Object Tracking with Calibrated Cameras Without Parameter Decomposition
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Solution Overview
Problem
Current camera calibration techniques require decomposition into intrinsic and extrinsic camera parameters, which is prone to reprojection errors and assumptions, limiting their accuracy and applicability to cameras with lens distortion or tilt.
Innovation Solution
A camera calibration method that avoids decomposition into explicit intrinsic and extrinsic parameters by using a transformation matrix and applying affine corrections to implicitly determine camera parameters, allowing calibration with any lens tilt or shift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If camera calibration uses decomposition into intrinsic and extrinsic parameters, then the calibration process becomes structured and manageable, but reprojection errors and assumptions (no lens distortion, no tilt) reduce accuracy
Solution Approach 1:
The patent merges the separate decomposition steps into a unified calibration approach. Instead of separating intrinsic and extrinsic parameters, the system uses a single transformation matrix that directly maps 3D world coordinates to 2D image coordinates, eliminating the need for decomposition and its associated assumptions about lens distortion and camera tilt.
Solution Approach 2:
The patent extracts the problematic decomposition step from the calibration process. By removing the requirement to separate parameters into intrinsic and extrinsic components, the method eliminates the source of reprojection errors while retaining the essential calibration functionality through the transformation matrix alone.
2Ease of operation
If camera parameters are decomposed into intrinsic and extrinsic parts, then parameter interpretation becomes easier, but the method cannot handle cameras with lens distortion or tilt
Solution Approach 1:
The transformation matrix serves multiple functions simultaneously: it performs coordinate transformation, encodes all necessary calibration information, and handles various camera configurations (with or without lens distortion, with or without tilt) without requiring separate parameter interpretations. This universal approach makes the calibration method adaptable to diverse camera systems.
3Ease of manufacture
If standard calibration methods are used, then the process follows established procedures, but the method requires fine tuning of camera parameters to a global reference
Solution Approach 1:
The calibration method is self-sufficient and does not require external fine-tuning or adjustment to global references. The transformation matrix is directly computed from calibration data without needing subsequent manual optimization or alignment procedures, eliminating the time-consuming fine-tuning step while maintaining procedural simplicity.
Data Source
AI summary
A method for calibrating a camera without the decomposition of camera parameters into extrinsic and intrinsic components is provided. Further, there is provided a method for tracking an object in motion comprising capturing one or more image frames of an object in motion, using one or more calibrated cameras that have been calibrated according to a calibration method that generates and uses a respective transformation matrix for mapping three-dimensional (3D) real world model features to corresponding two-dimensional (2D) image features. The tracking method further comprises determining, using a hardware processor, motion characteristics of the object in motion based on the captured one or more image frames from each one or more calibrated cameras, the determining of the motion characteristics based on implicit intrinsic camera parameters and implicit extrinsic camera parameters of the respective transformation matrix from each respective one or more calibrated cameras.