Automatic Camera Recalibration in Optical Motion Capture Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Camera calibration in optical motion capture systems is not timely updated when internal or external camera parameters change, leading to potential inaccuracies and reduced system fluency.
Innovation Solution
An automatic camera calibration method that periodically determines the need for recalibration by comparing projection points from other cameras with collection points from the camera in question, calculates distance differences, and updates calibration parameters using a gradient descent method to maintain optimal camera settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera calibration is performed manually and completely restarted, then calibration accuracy can be ensured, but system operating fluency is significantly reduced and time is wasted
Solution Approach 1:
The system implements periodic calibration by monitoring environmental parameters (temperature, humidity, vibration) and triggering calibration only when parameter deviations exceed predefined thresholds, rather than performing continuous or manual calibration. This periodic approach maintains calibration accuracy while preserving system operating fluency during normal operation.
Solution Approach 2:
The system establishes a feedback mechanism that continuously monitors camera parameter changes and environmental conditions, compares them against reference values, and automatically triggers recalibration when deviations indicate calibration degradation. This feedback-driven approach ensures calibration accuracy is maintained only when necessary, avoiding unnecessary interruptions to system operation.
2Measurement precision
If camera calibration is performed frequently to maintain accuracy, then measurement precision is improved, but time consumption and system interruption increase
Solution Approach 1:
The system transitions from frequent or manual calibration to condition-based periodic calibration, where recalibration is triggered only when environmental parameter deviations (temperature, humidity, vibration) exceed predetermined thresholds. This approach minimizes calibration time consumption while maintaining measurement precision by calibrating only when actually needed.
Solution Approach 2:
The system monitors changes in environmental parameters (temperature, humidity, vibration) and camera parameters, using these parameter variations as triggers for recalibration. By linking calibration timing to actual parameter changes rather than fixed schedules, the system reduces unnecessary calibration time while maintaining accuracy when conditions warrant it.
3Measurement precision
If complete system recalibration is performed, then all camera parameters are updated ensuring accuracy, but system complexity and processing requirements increase
Solution Approach 1:
The system segments the calibration process by identifying and recalibrating only the specific camera or cameras affected by environmental changes, rather than performing complete system recalibration. This segmentation reduces calibration process complexity and processing requirements while maintaining overall system accuracy through targeted updates.
Solution Approach 2:
The system applies local quality by performing calibration only on the specific camera or cameras whose parameters have deviated beyond thresholds, rather than uniformly recalibrating all cameras in the system. This localized approach reduces the complexity and computational burden of the calibration process while maintaining measurement precision where it is actually needed.
Data Source
AI summary
A camera automatic calibration method includes determining a camera to be recalibrated; and determining a point-to-point matching relationship between a first projection point set and a first collection point set. The first projection point set is formed in a camera coordinate system when first spatial coordinates, formed by capturing a plurality of test points using cameras other than the camera to be recalibrated, are projected onto the camera to be recalibrated, and the first collection point set is formed by capturing the test points using the camera to be recalibrated. The method includes calculating a distance difference between each point in the first projection point set and a matching point in the first collection point set according to the point-to-point matching relationship; obtaining a new calibration parameter of the camera to be recalibrated; and updating the current calibration parameter of the camera to be recalibrated with the new calibration parameter.