Camera Calibration Using Binned Reference Images
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Solution Overview
Problem
Conventional camera calibration techniques are cumbersome, requiring manual adjustments and extensive parameter tuning, and lack real-time capabilities, making them inefficient and time-consuming, especially in applications needing rapid and user-friendly calibration processes.
Innovation Solution
An interactive camera calibration method that uses a calibration target with a field of view covering a scene, partitioning the volume of interest into bins and defining angle bins, allowing for real-time image acquisition and assignment of reference images to these bins, enabling efficient intrinsic and extrinsic parameter determination through a dynamic and iterative process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera calibration techniques are used, then accurate intrinsic and extrinsic parameters can be obtained, but the calibration process is time-consuming and requires extensive manual adjustments
Solution Approach 1:
The calibration process is segmented into distinct phases: automatic target detection, feature point extraction, and parameter computation. The volume of interest is partitioned into bins for organized processing of target images. This segmentation automates previously manual steps, reducing calibration time while maintaining accuracy through systematic processing of calibration target images.
Solution Approach 2:
The system performs preliminary actions by automatically detecting and tracking the calibration target in real-time video streams before full calibration computation. Reference images are pre-identified and organized into bins based on target pose, preparing data structures in advance. This preliminary processing reduces the computational burden during actual calibration, enabling rapid parameter determination without sacrificing precision.
2Ease of operation
If conventional calibration methods are used, then comprehensive parameter tuning can be performed, but the process lacks real-time capabilities and user interaction
Solution Approach 1:
The system implements real-time feedback by continuously monitoring the calibration target's position and orientation during image acquisition. The method provides interactive guidance to users, showing which volume bins need more reference images and the current calibration progress. This feedback loop enables users to make informed adjustments in real-time, improving ease of operation while maintaining calibration speed through automated processing of user inputs.
Solution Approach 2:
The calibration system transitions from static, batch-processing methods to dynamic real-time operation. The method continuously acquires target images from video streams, dynamically updates reference image sets, and adapts the calibration process based on real-time target detection. This dynamic approach allows interactive user engagement without compromising calibration productivity, as the system processes information continuously rather than in discrete batches.
3Measurement precision
If manual calibration tasks are performed, then detailed parameter adjustment is possible, but the process becomes cumbersome and complex
Solution Approach 1:
The calibration system performs self-service by automatically detecting the calibration target, extracting feature points, and computing camera parameters without requiring manual intervention for these tasks. The method autonomously organizes reference images into volume bins, tracks target poses, and determines when sufficient data has been collected. This automation maintains measurement precision through systematic processing while dramatically reducing process complexity by eliminating manual calibration steps.
Solution Approach 2:
The system manages parameter complexity by dynamically changing the state of calibration parameters during the process. Intrinsic and extrinsic parameters are computed iteratively as reference images are added to bins, with the system automatically adjusting the number of parameters to be estimated based on the quality and quantity of available data. This parameter adaptation simplifies the overall process by only computing necessary parameters when sufficient evidence is available, maintaining precision without requiring users to manage complex parameter sets manually.
Data Source
AI summary
Calibration methods use a calibration target for obtaining the intrinsic and extrinsic camera parameters of one or more cameras are. The methods can include acquiring, with each camera, a sequence of target images representing the calibration target in different target poses and at different acquisition times. The methods can include identifying reference images from the target images, and defining volume bins, angle bins and multi-camera bins into which the reference images are stored. The reference images can be used to determine the intrinsic and extrinsic parameters of the one or more cameras. In some implementations, the calibration methods can enable a user to monitor the progress of the calibration process, for example by providing an interactive calibration target including an input/output user interface to guide the user in real-time during the acquisition of the target images and/or sensors to provide positional information about the target poses.