area coverage.
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.