UAV Camera Calibration Using Human Joint Triangulation
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Solution Overview
Problem
Conventional camera calibration methods using two-dimensional patterns, such as checkerboards, are time-consuming and require significant manual effort, prone to point-matching issues.
Innovation Solution
Utilize human joints for camera calibration by employing a system with pre-calibrated anchor cameras and unmanned aerial vehicles (UAVs) to detect 2D and 3D positions of joints, minimizing a 2D re-projection error through triangulation to calibrate cameras extrinsically and intrinsically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional two-dimensional patterns (checkerboard) are used for camera calibration, then accurate calibration results are achieved, but the process becomes time-consuming and requires significant manual effort
Solution Approach 1:
The system uses the human subject themselves as the calibration target, eliminating the need for external calibration patterns. The human joints naturally provide the necessary geometric features for calibration, and the subject can position themselves freely without manual placement of calibration objects
Solution Approach 2:
The patent replaces the mechanical placement of physical calibration patterns with an automated computer vision system that detects human joints through image processing and machine learning algorithms, substituting manual mechanical operations with automated digital processing
2Ease of manufacture
If conventional two-dimensional patterns are used for camera calibration, then calibration can be performed, but the process is prone to point-matching problems
Solution Approach 1:
The patent introduces human joints as an intermediary calibration target that bridges the gap between the camera system and the calibration process. Instead of directly using rigid calibration patterns that cause point-matching issues, the system uses the flexible, naturally occurring human joint structure as a mediator that provides robust feature detection points
Solution Approach 2:
The system changes the calibration target from rigid two-dimensional patterns to three-dimensional human joint positions, transforming the calibration parameters from planar coordinates to spatial coordinates that can be more reliably detected and matched across multiple views
3Quantity of substance
If manual calibration patterns are used, then calibration data can be collected, but significant manual effort is required
Solution Approach 1:
The human subject serves themselves as the calibration target, eliminating the need for operators to manually place or adjust calibration patterns. The subject simply needs to assume certain poses while the system automatically captures and processes the calibration images
Solution Approach 2:
The human subject serves multiple functions simultaneously: they are both the object being captured by the cameras and the calibration target itself. This multi-functionality eliminates the need for separate calibration objects and reduces the overall complexity of the calibration process
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reduces the need for manual calibration patterns and minimizes point-matching errors, providing efficient and accurate camera calibration on UAVs without the time-consuming manual placement of calibration patterns.
Implementation Method 1
The system computes 3D key-points as 3D positions of the human joints based on a triangulation of the determined first set of 2D positions
Implementation Method 2
The system may calibrate each of the group of cameras (i.e. extrinsic and/or intrinsic parameters) with respect to the set of anchor cameras or a gauge) by minimizing a 2D re-projection error between the 3D key-points and the second set of 2D positions
Data Source
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AI summary
A system and method for calibration of cameras on Unmanned Aerial Vehicles (UAVs) is provided. The system receives a set of anchor images of a human subject from a set of anchor cameras and a group of images of the human subject from multiple viewpoints in three-dimensional (3D) space from a group of cameras on a group of UAVs. The system determines a first set of two-dimensional (2D) positions of human joints from the set of anchor images and a second set of 2D positions of the human joints from the group of images. The system computes, as 3D key-points, 3D positions of the human joints based on triangulation using the first set of 2D positions and determines a 2D re-projection error between the 3D key-points and the second set of 2D positions. Thereafter, by minimizing the 2D re-projection error, the system calibrates each camera of the group of cameras.