Item Identification Search Space Reduction for Real-Time 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, and maintaining accuracy in dynamic environments with shifting cameras, 3D sensors, and platforms is challenging.
Innovation Solution
A system using cameras and 3D sensors to identify and track items on a platform, employing techniques like homography recalibration, container-based classification, and item height filtering to reduce search space and improve accuracy, while reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image comparison is performed against every item in a database containing thousands of items, then item identification accuracy is maintained, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the large database of thousands of items into multiple smaller groups or categories. By dividing the search space into manageable segments, the system can perform comparisons more efficiently within each segment while maintaining overall identification accuracy. This segmentation reduces the computational burden of comparing against every item in the entire database.
Solution Approach 2:
The patent extracts and removes irrelevant or low-probability items from the comparison set before performing image matching. By pre-filtering the database to only include items that are likely candidates based on certain criteria (such as category, size, or other features), the system maintains accuracy while significantly reducing the number of comparisons needed.
2Measurement precision
If image comparison is performed against every item in a database containing thousands of items, then item identification accuracy is maintained, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing the database before actual item identification occurs. This includes pre-categorizing items, creating index structures, and preparing reference images in advance. When an item needs to be identified, the system can quickly narrow down candidates using these pre-prepared structures, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent applies partial action by performing comparisons only with a subset of database items that are most likely to match the query image. Rather than exhaustively comparing against every item, the system uses heuristics and filtering to identify and compare only with the most relevant candidates, achieving sufficient accuracy with reduced processing time.
3Adaptability or versatility
If multiple items are simultaneously identified and tracked, then system functionality is enhanced, but the problem becomes intractable due to computational demands
Solution Approach 1:
The patent segments the multi-item tracking problem by assigning each item its own identification pipeline and maintaining separate tracking states for each object. By dividing the overall tracking task into independent sub-tasks for each item, the system can simultaneously track multiple items without the computational complexity growing exponentially with the number of items.
Solution Approach 2:
The patent introduces additional dimensions for organizing and managing multiple item tracks, such as using spatial information, temporal sequences, or hierarchical categorization. By adding these dimensional structures to the tracking system, the patent can efficiently manage and differentiate multiple items simultaneously, making the problem tractable even as the number of tracked items increases.
Data Source
AI summary
In response to detecting a triggering event corresponding to placement of a first item on a platform, a plurality of images are captured of the first item and a plurality of cropped images are generated based on the images. For each cropped image, a first encoded vector is generated and compared to encoded vectors in an encoded vector library that are tagged as a front image. Based on the comparison, a second encoded vector is selected from the encoded vector library that most closely matches with the first encoded vector. An item identifier is identified that is associated with the second encoded vector. A particular item identifier is selected that is identified for a particular cropped image.