Homography Matrix for Satellite-Image Geolocation
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Solution Overview
Problem
Current systems that use cameras and image analytics for monitoring, such as traffic and parking systems, cannot provide users with latitude and longitude data from captured images, leading to inefficiencies and increased costs.
Innovation Solution
A system and method that compares camera images from a lighting fixture with satellite images to determine a homography matrix, allowing for the transformation of camera images into top-down views and the calculation of latitude and longitude coordinates of observed objects, using an imaging device, a processor, and a remote computing device to match points of interest between the two image types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If camera images are used for monitoring, then real-time detection capability is improved, but latitude and longitude data cannot be provided
Solution Approach 1:
The patent introduces satellite images as an intermediary medium to bridge camera images and geographic coordinates. The system captures both camera images and corresponding satellite images, then uses image matching algorithms to establish a homography matrix that transforms camera coordinates to latitude and longitude, thereby providing geographic information without sacrificing real-time detection capability
Solution Approach 2:
The patent replaces manual measurement and physical surveying methods with automated image processing and computer vision techniques. By using machine learning models and homography transformations, the system automatically extracts geographic coordinates from camera images, eliminating the need for manual location marking or physical surveying while maintaining real-time detection
2Loss of information
If manual location marking is used, then latitude and longitude data can be obtained, but time and costs increase
Solution Approach 1:
The system enables self-service by automatically extracting geographic coordinates through image matching without requiring manual intervention. The automated homography matrix calculation and coordinate transformation processes eliminate the need for manual location marking, significantly reducing time and operational costs while maintaining accurate geographic data
Solution Approach 2:
The patent performs preliminary actions by capturing and processing satellite images in advance to create reference datasets. These pre-processed satellite images with known geographic coordinates serve as templates for subsequent automatic matching with camera images, enabling rapid coordinate extraction without repeated manual surveying
3Measurement precision
If camera images are transformed to top-down view, then mapping accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent replaces complex manual image processing and physical mapping operations with automated computer vision algorithms. By using deep learning models for feature detection, matching, and homography calculation, the system automatically transforms camera images to top-down views with high accuracy, eliminating the need for manual measurement and reducing processing complexity through automation
Solution Approach 2:
The patent changes the parameter space by transforming images from the camera's perspective coordinates to the satellite's top-down geographic coordinates through homography matrices. This coordinate transformation preserves mapping accuracy while simplifying the representation of geographic features, making it easier to integrate camera data with existing mapping systems
Data Source
AI summary
A system includes an imaging device configured to capture image data of a monitored area and a computing device having at least one processor. The computing device processor is configured to receive first image data from the imaging device and second image data from a satellite imagery system, where the second image data relates to the monitored area. The processor is also configured to determine a first set of points of interest in the first image data and a second set of points of interest in the second image data, wherein members of each set of points of interest are represented by a respective vector. The processor is further configured to generate a homography matrix based on the two vectors and determine, using the homography matrix, latitude and longitude coordinates of at least one object represented in the first image data but not the second image data.