Autonomous Aircraft Gesture Recognition via ML
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Solution Overview
Problem
Autonomous unmanned aerial vehicles lack the ability to interpret human gestures for ground operations, such as those used by marshals, due to the absence of a human pilot, leading to potential delays or errors in recognizing signals, especially in low visibility conditions.
Innovation Solution
A gesture recognition system utilizing a computer system and machine learning model manager that identifies temporal color images and generates optical flow data and saliency maps to train feature and classifier models, enabling the recognition of gestures made by human operators for ground operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hand signals are used for ground operations, then communication between marshaller and pilot is effective, but this method is not feasible for autonomous UAVs without a human pilot
Solution Approach 1:
The patent replaces the mechanical/visual communication system (hand signals visible to human pilot) with an automated computer vision system. The gesture recognition system uses image processing algorithms to detect and interpret hand signals, substituting the human pilot's visual perception and interpretation capabilities with automated computational processing.
Solution Approach 2:
The patent introduces an intermediary gesture recognition system between the marshaller and the autonomous UAV. This intermediary system captures images, processes them through machine learning models, and translates hand signals into automated commands, bridging the gap between human gesture language and autonomous vehicle control.
2Measurement precision
If traditional image processing is used for gesture recognition, then system complexity is low, but recognition accuracy and speed are insufficient for real-time operation
Solution Approach 1:
The patent segments the gesture recognition process into multiple specialized machine learning models: a first model for detecting hand presence, a second model for recognizing gesture types, and potentially additional models for different gesture categories. This segmentation allows each model to specialize in specific aspects of gesture recognition, improving overall accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent enhances traditional 2D image processing by incorporating temporal dimensions through video sequences and depth information from stereo cameras or time-of-flight sensors. This multi-dimensional approach (adding time and depth dimensions) provides richer feature sets for machine learning models, significantly improving recognition accuracy and enabling real-time processing.
3Reliability
If gesture recognition is implemented without specialized training data, then data collection is simple, but recognition performance in low visibility conditions deteriorates
Solution Approach 1:
The patent performs preliminary actions by collecting and preparing diverse training data under various conditions (different lighting, backgrounds, gesture speeds, and visibility levels) before deploying the system. This pre-training with comprehensive datasets ensures the model is robust to environmental variations, improving reliability without requiring complex real-time adaptations.
Solution Approach 2:
The patent employs parameter changes in the training data by varying lighting conditions, background scenarios, gesture execution speeds, and camera angles during the training phase. This exposes the machine learning model to a wide range of parameters and conditions, enabling it to maintain high recognition reliability across different operational environments including low visibility conditions.
Data Source
AI summary
A method, apparatus, system, and computer program product for training a gesture recognition machine learning model system. Temporal images for a set of gestures used for ground operations for an aircraft are identified by a computer system. Pixel variation data identifying movement on a per image basis from the temporal images is generated by the computer system. The temporal images and the pixel variation data form training data. A set of feature machine learning models is trained by the computer system to recognize features using the training data.