Camera-Based Shelf Inventory with 3D Geometry Calibration
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Solution Overview
Problem
Existing inventory tracking systems in retail stores are expensive, difficult to maintain, and provide infrequent updates, lacking insights into item removal times, which can affect cashierless checkout arrangements.
Innovation Solution
Utilizing camera systems integrated into store infrastructure that calibrate themselves to shelf geometry, employing flash photography and watermark data to generate realograms, and using multi-color LEDs to optimize illumination for watermark decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If survey robots or drones are used to automate inventory tracking, then labor costs and errors are reduced, but system cost and maintenance difficulty increase significantly
Solution Approach 1:
The camera system automatically calibrates itself to shelf geometry without human intervention. The system captures images, identifies shelf structures, and adjusts its viewpoint adaptively on its own, eliminating the need for manual setup and maintenance that would be required for complex robotic systems
Solution Approach 2:
The patent replaces mechanical survey robots with a stationary camera system that uses computational methods (image processing and geometry analysis) to achieve inventory tracking. This substitution of mechanical systems with optical-computational systems reduces complexity and maintenance requirements
2Device complexity
If stationary camera systems are used for inventory tracking, then system cost and maintenance are reduced, but the ability to capture timely item removal data is limited without frequent manual updates
Solution Approach 1:
The camera system operates continuously to capture images of shelves, enabling real-time detection of item removals. Unlike periodic manual surveys, the continuous operation ensures that item removal events are detected as they occur, providing timely data for cashierless checkout arrangements
Solution Approach 2:
The system analyzes captured images to detect changes in shelf content and provides feedback about item removals. This feedback mechanism enables the system to automatically identify when items are taken from shelves and report this information in real-time
3Measurement precision
If flash photography is used to illuminate shelves for image capture, then image quality is improved, but shopper comfort is reduced when persons are nearby
Solution Approach 1:
The camera system dynamically adjusts its operation based on detected conditions. When a person is detected near the camera, the system switches from flash photography to ambient light imaging, and vice versa. This dynamic adaptation allows the system to maintain image quality while avoiding shopper discomfort in different situations
Solution Approach 2:
The system changes the illumination parameter (flash vs. ambient light) based on the presence of persons. By monitoring environmental conditions and adjusting the lighting mode accordingly, the system optimizes both image capture quality and shopper comfort
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
Provides frequent inventory updates, reduces maintenance costs, and enables accurate tracking of item removal times, supporting cashierless checkout systems.
Implementation Method 1
a 3D sensor that captures a depth map of the scene
Implementation Method 2
a flash that illuminates the scene
Data Source
AI summary
Inventory on a rack of store shelves is monitored by a camera-equipped system that senses when items have been removed. Image data is desirably sensed at plural spectral bands, to enhance item identification by digital watermark and/or other image recognition techniques. The system can be alert to the presence of nearby shoppers, and change its mode of operation in response, e.g., suppressing flash illumination or suspending image capture. The system may self-calibrate to the geometry of shelving in its field of view, and affine-correct captured imagery based on the camera's viewpoint. A great many other features and arrangements are also detailed.