Camera Positioning for Retail Shelf Recognition
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Solution Overview
Problem
Existing visual product analytics systems for retail stores face challenges in accurately recognizing products on shelves due to varying shelf heights, camera mounting heights, and aisle widths, requiring a design and installation tool to optimize camera positioning for effective product recognition.
Innovation Solution
A camera positioning apparatus with a user interface and microprocessor that calculates optimal camera parameters such as elevation angle, resolution, and field of view based on input parameters like shelf dimensions and required pixel resolution, aiding in the selection and installation of suitable cameras for visual product analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cameras are installed at varying heights and positions to accommodate different store configurations, then adaptability to store layouts is improved, but measurement precision of product labels deteriorates
Solution Approach 1:
The system dynamically adjusts camera parameters (elevation angle, horizontal angle, focal length) based on the specific store configuration, shelf dimensions, and product label characteristics. This allows the same camera model to adapt to varying store layouts while maintaining consistent image quality and label recognition accuracy through real-time parameter optimization.
Solution Approach 2:
The patent changes multiple camera parameters simultaneously (mounting height, elevation angle, horizontal angle, focal length) to optimize both adaptability to different store configurations and measurement precision of product labels. The system calculates optimal parameter combinations that ensure sufficient pixel resolution on labels while accommodating various shelf heights and aisle widths.
2Area of stationary object
If camera mounting height and distance are increased to cover larger shelf areas, then field of view is improved, but manufacturing precision of clear product labeling deteriorates
Solution Approach 1:
The system transitions from a single fixed camera position to a multi-dimensional parameter space, adjusting not only mounting height and distance but also elevation angle, horizontal angle, and focal length. This dimensional expansion allows the camera to cover larger shelf areas while maintaining adequate label resolution by optimizing the combination of all parameters rather than relying solely on distance and height.
Solution Approach 2:
The system dynamically determines optimal camera parameters based on the specific shelf dimensions, product label sizes, and required pixel resolution. By calculating the appropriate combination of mounting height, distance, elevation angle, and focal length for each configuration, the system achieves both extensive shelf coverage and sufficient label image quality without manual trial and error.
3Measurement precision
If multiple camera parameters are adjusted to optimize product recognition, then product analytics accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary calculations to determine optimal camera parameters before actual installation. By pre-calculating the ideal combination of mounting height, elevation angle, horizontal angle, and focal length based on store layout data and shelf dimensions, the system eliminates the need for complex on-site adjustments and manual optimization, thereby reducing installation complexity while maintaining high product recognition accuracy.
Solution Approach 2:
The system automatically determines optimal camera parameters through self-service calculations without requiring manual intervention or complex adjustment mechanisms. The automated parameter determination based on input store configuration data simplifies the deployment process while ensuring optimal product recognition performance, reducing both device complexity and installation time.
Data Source
AI summary
An example disclosed apparatus includes memory configured to store a mapping of items to respective ones of a plurality of minimum pixel-resolutions, wherein the minimum pixel-resolutions correspond to an image resolution required to identify a corresponding one of the items using an image; and a processor configured to obtain identifying information corresponding to one of the items; select, using the mapping and based on the identifying information, one of the plurality of minimum pixel-resolutions; determine a value of an output parameter based on the selected one of the plurality of minimum pixel resolutions and a camera position relative to a shelf, wherein the output parameter is at least one of an elevation angle of the camera, a vertical resolution, a horizontal resolution, a vertical field of view, and a horizontal field of view; and output the determined value of the output parameter to facilitate positioning of the camera.