Camera Calibration Verification Using Hand-Joint Imaging
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Solution Overview
Problem
Conventional camera calibration methods for wearable devices require external objects like keyboards for verification, limiting flexibility and availability, and are not adaptable to changing environmental conditions.
Innovation Solution
Utilizing body parts, specifically hand joints, as alignment features for camera calibration verification, allowing calibration to be performed without external objects and adapting to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external objects like keyboards are used for camera calibration verification, then measurement precision can be achieved, but device complexity and ease of operation deteriorate due to limited flexibility and availability
Solution Approach 1:
The system uses the user's own body parts (hand joints) as the calibration verification target, eliminating the need for external objects. The user simply needs to display their hand in front of the camera, and the system automatically detects joint positions and measures distances to verify calibration accuracy.
Solution Approach 2:
The system transforms the camera into a multi-functional device that can both capture images for AR/XR applications and perform calibration verification simultaneously. The same camera and processor are used for both imaging and measurement functions, eliminating the need for separate calibration equipment.
2Manufacturing precision
If conventional calibration methods are used, then manufacturing precision can be maintained, but adaptability deteriorates because they are not adaptable to changing environmental conditions
Solution Approach 1:
The system performs calibration verification dynamically during device usage rather than only during manufacturing. It continuously monitors calibration status by detecting hand joint positions and comparing measured distances against stored reference values, allowing real-time adaptation to environmental changes such as temperature variations and device handling.
Solution Approach 2:
The system establishes a feedback loop where hand joint distance measurements are continuously compared against ground truth values, and calibration adjustments are made based on the differences. This feedback mechanism enables the system to automatically compensate for environmental changes and maintain calibration accuracy throughout the device lifecycle.
Data Source
AI summary
Techniques are directed to identifying focus points of a body part of a user as alignment features for calibration verification. In some implementations, a body part of the user may be a hand, and the focus points are joints. A calibration verification involves, in some implementations, capturing an image of a user's hand and detecting the joints of the hand. The calibration verification then involves, in such implementations, determining distances between joints and computing a metric based on the distances. This metric is then compared to a ground truth metric and the calibration is evaluated based on the verification.