Multi-Camera Calibration Using Vehicle Bounding Box Centers
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Solution Overview
Problem
Current sensing systems face challenges in accurately determining the location and orientation of objects in a traffic scene, particularly when using multiple cameras with overlapping fields of view, as they require complex camera localization and synchronization, which can be time-consuming and costly, and often rely on additional equipment or user intervention.
Innovation Solution
A method is disclosed that involves determining the center points of bounding boxes in images from multiple cameras, setting up non-linear equations based on these points and camera parameters, and solving them using Gauss-Newton iteration or the Levenberg-Marquardt algorithm to establish the six degree of freedom pose of cameras and objects, allowing for improved localization and object tracking without additional equipment or user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex camera localization and synchronization methods are used, then measurement precision of object location is improved, but device complexity and cost increase
Solution Approach 1:
The system uses the traffic scene itself and naturally occurring moving objects as calibration targets, eliminating the need for external calibration equipment. The moving objects serve their dual purpose of being traffic participants and calibration references, making the system self-sufficient and reducing external dependencies.
Solution Approach 2:
A shared global coordinate frame is introduced as an intermediary mathematical construct that enables integration of data from multiple cameras without requiring physical alignment equipment. This virtual coordinate system mediates between different camera perspectives and facilitates precise object location computation.
2Measurement precision
If complex camera localization and synchronization methods are used, then measurement precision of object location is improved, but loss of time increases
Solution Approach 1:
The system performs camera calibration continuously in the background using ongoing traffic scene data, rather than requiring a separate preliminary calibration step. This allows calibration to occur preliminarily and continuously without interrupting normal operation or requiring dedicated calibration time.
Solution Approach 2:
The calibration process operates continuously using the steady stream of traffic scene images and moving objects, transforming an intermittent calibration task into a continuous useful action that occurs alongside normal traffic monitoring and object tracking operations.
3Measurement precision
If additional equipment is used for camera localization, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The traffic scene and moving objects serve as self-provided calibration resources, eliminating the need for external calibration equipment. The system leverages naturally occurring elements in the environment rather than requiring additional specialized devices.
Solution Approach 2:
Moving traffic objects serve multiple functions simultaneously: they are both participants in the traffic scene being monitored and calibration references for camera localization. This multi-functionality eliminates the need for separate calibration targets or equipment.
4Measurement precision
If user intervention is required for calibration, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs calibration autonomously using algorithms that automatically detect moving objects and compute camera parameters without human intervention. The calibration process serves itself by automatically utilizing traffic scene data and computing necessary transformations.
Solution Approach 2:
The system continuously monitors traffic scenes and uses feedback from detected moving objects to automatically adjust and refine camera calibration parameters. This closed-loop feedback mechanism enables self-correction and maintains accuracy without manual intervention.
Data Source
AI summary
A first plurality of center points of first two-dimensional bounding boxes corresponding to a vehicle occurring in a first plurality of images acquired by a first camera can be determined. A second plurality of center points of second two-dimensional bounding boxes corresponding to the vehicle occurring in a second plurality of images acquired by a second camera can also be determined. A plurality of non-linear equations based on the locations of the first and second pluralities of center points and first and second camera parameters corresponding to the first and second cameras can be determined. The plurality of non-linear equations can be solved simultaneously for the locations of the vehicle with respect to the first and second cameras and the six degree of freedom pose of the second camera with respect to the first camera.