UAV Camera Calibration Using Human Joint Reprojection Error
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional camera calibration methods for UAVs are time-consuming and prone to the point-matching problem, requiring significant manual effort and the use of 2D patterns like checkerboards.
Innovation Solution
A system and method for calibrating cameras on UAVs using human joints, where pre-calibrated anchor cameras capture images of a human subject, and a machine learning model determines 2D and 3D positions of joints to calibrate the cameras by minimizing 2D re-projection error, eliminating the need for manual pattern placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 2D pattern methods (e.g., checkerboard) are used for camera calibration, then calibration accuracy can be 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 body naturally provides the necessary geometric features (joints as keypoints) for calibration, making the system self-sufficient and removing manual pattern placement steps
Solution Approach 2:
The patent replaces the mechanical/manual process of placing and adjusting physical calibration patterns with an automated computer vision system that detects human joints using machine learning models, thereby eliminating manual effort and reducing calibration time
2Measurement precision
If conventional 2D pattern methods are used for camera calibration, then calibration can be performed, but the process is prone to point-matching problems and requires manual effort
Solution Approach 1:
The human subject serves as their own calibration target, providing inherent geometric structure through body joints. This eliminates the need for operators to manually place or adjust external patterns, significantly reducing operational complexity and manual effort
Solution Approach 2:
The system transitions from detecting artificial 2D pattern points to detecting natural 3D human joint positions. This parameter change from artificial 2D coordinates to natural 3D anatomical landmarks provides more robust and unambiguous correspondence points for calibration
3Productivity
If 2D patterns are used for calibration, then the process can be completed, but it lacks automation and requires significant human intervention
Solution Approach 1:
The patent replaces manual mechanical operations with automated computational processes. Machine learning models automatically detect human joints, and algorithms automatically compute calibration parameters, eliminating the need for human operators to manually place patterns or perform calculations
Solution Approach 2:
The system is entirely self-operating: it automatically captures images, detects joints, computes 3D positions, and determines calibration parameters without human intervention. The human subject merely needs to pose, and the system handles all subsequent processing automatically
Data Source
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.