AR Model Pipeline for Egocentric Data Annotation and Training
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Solution Overview
Problem
Intelligent Augmented Reality (AR) technology faces challenges in processing diverse and dynamic egocentric sensor data due to inefficiencies in data standardization, processing, and privacy compliance, hindering the deployment of effective machine learning models for advanced applications like smart glasses.
Innovation Solution
A computer-implemented method comprising a sequence of processing modules for standardizing, annotating, and refining egocentric sensor data, including data ingestion, keyframe selection, manual annotation, and model generation, to create task-specific machine learning models for AR applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used for egocentric sensor data, then data standardization and processing can be performed, but processing time and errors increase significantly
Solution Approach 1:
The patent segments the data processing workflow into distinct modular components: data ingestion module, standardization module, annotation module, and model generation module. Each module handles specific tasks independently, enabling parallel processing and reducing overall processing time while maintaining data quality standards for egocentric sensor data from smart glasses.
Solution Approach 2:
The patent introduces an intermediary standardized data format that bridges raw diverse sensor data and machine learning model requirements. This intermediate representation layer includes standardized schemas for sensor data, annotations, and metadata, enabling efficient transformation and processing without repeated complex conversions.
2Manufacturing precision
If manual annotation processes are used for training data, then data quality and accuracy improve, but processing time and resource requirements increase
Solution Approach 1:
The patent performs preliminary automated preprocessing of sensor data including synchronization, filtering, and initial annotation before manual review. This preliminary action reduces the workload on human annotators while maintaining high accuracy standards, allowing them to focus on critical validation and complex annotation tasks.
Solution Approach 2:
The patent implements feedback loops where annotated data is used to improve automated annotation algorithms, which then assist in subsequent annotation tasks. This creates a progressively improving system where manual annotation quality enhances automated processes, increasing overall throughput while maintaining precision.
Data Source
AI summary
A method of providing a machine learning model (MLM) for use in a process which uses a ML algorithm includes receiving image sensor data and performing processing modules. A first module includes receiving the sensor data, determining a source, parsing source information, converting the sensor data into data items, associating them with meta data information, and ingesting them with associated meta data information. The second module includes accessing a data warehouse and identifying an image keyframe per data item, and associating information indicative of the image keyframe with the respective data item. The third module includes presenting the image keyframe and associated information of the respective data item via an HMI for annotation, receiving an annotation and augmenting the image keyframe. The fourth module includes generating and/or updating a MLM using an annotated dataset, and the fifth module includes uploading the MLM as a task-specific MLM to a storage device.