Spatial Calibration With Iterative Point Displacement
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Solution Overview
Problem
Existing spatial calibration methods for camera-based surveillance systems are prone to inaccuracies due to factors such as narrow overlapping areas, different capture parameters, and degraded image quality, leading to an approximate projection model that fails to provide accurate metric information about detected objects.
Innovation Solution
A method for spatial calibration involving the iterative determination of a projection model using pairs of points of interest, where the position of each point is displaced according to a displacement policy, and the reprojection error is evaluated to select the optimal projection model, reducing inaccuracies through refined displacement policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spatial calibration methods are used with fixed point correspondence, then the calibration process is simple, but the measurement precision deteriorates due to narrow overlapping areas and different capture parameters
Solution Approach 1:
The patent applies dynamics by making the point correspondence dynamic rather than fixed. Points of interest are allowed to move within a search area around their initial matched positions, enabling the calibration to adapt to variations in capture parameters and overlapping area geometry, thereby improving measurement precision without requiring overly complex manual intervention
Solution Approach 2:
The patent implements feedback through the reprojection error evaluation mechanism. The reprojection error serves as a feedback signal that guides the optimization process, allowing the system to iteratively adjust point positions and select the best projection model candidate, thus improving calibration accuracy while maintaining automated operation
2Measurement precision
If multiple projection model candidates are evaluated with point displacement, then the measurement precision improves, but the loss of time increases due to iterative optimization
Solution Approach 1:
The patent applies partial action by evaluating multiple projection model candidates with different numbers of point correspondences. Instead of using all available points, the system evaluates candidates with varying subsets of points, allowing it to find an optimal balance between computational efficiency and calibration accuracy, reducing time loss while maintaining precision
3Measurement precision
If point positions are fixed based on initial matching, then the ease of operation is maintained, but the measurement precision deteriorates under degraded image quality
Solution Approach 1:
The patent applies self-service by enabling the calibration system to automatically compensate for matching errors and image quality degradation. The iterative optimization process with reprojection error evaluation allows the system to self-correct inaccuracies in initial point matching without requiring manual intervention, thus improving measurement precision while maintaining ease of operation
Solution Approach 2:
The patent implements parameter changes by allowing point positions to vary within a search area rather than remaining fixed. This parameter flexibility enables the system to adapt to degraded image quality and different capture conditions, improving calibration accuracy while the automated nature of the process maintains operational simplicity
Data Source
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AI summary
According to some embodiments of the disclosure, it is provided a method for spatial calibration of a first image against a second image. After obtaining a plurality of pairs of points of interest, each pair matching a point of interest in the first image to a corresponding point of interest in the second image, the method further comprises determining, iteratively, a projection model between the two images and calibrating the first image based on the determined projected model. An iteration of the determining comprises displacing a position of a point of interest, determining a projection model candidate using multiple pairs of points of interest and evaluating a reprojection error between the positions of the points of interest as projected using the projection model candidate and the corresponding points of interest.