Geo-registration Accuracy via Machine Learning Object Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current georeistration methods for video data captured by airborne vehicles, such as drones, face challenges in accurately identifying and handling static and dynamic objects due to limitations in motion detection and object differentiation, especially in environments with varying lighting and camera movement.
Innovation Solution
The use of machine learning or neural networks to enhance georeistration by improving object identification and handling in video data captured by airborne vehicles, enabling better differentiation of static and dynamic objects and enhancing georeistration accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion detection methods are used to identify objects in video data, then the system complexity remains low, but the georeistration accuracy deteriorates due to inability to differentiate static and dynamic objects in varying lighting conditions
Solution Approach 1:
The patent replaces traditional motion detection algorithms with machine learning-based object identification systems. Neural networks are trained to recognize and classify objects in video frames, substituting simple pixel-difference-based motion detection with intelligent pattern recognition that can accurately differentiate static and dynamic objects even in varying lighting conditions, thereby improving georestation accuracy without excessive complexity increase
Solution Approach 2:
The system changes the operational parameters of object identification by using machine learning models that adapt to different lighting conditions. Instead of fixed threshold-based motion detection, the system uses trained neural networks that can adjust their sensitivity and recognition criteria based on environmental parameters like lighting, enabling accurate object differentiation across diverse conditions
2Reliability
If traditional object identification methods are used, then the processing speed remains high, but the ability to differentiate static and dynamic objects deteriorates in complex environments
Solution Approach 1:
The system performs preliminary training of machine learning models offline before actual georestation tasks. Neural networks are pre-trained on large datasets of images and videos containing various objects in different lighting conditions. This preliminary action enables the system to rapidly process incoming video data during actual operation, achieving both high reliability in object differentiation and efficient processing speed without real-time training overhead
Solution Approach 2:
The system uses trained machine learning models that have learned patterns from extensive training data. Instead of performing complex analysis from scratch for each video frame, the system applies pre-learned knowledge through copied neural network weights and structures, enabling rapid and accurate object identification in complex environments without excessive processing time
Data Source
AI summary
A Geo-synchronization system involves a video camera in a vehicle, such as a drone, that captures aerial images of an area. The success rate and the accuracy of the geo-synchronization algorithms is improved by using a trained feed-forward Artificial Neural Network (ANN) for identifying dynamic objects, that changes overtime, in frames captured by the video camera. Such frames are tagged, such as by adding metadata. The tagged frames may be used in a geosynchronization algorithm that may be based on comparing with reference images or may be based on another or same ANN, by removing the dynamic object from the fame, or removing the tagged frame for the algorithm. A dynamic object may change over time due to environmental conditions, such as weather changes, or geographical changes. The environmental condition may change is in response to the Earth rotation, the Moon orbit, or the Earth orbit around the Sun.