Extrinsic Camera Parameter Calibration via Multi-Cabin Reference Imaging
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Solution Overview
Problem
Existing camera calibration methods face challenges in accurately calibrating extrinsic camera parameters due to tooling errors and human adjustments, particularly in dynamic environments like vehicles, where camera locations and angles change, requiring real-time recalibration for precise operations like human-eye sight recognition and gesture recognition.
Innovation Solution
An extrinsic camera parameter calibration method that uses images from a calibration cabin and a reference cabin to determine position-posture change data between cameras, allowing for real-time calibration of extrinsic parameters without the need for calibration apparatuses like checkerboards, ensuring the parameters reflect the current camera state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If camera location and angle are adjusted to adapt to dynamic environments, then adaptability is improved, but extrinsic parameter accuracy deteriorates
Solution Approach 1:
The system performs real-time extrinsic parameter calibration by capturing images at multiple positions and angles, then uses optimization algorithms to compute updated parameters that reflect the current camera state. This dynamic recalibration process allows the camera to adapt to changing positions while maintaining parameter accuracy through continuous optimization rather than relying on static pre-calibrated values.
2Measurement precision
If real-time calibration is performed to maintain parameter accuracy, then measurement precision is improved, but calibration time increases
Solution Approach 1:
The system pre-captures a set of calibration images at multiple known positions and angles before real-time operation. These pre-captured images serve as reference data that can be quickly processed during real-time calibration, reducing the computational burden and time required for parameter optimization while maintaining high accuracy.
Solution Approach 2:
The optimization algorithm uses the pre-captured images and pre-computed projection matrices to rapidly converge to the solution by skipping iterative refinement steps that would otherwise be computationally expensive. This allows real-time calibration to complete quickly while still achieving high precision extrinsic parameters.
3Measurement precision
If multiple images at different positions are used for calibration, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses a single camera that serves multiple functions: capturing calibration images at different positions, serving as the reference camera, and performing real-time object detection. By making the camera multi-functional rather than requiring separate dedicated calibration and operation cameras, the system achieves high calibration precision without significantly increasing overall device complexity.
Solution Approach 2:
The system uses itself for calibration - the camera calibrates itself by capturing images of known objects at multiple positions and using those same images for real-time operation. This self-calibration approach eliminates the need for separate calibration equipment or additional cameras, reducing device complexity while maintaining high measurement precision through multi-position imaging.
Data Source
AI summary
Disclosed are an extrinsic camera parameter calibration method, apparatus and system. The method includes: obtaining a calibration image photographed for a target location in a calibration cabin by a first camera provided therein; determining calibration feature information of the target location from the calibration image; obtaining reference feature information pre-determined from a reference image photographed for a target location in a reference cabin by a second camera provided therein; determining position-posture change data of the first relative to the second camera based on a calibration location of the calibration feature information in the calibration image and a reference location of the reference feature information in the reference image; and determining an extrinsic parameter of the first camera based on the position-posture change data. The extrinsic parameter of the first camera can be calibrated in real time and is closer to a currently actual state of the first camera when used.