Automatic Camera Calibration Using Known Object Dimensions
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Solution Overview
Problem
Existing camera calibration methods are either manual and error-prone, requiring significant human effort, or automated but limited by assumptions about vehicle movements and occlusions, and struggle with low-resolution images.
Innovation Solution
A method that uses known physical dimensions of common objects, such as vehicles, to compute multiple camera calibrations, filters out outliers based on orientation and displacement filters, and derives a final calibration without requiring accurate vehicle recognition, enabling calibration even with low-resolution images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration using real-world coordinates of known landmarks is used, then camera parameters can be derived, but tremendous amounts of human effort are required and it needs to be re-performed if camera parameters change
Solution Approach 1:
The system performs automatic calibration by having the camera capture images of objects with known dimensions, and the processing unit automatically computes calibration parameters from these images without requiring manual landmark coordinate input or repeated calibration procedures when camera parameters change
Solution Approach 2:
Instead of using manual landmark coordinates, the system uses images of objects with known dimensions as proxies, where the object dimensions serve as reference standards to derive camera parameters automatically from the captured image data
2Extent of automation
If automated calibration using vehicle movements and landmarks is used, then human effort is reduced, but the method is limited by assumptions about vehicle movements and occlusions
Solution Approach 1:
The method extracts only the essential calibration information from images of objects with known dimensions, removing the need for assumptions about vehicle movements, occlusions, or landmark tracking, thereby achieving both automation and broad applicability across different scenarios
Solution Approach 2:
The calibration method using objects with known dimensions is universally applicable regardless of camera type, scene conditions, or object motion patterns, making the automated calibration versatile and adaptable to various deployment scenarios without requiring scenario-specific assumptions
3Extent of automation
If automated calibration using vehicle recognition is used, then human effort is reduced, but it struggles with low-resolution images
Solution Approach 1:
The method uses objects with known dimensions as disposable reference standards that can be clearly identified in low-resolution images, where the known dimensions provide sufficient calibration information without requiring high-resolution vehicle recognition or tracking
Data Source
AI summary
This document relates to camera calibration. One example uses real-world distances and image coordinates of object features in images to determine multiple candidate camera calibrations for a camera. This example filters out at least some of the multiple candidate camera calibrations to obtain remaining calibrations, and obtains a final calibration for the camera from the remaining calibrations