Item Identification Search Space Reduction via ML Clustering
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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 dealing with large databases and complex images.
Innovation Solution
A system utilizing a combination of cameras and 3D sensors to capture and process images of items on a platform, selectively choosing cameras for optimal views, and employing machine learning to identify items without user intervention, reducing the search space and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to identify items by comparing features against every item in a database, then item identification accuracy is maintained, but processing time increases significantly and real-time performance is lost
Solution Approach 1:
The patent segments the large item database into multiple smaller clusters or groups based on feature similarities. Instead of comparing query image features against every item in the entire database, the system first identifies which cluster the query belongs to and only then performs detailed comparison within that specific cluster. This segmentation dramatically reduces the number of comparisons needed while maintaining identification accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing item features into clustered structures before actual identification occurs. Feature extraction, dimensionality reduction, and cluster formation are completed in advance, so that during real-time identification, only final matching within pre-defined clusters is needed, significantly reducing processing time.
2Measurement precision
If multiple cameras are used to capture images of items on a platform, then item identification accuracy improves, but computational complexity and processing load increase
Solution Approach 1:
The patent extracts and removes redundant information from multiple camera images before processing. By identifying and eliminating duplicate or highly similar views, the system keeps only the most informative images for analysis. This extraction approach maintains identification accuracy while significantly reducing the computational load associated with processing all camera outputs.
Solution Approach 2:
The system applies partial action by selectively processing only certain camera images rather than all available images. Based on item position, camera angles, and view quality metrics, the system chooses to process only the most relevant subset of images, achieving sufficient identification accuracy without the full computational burden of analyzing every camera capture.
3Measurement precision
If user scanning or manual identification is required for item checkout, then item identification accuracy is ensured, but system throughput and productivity decrease
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify and checkout items without user intervention. The image processing and item recognition systems autonomously perform what previously required manual user action, dramatically increasing throughput while maintaining accuracy through automated feature extraction and database matching processes.
Solution Approach 2:
The system replaces the mechanical action of manual item scanning and identification with automated optical and computational processes. Cameras capture images and machine learning algorithms perform identification, substituting human manual operations with automated systems that operate faster and without fatigue, thereby increasing overall system productivity.
Data Source
AI summary
A device is configured to receive a first encoded vector and receive one or more feature descriptors for a first object. The device is further configured to remove one or more encoded vectors from an encoded vector library that are not associated with the one or more feature descriptors and to identify a second encoded vector in the encoded vector library that most closely matches the first encoded vector based on the numerical values within the first encoded vector. The device is further configured to identify a first item identifier in the encoded vector library that is associated with the second encoded vector and to output the first item identifier.