Camera Auto-Calibration Using Seed Reference and Physical Marker
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Solution Overview
Problem
Manual calibration of multiple cameras in large facilities is labor-intensive and inefficient, especially when there are over 100 cameras involved.
Innovation Solution
A distributed camera system that uses a seed camera to calibrate neighboring cameras automatically by detecting a physical marker with predefined characteristics, allowing for the computation of calibration information and reducing the need for manual calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is performed for each camera in a large facility with over 100 cameras, then calibration accuracy can be ensured, but the labor intensity and time consumption increase significantly
Solution Approach 1:
The calibration process is segmented into two distinct phases: an initial manual calibration for a reference camera, and subsequent automatic calibration for all other cameras using the reference camera as a seed. This segmentation allows the time-consuming manual process to be performed only once, while the majority of cameras are calibrated automatically, thereby resolving the contradiction between calibration accuracy and time consumption.
Solution Approach 2:
The system performs preliminary manual calibration on one reference camera before deploying automatic calibration to all other cameras. This preliminary action establishes the foundation for subsequent automatic calibration processes, enabling the system to achieve both high accuracy (through the initial careful manual calibration) and efficiency (through the automated propagation to remaining cameras).
2Measurement precision
If manual calibration is performed for each camera in a large facility with over 100 cameras, then calibration quality can be maintained, but the labor intensity increases significantly
Solution Approach 1:
The system implements self-service calibration where the reference camera serves as a seed to automatically calibrate all other cameras in the network. Once the initial manual calibration is complete, the system performs self-calibration for remaining cameras by detecting physical markers and computing calibration information automatically, thereby dramatically reducing labor intensity while maintaining calibration quality through the established reference framework.
Solution Approach 2:
A physical marker with predefined characteristics acts as an intermediary between the reference camera and other cameras during calibration. The marker enables automatic detection and computation of calibration information, serving as a mediator that transfers calibration data from the reference camera to other cameras without requiring direct manual intervention for each camera, thus reducing labor intensity while preserving calibration quality.
3Productivity
If automatic calibration using a seed camera is implemented, then labor intensity is reduced, but the system complexity increases
Solution Approach 1:
The physical marker with predefined characteristics serves as a simple intermediary that enables automatic calibration without requiring complex inter-camera communication or coordination protocols. The marker's predefined characteristics allow for straightforward detection and processing, maintaining system simplicity while enabling automatic calibration and improving productivity.
Solution Approach 2:
The system uses the reference camera as a template or copy source for calibrating other cameras. By replicating the calibration process from the reference camera to other cameras through marker detection and coordinate computation, the system achieves automatic calibration with relatively simple logic, balancing productivity improvement with acceptable system complexity.
4Loss of time
If automatic calibration is implemented for multiple cameras, then calibration time is reduced, but measurement precision may be compromised
Solution Approach 1:
The physical marker with predefined characteristics acts as a precise intermediary reference point that enables automatic calibration to achieve accuracy comparable to manual calibration. The marker's known properties allow the system to compute accurate calibration information automatically, thereby reducing calibration time without compromising measurement precision.
Solution Approach 2:
The system replaces the manual mechanical calibration process with an automatic computational process based on marker detection and coordinate geometry. This substitution uses computer vision and mathematical computation instead of manual adjustment, achieving both time reduction and maintained precision through algorithmic accuracy.
Data Source
AI summary
A seed camera disposed a first location is manually calibrated. A second camera, disposed at a second location, detects a physical marker based on predefined characteristics of the physical marker. The physical marker is located within an overlapping field of view between the seed camera and the second camera. The second camera is calibrated based on a combination of the physical location of the physical marker, the first location of the seed camera, the second location of the second camera, a first image of the physical marker generated with the seed camera, and a second image of the physical marker generated with the second camera.