Camera self-calibration method based on IOS-PSO (Internetwork Operating System-Particle Swarm Optimization)

A camera and coordinate system technology, applied in the field of computer vision, can solve problems such as large errors and large changes in operating results, and achieve the effects of improving accuracy, avoiding volatility, and being easy to implement.

Active Publication Date: 2015-11-25
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

That is, in the interval [0, f u ], the results of each run vary greatly, and may converge to P with a large error 1 ,P 2 ,P 3

Method used

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  • Camera self-calibration method based on IOS-PSO (Internetwork Operating System-Particle Swarm Optimization)
  • Camera self-calibration method based on IOS-PSO (Internetwork Operating System-Particle Swarm Optimization)
  • Camera self-calibration method based on IOS-PSO (Internetwork Operating System-Particle Swarm Optimization)

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Embodiment

[0102] An IOS-PSO-based method for camera self-calibration derived from an optimization search of an independent five-parameter cost function. It mainly includes three steps: S1. Establishing a cost function based on internal parameters of the camera; S2. IOS-PSO optimizing the cost function; S3. IOS-PSO search result limiting rules. The specific introduction of this method is as follows:

[0103] S1. Establishing a cost function of the camera based on internal parameters;

[0104] Such as Figure 8 As shown, any point x in the left figure has a corresponding epipolar line I' in the right figure, and the mapping relationship between the two is expressed as x→I', introducing the fundamental matrix F, and expressing this mapping as:

[0105] I'=Fx(0.7)

[0106] Because the corresponding point x in the left picture is located on the epipolar line I' in the right picture, the corresponding image points of the two images of the same scene satisfy:

[0107] x'Fx=0(0.8)

[0108]...

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Abstract

The invention discloses a camera self-calibration method based on IOS-PSO (Internetwork Operating System-Particle Swarm Optimization). The method is relatively simple in algorithm, high in efficiency and high in optimizing result precision. The method mainly comprises three steps: S1, establishing an internal parameter-based cost function of a camera; S2, performing IOS-PSO on the cost function; and S3, limiting a rule for an IOS-PSO search result. The method comprises the following specific steps: acquiring four pictures of different angles of the same scenario; extracting and matching image feature points; solving three basis matrixes F by using the matched points and adopting an RANSAC (Random Sample Consensus) method, and then performing SVD (singular value decomposition) on the basis matrixes to construct the cost function as a fitness function of particle swarm optimization; dividing a set initial search interval, and setting a research rule according to the optimal value; taking five unknown internal parameters fu, fv, u0, v0 and s to be calibrated in the camera as particle elements of a particle swarm; performing IOS-PSO search; and if the search result accords with the search rule, selecting the final search result, otherwise, searching again. Contrast experiments of four algorithms verify that the calibration result of the method is accurate, stable and efficient.

Description

technical field [0001] The invention relates to a particle swarm optimization algorithm and belongs to the field of computer vision, in particular to an IOS-PSO-based camera self-calibration method. Background technique [0002] Camera calibration is an essential step in reconstructing the three-dimensional information of an object in space from the plane two-dimensional image captured by the camera. Assuming that a point A on an object in space corresponds to a in the plane image captured by the camera, then there is a linear relationship between the two: [Image point a]=K[Rt][Space point A]. Among them, R is a 3*3 matrix, t is a 3*1 matrix, which are the rotation matrix and translation matrix from the world coordinate system to the camera coordinate system; K is the camera internal parameter matrix, and the process of determining K is camera calibration. [0003] The existing camera self-calibration method utilizes the algebraic geometry constraints between several images...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 邓方徐建萍陈杰窦丽华张乐乐陈文颉白永强代凤驰周睿
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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