Camera Calibration Method Using Reference Points for Precision Positioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera calibration methods are cumbersome and lack accuracy in determining physical position coordinates for objects within a camera's field of view, particularly in smart city applications where high precision and depth coverage are required, such as in cellular vehicle to everything (C-V2X) systems.
Innovation Solution
A method that involves selecting physical reference points within a defined region of interest, obtaining their pixel locations from camera image data, and deriving a relationship to transform pixel locations into corresponding physical position coordinates, using a processor to execute machine-readable instructions for calibration and data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera calibration methods are used, then the calibration process can be completed, but the accuracy in determining physical position coordinates is poor and the process is time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-establishing a coordinate transformation relationship between the camera coordinate system and the real-world coordinate system using known reference points with predetermined coordinates. This pre-calibration process creates a transformation model that can be directly applied during operation, eliminating the need for time-consuming real-time calibration while ensuring accurate position determination.
Solution Approach 2:
The patent uses reference points with known coordinates as intermediaries to establish the relationship between pixel coordinates and physical position coordinates. These reference points serve as a bridge between the camera's image space and the real-world coordinate system, enabling accurate transformation without direct measurement during operation.
2Measurement precision
If depth cameras are used to improve positioning accuracy, then measurement precision improves, but the range is limited to 20m or less
Solution Approach 1:
The patent transitions from relying on depth camera measurements in 3D space to using 2D image plane coordinates combined with a pre-established coordinate transformation model. By projecting 3D world coordinates onto the 2D image plane and creating a transformation relationship, the system achieves accurate positioning over extended ranges without being constrained by depth camera limitations.
3Area of stationary object
If surveillance cameras mounted at heights of 6-8 metres are used, then coverage area increases, but manual calibration becomes difficult
Solution Approach 1:
The patent enables self-service calibration by using reference points with known coordinates that can be automatically processed through the coordinate transformation model. The system performs its own calibration by comparing observed reference points in the image with their predetermined coordinates, eliminating the need for manual intervention even when the camera is mounted at difficult-to-reach heights.
Data Source
AI summary
Described is a method of calibrating a camera. The method comprises defining an area observed in a field of view (FOV) of the camera as a region of interest (ROI). The method includes selecting a plurality of physical reference points in said ROI and obtaining for each reference point physical position coordinates. The method also includes obtaining from said camera FOV or camera image data pixel locations for each point of reference and pairing each pixel location with the physical position coordinates of its respective reference point. Then, a relationship is derived from the paired pixel locations and physical position coordinates to enable a selected or identified pixel location in the camera FOV or camera image data to be transformed to physical position coordinates for a corresponding physical location in said ROI.