Multi-Camera Geolocation via Calibration Pixel Association
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Solution Overview
Problem
Current systems for geolocating objects of interest within an area of interest lack accuracy and efficiency, particularly in military and security operations, where precise coordinates are necessary for targeting and surveillance.
Innovation Solution
A system comprising multiple cameras with overlapping fields of regard and calibration points, where a processor associates the coordinates of calibration points with pixels in video images from multiple cameras to determine the geolocation of objects of interest, using triangulation and trilateration for precise positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras with overlapping fields of regard are used to cover the area of interest, then measurement precision of object geolocation is improved, but device complexity increases
Solution Approach 1:
The area of interest is divided into multiple overlapping fields of regard, each captured by a separate camera. This segmentation allows precise geolocation through triangulation while keeping individual camera components simple and manageable.
Solution Approach 2:
Multiple camera systems are merged into a unified geolocation network where images from different cameras are processed together. The processor integrates data from all cameras to calculate precise object positions through triangulation and trilateration, achieving high precision without requiring each individual camera to be overly complex.
2Manufacturing precision
If calibration points are associated with pixels in video images to establish coordinate relationships, then manufacturing precision of geolocation accuracy is improved, but loss of time for system setup increases
Solution Approach 1:
Calibration points are pre-placed at known locations within the area of interest before the actual geolocation operation. These calibration points establish a reference framework in advance, allowing the processor to quickly associate pixels with known coordinates without time-consuming setup during operation.
Solution Approach 2:
The system creates a digital copy of the physical calibration points by capturing their positions in video images and storing the coordinate relationships in a database. This digital copying allows rapid retrieval and application of calibration data during geolocation operations, reducing setup time while maintaining high accuracy.
3Reliability
If coordinates of calibration points are associated with calibration pixels in multiple camera images, then reliability of geolocation data is improved, but use of energy by the processing system increases
Solution Approach 1:
The system processes only the necessary portions of image data - specifically the calibration pixels and their corresponding coordinates - rather than processing entire images or all visual data. This partial processing approach maintains high reliability through multiple calibration points while significantly reducing overall energy consumption.
Solution Approach 2:
The calibration points serve as self-contained reference markers that automatically provide coordinate information when detected in images. This self-service mechanism eliminates the need for complex continuous processing to maintain calibration, reducing energy consumption while ensuring reliable geolocation data through the inherent properties of the calibration markers.
Data Source
AI summary
A system comprises a plurality of fixed cameras that each having a field of regard. Each point within an area of interest is covered by the field of regard of at least two of the cameras. Each camera captures an image of its field of regard and a plurality of calibration points within the area of interest. A processor calibrates the imaging system by at least associating the coordinates of each of the plurality of calibration points with a calibration pixel corresponding to an image of the calibration point in the image of each of the cameras. The processor geolocates the object of interest within the area of interest by at least comparing the location an image of the object of interest to the calibration pixels in the images generated by each of the plurality of cameras.