Multi-Camera Image Parameter Calibration Using Skeletal Features
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Solution Overview
Problem
Conventional image collection device calibration processes are complex, requiring manual adjustment, professional knowledge, and high computing power to determine extrinsic and intrinsic parameters, making them inefficient and difficult to operate.
Innovation Solution
A method and device that automatically determine image adjustment parameters using skeletal features extracted from collected images, allowing for automatic calibration and free viewpoint video generation without the need for manual adjustments or high computing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment and professional calibration knowledge are used to determine extrinsic and intrinsic parameters, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent replaces manual mechanical adjustment operations with automated computer vision technology. The system uses image processing algorithms to automatically detect feature points and calculate calibration parameters, substituting the mechanical manual adjustment process with an automated digital system that eliminates the need for professional calibration knowledge while maintaining precision
Solution Approach 2:
The calibration system performs self-calibration by automatically capturing images, detecting feature points, and computing extrinsic and intrinsic parameters without human intervention. The device serves itself by using its own imaging system to determine its calibration parameters, eliminating the need for external professional calibration services
2Measurement precision
If manual adjustment operations are performed to obtain collected images meeting requirements, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary automated image capture and feature point detection before formal calibration. By pre-processing images and automatically identifying feature points in advance, the system eliminates the need for repeated manual adjustments during calibration, thereby improving both image quality and calibration efficiency simultaneously
Solution Approach 2:
Manual iterative adjustment operations are replaced with automated image processing and parameter calculation algorithms. The system uses computer vision to automatically evaluate image quality and adjust parameters, replacing the slow manual trial-and-error process with rapid automated computation that maintains precision while dramatically improving productivity
3Measurement precision
If high computing power is used for parameter calculation, then measurement precision is improved, but use of energy deteriorates
Solution Approach 1:
The system applies partial computation by focusing calculation resources only on critical feature points and essential parameter calculations. Instead of processing entire images or performing exhaustive computations, the algorithm selectively processes key feature regions and calculates only the necessary extrinsic and intrinsic parameters, reducing energy consumption while maintaining calculation precision
Data Source
AI summary
A parameter determining method for a control device and a related device are disclosed, to automatically determine an image adjustment parameter of a collection device. The method includes: controlling poses of a plurality of pan-tilt-zooms, causing lenses of N collection devices on the plurality of pan-tilt-zooms to point to a target subject in a target area, where the N collection devices are arranged around the target area, and N is a positive integer greater than 1; obtaining N images, where each of the N images is a frame of image collected by each of the N collection devices for the target subject; and determining image adjustment parameters of the N collection devices based on skeletal features extracted from the N images, and the image adjustment parameters are used to obtain the frame image of the free viewpoint video.