Automated Aviation Video Generation for Annotated ML Training
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Solution Overview
Problem
The collection of domain-specific video data for training machine learning algorithms in aviation environments is expensive, cumbersome, and hindered by privacy concerns, resulting in insufficient and biased datasets.
Innovation Solution
A system and method for automated video generation that creates photorealistic digital human characters and environments, allowing for the synthesis of annotated video datasets with varied scenarios and attributes, including physics configurations, to train machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real video data is collected for training machine learning algorithms in aviation environments, then the dataset provides authentic domain-specific content, but the collection process becomes expensive and cumbersome requiring specialized hardware and high setup overhead
Solution Approach 1:
The patent creates synthetic video data that copies and replicates real aviation scenarios, environments, and objects through computer-generated imagery. Instead of collecting real video data requiring specialized hardware, the system generates photorealistic simulations of aircraft interiors, exteriors, and aviation-related activities that serve as training data for machine learning algorithms
Solution Approach 2:
The patent introduces a synthetic data generation system as an intermediary between the need for authentic domain-specific data and the complexity of real data collection. This intermediary system uses physics engines and rendering software to create intermediate synthetic representations that bridge the gap between virtual simulations and real-world aviation scenarios
2Adaptability or versatility
If real video data is collected involving third party or personal data, then the dataset includes diverse real-world scenarios, but privacy concerns and personal privacy guidelines create barriers to data sharing
Solution Approach 1:
The patent creates synthetic copies of real-world aviation scenarios that replicate diverse situations (passenger behavior, crew activities, aircraft operations) without using actual personal data. The synthetic characters and environments mimic real-world diversity while eliminating privacy risks associated with collecting and sharing personal video data
Solution Approach 2:
The patent converts the limitation of not having access to real personal data into a benefit by generating synthetic data that is free from privacy concerns. The very fact that real data cannot be easily collected due to privacy issues is transformed into an opportunity to create unlimited synthetic training data without ethical or legal restrictions
3Measurement precision
If more domain-specific video data is collected to improve machine learning model performance, then the model accuracy increases, but the cost and complexity of data collection increases proportionally
Solution Approach 1:
The patent creates synthetic video data copies that can be generated indefinitely without additional collection costs. Once the synthetic data generation system is established, it can produce unlimited training data through computational processes, eliminating the linear relationship between data quantity and collection cost
Solution Approach 2:
The patent enables efficient data generation by changing the parameter space of synthetic data creation. Instead of collecting more real data, the system varies parameters such as lighting conditions, camera angles, character actions, and environmental factors to generate diverse training scenarios computationally, achieving scale without proportional cost increases
Data Source
AI summary
A system and method for automated video generation for training machine and deep learning algorithms in aviation environments generates photorealistic digital human characters and an aviation environment according to a desired scenario, including fixtures, free objects, lighting and physics configurations, and camera views. Character actions in the desired scenario are mapped to pose sequences which may be manually generated or transferred from image sequences of human activities, including both main character activities specified by the scenario, characters responding to the main characters, and background character actions. A video automation pipeline animates character actions performed by the digital human characters into video datasets with annotation files incorporating detailed pixel and depth information for each frame. Video datasets may include variant video datasets differentiated from the primary dataset by changes in domain variants (e.g., character attributes, environmental attributes) while preserving the portrayal of the desired scenario.