Automated Camera Calibration Using Neural Network Parameter Estimation
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Solution Overview
Problem
Existing camera calibration methods are impractical for outdoor scenes and broadcast sport videos due to the difficulty in detecting calibration patterns, susceptibility to noise and distortion, and the accumulation of errors over time, especially when camera parameters are constantly changing.
Innovation Solution
A method for calibrating and re-calibrating imaging devices using 2D or 3D shapes with known geometry, involving an initial estimation of camera parameters and a parameter adjustment operation based on mutual information and geometric transformation estimation, which iteratively aligns images with a template to minimize disparity and update parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration patterns are used for outdoor cameras, then camera parameters can be estimated, but the pattern size becomes impractically large (meters scale) and detection becomes unreliable
Solution Approach 1:
The patent replaces the mechanical/optical calibration pattern detection approach with a machine learning-based direct estimation approach. Instead of using physical calibration patterns that require detection and correspondence matching, the system uses a trained neural network to directly estimate camera parameters from images, eliminating the need for large-scale physical patterns and their detection challenges
Solution Approach 2:
The patent uses synthetic training images that copy the visual characteristics of real outdoor scenes to train the estimation model. These synthetic images contain ground truth camera parameters and are used to teach the system to estimate parameters from real images without requiring physical calibration patterns in the deployment scenario
2Measurement precision
If feature correspondence methods are used, then camera parameters can be estimated, but the system becomes susceptible to noise, blur, distortion, and illumination variations
Solution Approach 1:
The patent replaces the fragile feature correspondence mechanism with a robust machine learning model. Instead of detecting and matching features that are sensitive to noise and distortion, the neural network directly maps image content to camera parameters, leveraging learned invariances to illumination variations, blur, and distortion that make the system more reliable
Solution Approach 2:
The patent changes the approach from discrete feature parameter matching to continuous parameter estimation. The system estimates camera parameters directly as continuous values from the image data, avoiding the discrete feature detection and matching steps that are vulnerable to image quality degradation
3Measurement precision
If standard calibration algorithms are used, then camera parameters can be estimated from calibration patterns, but the correspondence identification becomes challenging and error-prone
Solution Approach 1:
The patent replaces the correspondence identification mechanism with a direct estimation neural network. Instead of detecting features and establishing correspondences between image points and world points, the system uses a trained model to directly predict camera parameters from the image, eliminating the correspondence identification step entirely
Solution Approach 2:
The patent extracts and removes the correspondence identification step from the calibration process. By using a machine learning model that directly estimates parameters from images, the system extracts only the essential function (parameter estimation) while removing the problematic intermediate step of finding and matching correspondences
4Ease of operation
If holistic image alignment techniques are used, then camera parameters can be estimated without point landmarks, but the algorithm requires an initial point close to the actual solution to achieve convergence
Solution Approach 1:
The patent performs preliminary training action by training a neural network on synthetic data with ground truth parameters before deployment. This preliminary training equips the model with the ability to provide accurate initial estimates and converge to correct solutions without requiring manual initialization close to the actual parameters
Solution Approach 2:
The patent enables the system to self-initialize and self-correct by using the neural network to provide its own accurate initial parameter estimates. The model serves itself by generating reliable starting points for optimization and correcting its own estimates through the learned mapping from image to parameters
Data Source
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AI summary
A system and method are provided for calibrating and re-calibrating an imaging device. The image device comprises at least one varying intrinsic or extrinsic parameter value based on a view of 3D shapes and a 2D template therefor. The method and system are operable to receive a sequence of images from the imaging device, wherein a planar surface with known geometry is observable from the sequence of images; perform an initial estimation of a set of camera parameters for a given image frame; perform a parameter adjustment operation to minimize a dissimilarity between the template and the sequence of images; perform a parameter adjustment operation to maximize similarities in either the camera's coordinate system or the template's coordinate system; and update an adaptive world template to incorporate an additional stable point from the observed images to the template.