Multi-Camera Self-Calibration for 3D Object Tracking
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Solution Overview
Problem
Existing systems for tracking moving objects with multiple cameras require manual or semi-automatic calibration, which is time-consuming and costly, and lack efficient methods for establishing accurate relationships between overlapping camera fields of view.
Innovation Solution
A self-calibration system that automatically determines the topology of a camera network using a server to align observations from multiple nodes, employing methods like RANSAC and global optimization to refine camera mappings and establish a common coordinate frame, enabling seamless tracking across overlapping areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is used to establish relationships between multiple cameras, then measurement precision of object locations is improved, but loss of time and installation cost increase
Solution Approach 1:
The system performs self-calibration by automatically determining camera relationships and coordinate transformations without human intervention. The calibration process uses detected object locations from multiple cameras to compute transformation matrices autonomously, eliminating the need for manual calibration operations while maintaining measurement precision.
Solution Approach 2:
The patent replaces manual mechanical calibration procedures with an automated computational system. Instead of physically adjusting camera positions and manually establishing coordinate relationships, the system uses algorithmic processing of location data to determine transformations, substituting mechanical/manual operations with information processing.
2Manufacturing precision
If manual calibration is performed to establish camera relationships, then manufacturing precision of system setup is improved, but ease of manufacture deteriorates
Solution Approach 1:
The system automatically determines camera coordinate relationships and field of view transformations through self-calibration algorithms. The installation process does not require skilled technicians to perform manual calibration procedures, as the system autonomously computes the necessary transformation matrices from observed object locations.
Solution Approach 2:
Manual calibration operations are replaced with automated computational procedures. The system uses processing power and algorithms to establish precise camera relationships, replacing the need for manual mechanical adjustment and expert intervention during installation.
3Reliability
If multiple cameras with overlapping fields of view are used to monitor an area, then reliability of object tracking is improved, but device complexity increases
Solution Approach 1:
The patent merges data from multiple cameras by establishing coordinate transformations that map all camera views to a common reference frame. This integration allows the system to treat multiple camera inputs as a unified monitoring system, improving tracking reliability across camera boundaries while managing complexity through systematic data fusion.
Solution Approach 2:
The system creates a universal coordinate system that serves as a common reference frame for all cameras. This universal framework allows any camera to contribute to tracking objects in any region of the monitored area, making the system versatile and reliable while providing a structured approach to handling multiple inputs.
Data Source
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AI summary
An apparatus for controlling a plurality of imaging sensor nodes producing 3D structure of a scene is provided. The apparatus receives (500) location data from the sensor nodes, the location data indicating the locations of the moving objects, compares (502) the location data received from different sensor nodes at the same time instants with each other and determines (504) which detections of different sensor nodes relate to same moving objects. The apparatus further maps (600) the location data received from different sensor nodes to a common coordinate system and determines (602) the relationships of the fields of view of the sensor nodes with each other and the location data mapped to the common coordinate system.