Self-Calibrating Camera System Using People as Calibration Markers
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Solution Overview
Problem
Current camera calibration methods require human intervention and are time-consuming, making them costly and inefficient for self-calibration in multi-camera systems.
Innovation Solution
A system that uses people as calibration markers, identifies key points, synchronizes frames using timestamps, and implements automatic re-calibration to reduce human intervention, utilizing multi-camera tracking and neural networks to match and triangulate feature points, with a self-healing scheme for recalibration in case of camera position changes or upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods using reference objects are used, then calibration accuracy can be achieved, but human intervention time and cost increase significantly
Solution Approach 1:
The system enables cameras to perform self-calibration automatically by detecting and tracking moving objects (people) in the scene. The calibration process is initiated and executed by the camera system itself without human intervention, using the natural movement of objects in the environment as calibration targets. This eliminates the need for manual calibration operations while maintaining calibration accuracy through automated feature point detection and matching algorithms.
Solution Approach 2:
The patent introduces moving objects (people) as intermediary calibration targets between the camera system and the calibration process. Instead of requiring direct human intervention with reference objects, the system uses naturally occurring moving objects in the scene as mediators to carry out the calibration function. These intermediaries provide the necessary feature points for calibration while the system automatically processes the calibration data.
2Reliability
If traditional calibration methods are used, then calibration can be performed, but the process is costly and time-consuming
Solution Approach 1:
The system implements continuous calibration by continuously monitoring moving objects in the scene and performing calibration updates in real-time. Rather than performing calibration as a discrete manual operation, the system maintains continuous calibration functionality by constantly tracking feature points on moving objects and adjusting calibration parameters as needed. This continuous process improves productivity while maintaining reliability through ongoing validation.
Solution Approach 2:
The patent replaces manual mechanical calibration operations with automated computer vision-based detection and processing. Instead of physically manipulating reference objects and manually adjusting camera parameters, the system uses neural networks and automated algorithms to detect feature points, track movement, and compute calibration parameters. This substitution of mechanical/manual processes with automated computational processes significantly improves calibration efficiency while maintaining reliability.
3Measurement precision
If feature matching between cameras is performed manually, then calibration accuracy can be maintained, but the complexity of operation increases
Solution Approach 1:
The camera system automatically performs feature point detection and matching between multiple cameras without human intervention. The system independently identifies corresponding feature points on moving objects across different camera views, computes the geometric relationships, and adjusts calibration parameters automatically. This self-service capability maintains high matching accuracy while dramatically simplifying operation to the point of being completely hands-free.
Solution Approach 2:
Moving objects serve as intermediaries that naturally provide the feature matching information needed for calibration. Instead of requiring operators to manually identify and match features, the system uses the moving objects themselves as carriers of feature information. The objects' natural movement and appearance across multiple camera views provide the intermediary data structure that the automated algorithms process to achieve accurate feature matching and calibration.
Data Source
AI summary
The present invention describes a system for calibrating a plurality of cameras in an area. The system method and device functions by using moving objects to calibrate, in particular using people. In addition, the system implements automatic re-calibration in a specific way to reduce human intervention, cost and time.