Composite Image Generation for Display Rack Item Tracking
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Solution Overview
Problem
Existing digital image processing systems struggle to accurately track and identify items on display racks when a complete image cannot be captured, leading to inefficiencies and inaccuracies due to the need for multiple images and increased processing time, which is not compatible with real-time applications.
Innovation Solution
A system that generates a composite image from multiple partial images of a display rack, uses machine learning models to identify items and their locations, and compares these to a master template to determine correct placement, thereby enabling efficient and accurate item tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the user stands some distance away from the rack to capture the entire rack within a single image, then the complete rack can be imaged, but the items in the image become too small to be identified using existing image processing techniques
Solution Approach 1:
The system divides the rack into multiple sections and captures images of each section from different positions. Instead of requiring a single complete image of the entire rack, the system segments the rack into manageable portions that can be captured from various angles, allowing items to remain sufficiently large and identifiable in each partial image.
Solution Approach 2:
The system merges multiple partial images of the rack into a comprehensive representation. By combining images captured from different positions and angles, the system reconstructs the complete rack layout while maintaining item visibility and identifiability, resolving the contradiction between complete coverage and identification precision.
2Measurement precision
If the user captures multiple images of the rack from different positions to identify items, then items can be identified with sufficient size, but existing systems are unable to associate the identified items with items from other images
Solution Approach 1:
The system introduces a composite image as an intermediary that serves as a reference framework. This composite image acts as a mediator that associates and links items identified in multiple partial images, providing a unified context that connects items across different captured images without requiring complex direct association between each pair of images.
Solution Approach 2:
The system creates a composite image that serves as a virtual copy or representation of the complete rack. This composite image is generated by merging partial images and serves as a reference model that enables the system to associate and track items across multiple captures, simplifying the association process through this intermediary representation.
3Measurement precision
If the system analyzes items using multiple partial images, then complete analysis of all items can be performed, but processing time increases significantly
Solution Approach 1:
The system performs preliminary actions by capturing multiple partial images of the rack from different positions before the actual analysis begins. These pre-captured images are then merged into a composite image that serves as the basis for subsequent item identification and location analysis, allowing the analysis process to work with a pre-prepared comprehensive representation rather than processing each partial image separately during analysis.
Solution Approach 2:
The system merges multiple partial images into a single composite image that represents the complete rack. This merging operation is performed beforehand to create a unified analysis target, significantly reducing the processing time required during item identification and location analysis compared to processing each partial image separately.
4Area of stationary object
If the system uses existing image processing techniques to identify items in distant images, then complete rack coverage is achieved, but the process is not compatible with real-time applications due to significant processing time
Solution Approach 1:
The system segments the rack into multiple sections and captures images of each section from different positions. By working with smaller, more manageable partial images rather than attempting to process a complete distant image, the system maintains real-time processing capability while achieving comprehensive rack coverage through the segmented approach.
Solution Approach 2:
The system merges multiple partial images into a composite image that represents the complete rack. This merging operation is optimized to create a comprehensive representation efficiently, enabling real-time analysis of the complete rack layout while maintaining the speed requirements for real-time applications.
Data Source
AI summary
A device configured to receive a rack identifier for a rack that is configured to hold items. The device is further configured to identify a master template that is associated with the rack. The device is further configured to receive images of the plurality of items on the rack and to combine the images into a composite image of the rack. The device is further configured to identify shelves on the rack within the composite image and to generate bounding boxes that correspond with an item on the rack. The device is further configured to associate each bounding box with an item identifier and an item location. The device is further configured to generate a rack analysis message based on a comparison of the item locations for each bounding box and the rack positions from the master template and to output the rack analysis message.