Automated Training Data Generation for Re-Identification Models
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Solution Overview
Problem
Current methods for training machine-learning models for visual person re-identification require extensive manual annotation, which is time-consuming and inefficient, limiting the scalability and adaptability of re-identification systems.
Innovation Solution
A computer system and method that automatically generate training data by grouping samples of media data representing the same person, animal, or object into tuples using secondary information such as positional, temporal, or wireless identifiers, allowing for triplet loss-based training without relying on manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to create training images, then training data can be generated, but the process becomes work-intensive and slow
Solution Approach 1:
The system automatically generates training data by self-organizing media samples into tuples based on secondary information, eliminating the need for manual annotation. The computer system performs all grouping and training data generation operations autonomously, allowing the training process to serve itself without human intervention.
Solution Approach 2:
The system performs preliminary grouping of media samples into tuples using secondary information before the actual training process. This pre-organization of training data into structured tuples (baseline, positive, negative) prepares everything in advance, so that when training begins, the data is already ready to use without requiring manual annotation during the training phase.
2Quantity of substance
If large amounts of manual annotation work are performed, then comprehensive training data can be created, but the process becomes time-consuming
Solution Approach 1:
The system replaces the mechanical manual annotation process with an automated computer-based system that uses secondary information (metadata, timestamps, GPS coordinates, device identifiers) to automatically group media samples. This substitution eliminates the need for human annotators to manually review and label each image, dramatically reducing annotation time while maintaining or increasing training data volume.
Solution Approach 2:
The system introduces secondary information as an intermediary to automatically associate media samples with their corresponding tuples. Instead of manual annotation directly linking images to identities, the secondary information (such as metadata from mobile devices, timestamps, and location data) acts as a mediator that automatically groups samples, enabling rapid generation of large volumes of training data.
3Extent of automation
If automated grouping using secondary information is implemented, then manual annotation effort is reduced, but system complexity increases
Solution Approach 1:
The system uses secondary information that is already universally generated by mobile devices and media capture systems (metadata, timestamps, GPS, device identifiers). By leveraging this existing multi-functional data that serves multiple purposes (device operation, location tracking, timing), the system avoids adding dedicated complex annotation infrastructure, achieving automation while keeping complexity manageable.
Data Source
AI summary
Examples relate to a concept for generating training data and training a machine-learning model for use in re-identification. A computer system for generating training data for training a machine-learning model for use in re-identification comprising processing circuitry configured to obtain media data, the media data comprising a plurality of samples representing a person, an animal or an object. The processing circuitry is configured to process the media data to identify tuples of samples that represent the same person, animal or object. The processing circuitry is configured to generate the training data based on the identified tuples of samples that represent the same person, animal or object.