Elastic Directory Object Recognition Profiles
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Solution Overview
Problem
Conventional object recognition methods, such as facial recognition, face challenges in real-time processing due to the need for extensive training data cross-referencing and are inadequate for creating or updating groups, requiring significant time and resource allocation, especially when adding objects to new groups or updating existing ones.
Innovation Solution
A computerized system and method for object data storage that utilizes an elastic directory to eliminate the need for training data, enabling real-time object recognition by generating profiles with feature vectors and global unique identifiers, allowing for parallelized searches across partitions, thus facilitating rapid identification and scalability beyond existing limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional training-based object recognition methods are used, then accurate cross-referencing of object images is achieved, but significant time is required for training and real-time processing is insufficient
Solution Approach 1:
The system performs preliminary actions by generating feature vectors from training images and storing them in an elastic directory before actual recognition is needed. This pre-processing eliminates the need for time-consuming training during real-time operation, as the feature vectors are already prepared and stored for immediate comparison.
Solution Approach 2:
The patent extracts only the essential feature vectors from complete training images and stores these extracted features in the elastic directory. This extraction process separates the critical recognition data from the full images, enabling fast comparison while reducing storage requirements and processing time during recognition operations.
2Adaptability or versatility
If conventional object recognition systems store image data for groups, then object profiles can be created, but adding objects to new groups requires generating entirely new profiles and re-uploading image data
Solution Approach 1:
The patent implements a universal profile management system where a single object profile stored in the elastic directory can be referenced by multiple groups. The system uses group definitions that contain references to existing object profiles rather than storing duplicate data, allowing objects to be added to or removed from groups by simply modifying group reference lists without creating or deleting object profiles.
Solution Approach 2:
Instead of copying entire object profiles when creating groups, the system creates lightweight group definitions that contain references to existing object profiles. This referencing mechanism allows groups to be created and modified by copying only the necessary reference information, dramatically reducing the complexity of profile management.
3Quantity of substance
If large directories containing millions of profiles are maintained, then comprehensive object recognition is achieved, but system performance and uptime are compromised
Solution Approach 1:
The patent segments the large directory of profiles into multiple partitions or subsets that can be independently managed and searched. The elastic directory structure allows the system to divide the massive profile database into smaller, more manageable segments that can be processed in parallel, maintaining system performance even with millions of profiles.
Solution Approach 2:
The system replaces traditional mechanical database search mechanisms with an elastic directory structure that uses feature vector indexing and efficient data structures. This substitution enables the system to handle millions of profiles with improved search performance and system reliability, as the elastic directory is designed to scale efficiently with data size.
Data Source
AI summary
The disclosure herein describes systems and methods for object data storage. In some examples, the method includes generating a profile for an object in a directory, the profile including a first feature vector corresponding to the object and a global unique identifier (GUID) corresponding to the first feature vector in the profile; generating a search scope, the search scope including at least the GUID corresponding to the profile; generating a second feature vector from a live image scan; matching the generated second feature vector from the live image scan to the first feature vector using the generated search scope; identifying the GUID corresponding to the first feature vector that matches the second feature vector; and outputting information corresponding to the object of the profile identified by the GUID corresponding to the first feature vector.