Camera Calibration via Fixed Object Edge Detection
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Solution Overview
Problem
Vehicle cameras often become misaligned, leading to incorrect guidance information and images, as they move or become recalibration is not efficiently addressed by existing technologies.
Innovation Solution
A method and apparatus that detect misalignment by analyzing images of fixed objects, such as vehicle beds or bumpers, by calculating pixel distances and determining affine movements, and recalibrate the camera through affine transformations and re-cropping of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera calibration is performed manually or through complex automated procedures, then calibration accuracy is improved, but system complexity and time consumption increase
Solution Approach 1:
The system performs self-calibration by automatically detecting edges of fixed objects in images, calculating pixel distances, determining affine transformations, and adjusting camera parameters without external intervention. The processor executes calibration algorithms autonomously, eliminating the need for manual calibration operations while maintaining high accuracy through automated geometric analysis.
Solution Approach 2:
The system stores reference information about fixed objects (vehicle beds, bumpers) including their geometric characteristics and expected positions in advance. During operation, the system compares real-time detected edge positions against these pre-stored references to quickly determine calibration status and perform corrections without requiring full recalibration procedures.
2Reliability
If frequent camera calibration checks are performed, then calibration accuracy is maintained, but processing time and computational resources increase
Solution Approach 1:
Instead of performing complete calibration procedures frequently, the system executes lightweight calibration checks by detecting only critical features (edge midpoints and key points on fixed objects). The system calculates only necessary pixel distances and performs partial affine transformations when needed, maintaining calibration reliability with minimal processing overhead compared to full calibration routines.
3Measurement precision
If manual calibration procedures are used, then calibration can be performed, but ease of operation deteriorates due to complex manual adjustment requirements
Solution Approach 1:
The system replaces manual mechanical calibration adjustments with automated image processing and computational geometry algorithms. The processor automatically detects edges, calculates positions, determines affine transformations, and applies calibration corrections through software-based image transformation and re-cropping, eliminating the need for physical camera adjustments while maintaining precision.
Data Source
AI summary
A method and apparatus for calibrating a camera are provided. The method includes: detecting at least one edge of fixed object in image received from a camera, determining at least one horizontal midpoint of the at least one edge, calculating a first pixel distance between the determined at least one horizontal midpoint and a pre-stored at least one horizontal midpoint, calculating second pixel distances between a plurality of points on the detected at least one edge of the fixed object and a pre-stored plurality of points of the at least one edge of the fixed object, determining whether to recalibrate the camera based on the first pixel distance and the second pixel distances, and recalibrating the camera if the determining determines to recalibrate the camera.