Camera Calibration with Sequential 3D Light Sources and ML Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera calibration methods, particularly for large-scale scenes, are time-consuming, resource-intensive, or lack precision due to manual interventions and image quality issues, making them unsuitable for multi-camera setups.
Innovation Solution
A machine learning-based method using non-linearly arranged light sources with sequential illumination to determine 2D and 3D coordinates, eliminating the need for manual interventions and improving accuracy by using an object detection model to construct a projective relation for camera calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods (DLT or Zhang's method) are used for camera calibration, then calibration can be performed, but the process becomes time-consuming and labor-intensive, especially in multi-camera scenarios
Solution Approach 1:
The system uses automatic feature detection and matching algorithms that autonomously identify corresponding points between images without manual intervention. The calibration process self-performs through automated computation of projection matrices and parameter estimation, eliminating the need for manual point identification while maintaining calibration accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes (physically identifying and marking corresponding points) with automated computational methods. Machine learning models and algorithmic feature detection substitute for human operators, enabling rapid automated calibration that maintains precision while dramatically reducing time consumption.
2Measurement precision
If more than 6 point-pairs are used in DLT method to mitigate noise effects, then calibration accuracy improves, but the complexity and time required for manual identification increases
Solution Approach 1:
The automated system automatically identifies and matches sufficient feature points without requiring manual selection. The algorithm autonomously determines the optimal set of corresponding points, eliminating the complexity of manual point selection while ensuring adequate sample size for accurate calibration.
Solution Approach 2:
The patent changes the approach from manual parameter selection (choosing specific point pairs) to automated parameter estimation through algorithmic processing. The system automatically adjusts and optimizes the set of corresponding points based on image content and geometric constraints, reducing operational complexity while maintaining accuracy.
3Measurement precision
If Zhang's method with calibration boards is used for large-scale scenes, then calibration can be performed, but the accuracy heavily depends on image quality and corner detection becomes problematic with large boards
Solution Approach 1:
The patent introduces an intermediary feature detection and matching mechanism that bridges the gap between 3D world coordinates and 2D image coordinates. Instead of relying directly on corner detection of calibration boards, the system uses intermediate feature points and automated matching algorithms to establish correspondences, overcoming the limitations of direct corner detection in large-scale scenes.
Solution Approach 2:
The system replaces manual corner detection methods with automated machine learning-based feature detection. This substitution enables reliable identification of corresponding points even in large-scale scenes where traditional corner detection fails, maintaining calibration accuracy without being constrained by image quality or board size.
4Ease of manufacture
If self-calibration is used without known object dimensions, then calibration can be performed without calibration objects, but the method yields parameters with scale factor and cannot directly calculate real-world dimensions
Solution Approach 1:
The patent introduces an intermediary calibration approach that combines the ease of self-calibration with the ability to obtain absolute scale information. The system uses a calibrated reference frame or known 3D points as an intermediary to bridge the gap between relative and absolute measurements, enabling both easy setup and accurate real-world dimension calculation.
Solution Approach 2:
The calibration method is designed to be universally applicable to various scene scales and configurations. The same automated system can handle both close-range and large-scale calibration, providing both the ease of self-calibration and the accuracy of absolute measurement through adaptive feature matching and parameter estimation that works across different scale ranges.
Data Source
AI summary
The present invention provides a machine learning-based method for calibrating a camera with respect to a scene, comprising: setting up, in the scene, light sources including: a first group of light sources arranged in a non-linear manner on a first plane and a second group of light sources arranged in a non-linear manner on a second plane orthogonal to the first plane; labelling each light source with a serial number; determining 3D global coordinates of each light source in the scene; configuring the light sources to luminate sequentially; configuring the camera to capture a video of the scene when the light sources sequentially luminate; extracting 2D pixel coordinates of each light source from the video using object detection machine-learning model; matching 3D global coordinates for each light source with respective 2D pixel coordinates to construct a projective relation; and obtaining a projection matrix of the camera from the projective relations.