Item Identity Confirmation Using Height-Based Search Filtering
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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 item identification and tracking in dynamic environments is challenging due to shifts in camera, 3D sensor, and platform positions.
Innovation Solution
A system using 3D sensors and cameras to capture images, select optimal cameras based on item pose, and continuously recalibrate homographies to maintain accurate pixel-to-physical location mapping, along with intelligent detection of triggering events and item placement, and reducing search space through container categorization and item height filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional item identification methods are used to identify and track multiple items, then item identification can be achieved, but the process is computationally intensive and time-consuming, making it incompatible with real-time applications
Solution Approach 1:
The patent segments the item identification process by dividing items into groups based on shared characteristics (container category, height range). Instead of comparing each item against the entire database, the system first filters items into relevant groups, then performs detailed comparison only within those groups. This segmentation dramatically reduces computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-categorizing items into container categories and storing height information in advance. When an item needs identification, the system has already prepared the filtered groups based on container category and height, eliminating the need for real-time comprehensive database searches. This preliminary organization enables real-time identification performance.
2Measurement precision
If comprehensive item feature comparison is performed against all items in database, then identification accuracy is maintained, but the process requires significant time which is not compatible with real-time applications
Solution Approach 1:
The patent segments the database into multiple groups based on container category and height characteristics. When identifying an item, the system only compares features against items in the relevant segment rather than the entire database. This maintains identification accuracy within the relevant group while dramatically reducing the time required by limiting the comparison scope.
Solution Approach 2:
The patent applies local quality by making the identification process adaptive to the specific item being identified. Instead of using a uniform comprehensive comparison approach for all items, the system tailors the comparison scope to the local characteristics of the item (its container category and height), comparing only against items with similar characteristics. This localized approach maintains accuracy while reducing time.
3Measurement precision
If multiple cameras are used to capture images of items, then item identification accuracy is improved, but the number of images to be processed increases, increasing computational load
Solution Approach 1:
The patent segments the image processing task by first filtering potential item matches based on container category and height before performing detailed image comparison. This segmentation means that even though multiple camera images are captured, the system only processes images of items that are relevant based on the filtered criteria, reducing the overall processing complexity while maintaining the benefits of multiple camera views.
4Productivity
If the system processes all captured images to identify items, then comprehensive item identification is achieved, but hardware resources are not efficiently utilized and throughput is limited
Solution Approach 1:
The patent extracts and utilizes specific characteristics (container category, height) from items to create filtered groups. By extracting these key features and using them to pre-filter the item database, the system avoids processing images for items that are unlikely matches. This extraction approach improves hardware resource utilization by focusing computational power only on relevant image processing tasks, thereby increasing overall system throughput.
Solution Approach 2:
The patent performs preliminary filtering based on container category and height characteristics before image processing. This preliminary action prepares the data in advance, so that when images are captured and processed, the system already knows which items are relevant candidates. This eliminates wasted hardware resources on processing irrelevant items and significantly improves system throughput.
Data Source
AI summary
A device captures an image of a first item and generates a first encoded vector for the image. The device identifies a set of items that have at least one attribute in common with the first item. The device determines the identity of the first item based at least on attributes of the first item. The device determines that a confidence score associated with the identity of the first item is less than a threshold percentage. In response, the device determines a height of the first item. The device identifies item(s) with average heights within a threshold range from the height of the first item. The device compares the first encoded vector with a second encoded vector associated with a second item from the identified item(s). If the first encoded vector corresponds to the second encoded vector, the device determines that the first item corresponds to the second item.