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Particle swarm optimization algorithm based camera self-calibration method and apparatus

A particle swarm optimization, camera technology, applied in computing, image analysis, image data processing, etc., can solve the problems of inconvenient camera calibration, low solution accuracy, poor robustness, etc.

Active Publication Date: 2016-03-30
华雁智能科技(集团)股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] At present, with the continuous development of computer vision technology, visual sensors have been successfully used in object recognition, video surveillance and other fields due to their simplicity and ease of use. Binocular stereo vision is based on the principle of parallax to acquire objects from multiple images. The method of 3D geometric information; camera calibration technology is a key step in the process of 3D vision reconstruction and accurate target positioning. After years of technical research, many advanced and effective camera calibration methods with good practical value have come out one by one, but at present So far no one calibration method can meet all calibration requirements, they all have their own advantages and disadvantages
[0003] The traditional camera calibration methods all need a calibration reference object, and the three-dimensional coordinates of the point on the reference object and its image icon are known. In practical applications, some parameters such as focal length and magnification will change accordingly according to the needs. If every It will be very inconvenient to re-calibrate the camera with a standard reference object every time, so the study of camera calibration methods has important theoretical research significance and practical application value
[0004] Although the camera self-calibration method in the prior art is flexible and does not need to depend on the scene and the calibration object, the solution obtained is low in accuracy and poor in robustness, so the focus of self-calibration method research can be on how to improve the calibration accuracy and robustness. In terms of performance; camera calibration methods keep pace with the times, and with the improvement of requirements and existing experimental conditions, there is no end to camera calibration research methods, because our needs are always changing, and research efficiency needs to be continuously improved , so we need to use a calibration method that is more flexible, faster, and more accurate. It also means that we need to better solve the defects in the optimization problem (redundant parameters, model expression, equation ill-conditioning, etc.), which It is also an important content of the main direction and research of continuous improvement of calibration technology.

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  • Particle swarm optimization algorithm based camera self-calibration method and apparatus
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  • Particle swarm optimization algorithm based camera self-calibration method and apparatus

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Embodiment 1

[0022] Such as figure 1 As shown, the camera self-calibration method based on the particle swarm optimization algorithm provided by the embodiment of the present invention includes:

[0023] Step S100: Obtain multiple images taken by the camera to be calibrated, and use the SURF algorithm to extract feature points of the multiple images;

[0024] When a camera set at a fixed position needs to be calibrated, the camera is used to capture multiple images in its shooting area, and the multiple images should have overlapping areas, and the feature points of the multiple images are extracted. In this embodiment, the camera used for calibration can be a monocular camera or a binocular camera, it can be a bolt camera, that is, a camera with a fixed position, or a dome camera, that is, a camera that can rotate 360 ​​degrees , the specific camera type is not limited to the embodiment of the present invention.

[0025] In this embodiment, the multiple images taken should be at least t...

Embodiment 2

[0092] Such as Figure 6 As shown, the camera self-calibration device based on the particle swarm optimization algorithm provided by the embodiment of the present invention includes a feature point acquisition module 200, which is used to acquire multiple images taken by the camera to be calibrated, and extract the feature points of the multiple images;

[0093] The feature point matching module 210 is used to measure the similarity of the feature points of the multiple images to obtain matching feature points that match each other;

[0094] The optimal camera parameter acquisition module 220 is configured to obtain a solution set of various parameters of the camera by using the matching feature points and the nonlinear model of the camera based on a particle swarm optimization algorithm.

[0095] The feature point matching module 210 is also used for:

[0096] Calculate the Euclidean distance from each feature point on the first image to all feature points on the second imag...

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Abstract

Embodiments of the invention provide a particle swarm optimization algorithm based camera self-calibration method and apparatus, and relates to the field of camera self-calibration. The method comprises: obtaining a plurality of images shot by a to-be-calibrated camera and extracting feature points of the images; performing similarity measurement on the feature points of the images to obtain matched feature points which are mutually matched; and based on a particle swarm optimization algorithm, obtaining a solution set of camera parameters by utilizing the matched feature points and a nonlinear model of the camera. The camera self-calibration method is high in precision and high in robustness.

Description

technical field [0001] The invention relates to the field of camera self-calibration, in particular to a camera self-calibration method and device based on particle swarm optimization algorithm. Background technique [0002] At present, with the continuous development of computer vision technology, visual sensors have been successfully used in object recognition, video surveillance and other fields due to their simplicity and ease of use. Binocular stereo vision is based on the principle of parallax to acquire objects from multiple images. The method of 3D geometric information; camera calibration technology is a key step in the process of 3D vision reconstruction and accurate target positioning. After years of technical research, many advanced and effective camera calibration methods with good practical value have come out one by one, but at present So far no one calibration method can meet all calibration requirements, and they all have their own advantages and disadvantag...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 胡娟贺苏宁蒲刚
Owner 华雁智能科技(集团)股份有限公司