Fisheye Camera Calibration Using Arc Edge Segments
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Solution Overview
Problem
Fisheye cameras introduce severe radial distortion, which complicates video analytics tasks like face detection, especially near the borders of the monitored area, as traditional algorithms perform poorly due to twisted representations of faces.
Innovation Solution
A method that captures images, detects arc-shaped edge segments, estimates the main distortion parameter by fixing a distortion centerpoint in the image, and inverts the distortion model to obtain an undistorted version, minimizing radial distortion by optimizing the area between circular arcs and their corresponding chords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If fisheye cameras are used to monitor the whole scene, then the number of cameras required is reduced, but radial distortion causes severe twisting near borders that degrades video analytics performance
Solution Approach 1:
The patent applies preliminary action by performing camera calibration and distortion correction before video analytics processing. The system pre-computes undistorted images from fisheye camera footage using detected arc-shaped features and distortion models, so that subsequent analytics operations work on corrected imagery rather than distorted raw footage.
Solution Approach 2:
The patent introduces an intermediary processing step between image capture and analytics. A calibration module acts as a mediator that detects arc-shaped edge segments, estimates distortion parameters, and generates corrected images. This intermediary layer transforms the distorted fisheye output into a form suitable for traditional analytics algorithms.
2Device complexity
If traditional face detection algorithms are applied to fisheye images, then the detection process is simple, but the algorithms perform poorly due to radial distortion twisting faces near borders
Solution Approach 1:
The patent converts the harmful radial distortion into a beneficial feature by detecting arc-shaped edge segments caused by the distortion. The system uses the characteristic arc patterns produced by fisheye lenses as positive identification markers for calibration, transforming what was previously a degradation into a useful signal for determining distortion parameters.
Solution Approach 2:
The patent changes the parameter space by estimating distortion parameters (such as distortion coefficients and center points) from the detected arc features. By adjusting and optimizing these parameters through the calibration process, the system transforms the distorted image parameters into corrected parameters that restore proper geometric relationships for reliable face detection.
3Measurement precision
If camera calibration is performed to compensate for radial distortion, then video analytics accuracy is improved, but the processing complexity and time increase
Solution Approach 1:
The patent extracts only the essential calibration information needed for distortion correction by focusing on detecting arc-shaped edge segments rather than performing full camera calibration. This selective extraction of relevant features (arcs caused by radial distortion) simplifies the calibration process while maintaining effectiveness for fisheye-specific distortion correction.
Solution Approach 2:
The patent applies partial action by implementing a simplified calibration approach that addresses only the radial distortion component rather than performing complete intrinsic and extrinsic parameter calibration. The system performs sufficient calibration to correct the dominant distortion effect without the overhead of comprehensive calibration procedures.
Data Source
AI summary
A computer-implemented method executed by at least one processor for reducing radial distortion errors in fish-eye images is presented. The method includes capturing an image from a camera including distortions, detecting arc-shaped edge segments in the image including the distortions, estimating a main distortion parameter by fixing a distortion centerpoint in a middle of the image, estimating the distortion centerpoint with the main distortion parameter, and obtaining an undistorted version of the captured image by inverting the distortion model.