Item Identification Using Interaction History for Real-Time Tracking
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying and tracking multiple items in real-time due to computational intensity and time requirements, especially when images include several items, and maintaining accuracy in dynamic environments where cameras, 3D sensors, and platforms may shift positions.
Innovation Solution
A system utilizing cameras and 3D sensors to identify and track items on a platform, employing techniques such as homography recalibration, container-based classification, and item height filtering to enhance accuracy and efficiency, and reducing computational load by intelligent detection of triggering events and user interaction history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional item identification systems compare features of each item against every item in a database containing thousands of items, then identification accuracy can be maintained, but the process becomes computationally intensive and requires significant time, making it incompatible with real-time applications
Solution Approach 1:
The patent segments the large database of thousands of items into smaller groups or categories based on shared characteristics. This segmentation allows the system to compare item features against a reduced subset of relevant items rather than the entire database, significantly reducing computational complexity while maintaining identification accuracy for items within each segment.
Solution Approach 2:
The patent implements preliminary filtering and pre-processing of item data before the main identification process. By pre-organizing items into categories, pre-computing feature representations, or creating lookup tables, the system prepares the data structure in advance to enable rapid real-time comparison without sacrificing accuracy.
2Adaptability or versatility
If the system simultaneously identifies and tracks multiple items, then comprehensive monitoring capability is achieved, but the problem becomes intractable due to exponential increase in computational requirements
Solution Approach 1:
The patent divides the multi-item tracking problem into separate sub-problems by segmenting items into different tracks or groups. Each item or group of items is processed independently through dedicated identification pipelines, transforming the intractable simultaneous tracking of multiple items into manageable sequential or parallel sub-tasks.
Solution Approach 2:
The patent applies partial action by focusing computational resources on identifying and tracking only the most relevant or recently interacted items at any given moment, rather than continuously processing all items in the database. This selective approach maintains multi-item tracking capability while reducing overall computational burden.
3Measurement precision
If user manual scanning or identification is required, then precise item selection is achieved, but a bottleneck is created that slows down the system's ability to quickly identify items
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify and select items without requiring user intervention for scanning or manual identification. The system uses automated image recognition, feature matching, and database querying to independently complete the item identification process, eliminating the time bottleneck caused by manual user actions while maintaining selection accuracy.
4Productivity
If additional hardware resources are deployed to improve item identification speed, then processing throughput increases, but hardware utilization efficiency decreases and resources become unavailable for other processes
Solution Approach 1:
The patent improves processing throughput by optimizing software parameters and algorithms rather than adding hardware. This includes adjusting processing thresholds, optimizing data structures, improving feature extraction efficiency, and refining database query performance. These parameter changes increase identification speed and throughput while maintaining efficient hardware utilization without requiring additional physical resources.
Data Source
AI summary
In response to detecting a first triggering event corresponding to placement of a first item on a platform, a plurality of first images are captured of the first item. An item identifier associated with the first item is identified based on the first images and assigned to the first item. In response to detecting a second triggering event corresponding to placement of a second item on the platform, a plurality of second images are captured of the second item, a plurality of cropped images are generated based on the second images, and a plurality of item identifiers are determined for the second item based on the cropped images. When a process for selecting a particular item identifier from the plurality of item identifiers fails, a second item identifier is assigned to the second item based on an association between the first item identifier and the second item identifier.