Single Camera FOV Calibration via Ray-Based Coordinate Matrix
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Solution Overview
Problem
Current camera calibration methods do not effectively calibrate the field of view (FOV) of a single camera, limiting the ability to accurately measure object dimensions in video streams and preventing the use of Reverse Projection for object measurement tasks.
Innovation Solution
A method for calibrating the FOV of a single camera involves setting fixed installation parameters, dividing the FOV into sectioned rays, capturing images while a calibration board moves along these rays, processing the images to digitize coordinates, creating a coordination matrix, and performing extrapolation to generate a virtual grid for aligning with objects in a video stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera calibration methods are used, then image quality and focus are improved, but field of view calibration and depth perspective are not achieved
Solution Approach 1:
The field of view is divided into multiple sectioned rays radiating from the camera center, with calibration performed along each ray independently. This segmentation allows precise measurement of depth and angle relationships without losing spatial information.
Solution Approach 2:
The calibration process extends from traditional 2D image plane calibration to 3D spatial calibration by moving the calibration board along rays in depth and angle, capturing coordinates at multiple locations to build a coordination matrix that preserves depth perspective.
2Measurement precision
If multiple cameras are used for object size measurement, then measurement accuracy is improved, but system complexity and cost increase
Solution Approach 1:
A single camera performs all calibration and measurement functions by itself through the ray-based coordination matrix method, eliminating the need for multiple cameras while maintaining measurement accuracy through mathematical extrapolation.
Solution Approach 2:
The system changes the calibration approach from requiring multiple simultaneous camera views to using a single camera with extended calibration along multiple rays at different depths and angles, allowing the same measurement capability with fewer devices.
3Measurement precision
If Reverse Projection method is applied without FOV calibration, then object identification is possible, but accurate dimension extraction cannot be achieved
Solution Approach 1:
The field of view is pre-calibrated by establishing a coordination matrix along sectioned rays before actual measurement operations. This preliminary calibration enables accurate dimension extraction during Reverse Projection without adding complexity to the operational workflow.
Data Source
AI summary
A method for calibrating an active FOV of a single camera, wherein from the calibration of the cameras active FOV, a coordinate matrix is obtained which remotely produces a virtual interpolation measurement network at any point within an image (a frame) extracted from a video stream (recorded by the single camera), while eliminating the need to be physically located at the actual location where the video stream has been recorded. According to an embodiment of the invention, the basis of the active FOV of a camera is the ability to obtain (measure) coordinates of the measurement points marked on a calibration board.