Associated Item Identification for Real-Time Multi-Item Tracking
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Solution Overview
Problem
Identifying and tracking multiple items in real-time is computationally intensive and time-consuming, making it incompatible with real-time applications, especially when items need to be identified and tracked simultaneously.
Innovation Solution
A system utilizing cameras and 3D sensors to capture and process images of items on a platform, selecting optimal cameras based on item pose, and employing machine learning to identify and assign items to users without manual scanning, combined with techniques like homography and encoded vector libraries for efficient item tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional item identification methods are used where each item is individually identified and compared against a database, then identification accuracy is maintained, but processing time increases significantly and real-time performance is lost
Solution Approach 1:
The patent merges multiple item identification processes into a single batch processing operation. Instead of identifying and comparing each item individually against the database, the system captures images of multiple items simultaneously and processes them together through a single comparison operation against the encoded vector library, dramatically reducing total processing time while maintaining identification accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing item images into encoded vectors and organizing them in a searchable library before actual identification occurs. This pre-computation of visual features allows the system to quickly compare captured items against the pre-organized library without performing computationally intensive processing during the identification moment, enabling real-time performance.
2Measurement precision
If multiple cameras are used to capture images of items from different angles, then item identification accuracy improves, but system complexity and hardware resources increase
Solution Approach 1:
The system extracts only the essential visual features from item images using encoded vectors, rather than processing and storing complete high-resolution images. This extraction of critical identifying features into compact vector representations maintains identification accuracy while significantly reducing the computational burden and system complexity associated with processing multiple camera inputs.
Solution Approach 2:
The patent transforms the representation of item visual data from complex image formats to simplified encoded vector parameters. This parameter change converts multi-dimensional image data into compact numerical vectors that capture essential identifying features, enabling efficient comparison and processing without requiring complex hardware or software infrastructure.
3Reliability
If manual scanning or user identification is required, then system reliability is maintained, but productivity and throughput are significantly reduced
Solution Approach 1:
The system implements self-service identification where items automatically identify themselves through their visual features captured by cameras. The encoded vector library enables items to be automatically matched and identified without requiring manual scanning or user intervention, eliminating the bottleneck of manual identification while maintaining reliability through the robustness of the visual feature comparison approach.
4Productivity
If the system processes a larger number of items simultaneously, then productivity increases, but computational intensity and time requirements worsen
Solution Approach 1:
The system uses encoded vectors as simplified copies or representations of actual item images. Instead of processing complete high-resolution images of multiple items simultaneously, the system works with compact vector copies that capture essential identifying features. This copying approach enables batch processing of many items with minimal computational intensity, as vector comparison is far less computationally demanding than full image processing.
Data Source
AI summary
An item tracking system comprises a plurality of cameras, a memory storing associations between item identifiers of respective items, and a processor configured to capture a plurality of first images of a first item and identify a first item identifier of the first item based on the first images. The processor captures a plurality of second images of a second item, generates cropped image of the second item from each second image, and identifies an item identifier for each cropped image. Based on the associations stored in the memory, the processor determines that an association exists between the first item identifier of the first item and a second item identifier, and assigns the second item identifier to the second item when at least one of the item identifiers corresponding to the cropped images is the second item identifier.