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准确标定摄像机的混合粒子群优化方法

Hybrid Particle-Swarm-Optimization Method for Accurately Calibrating Cameras

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摘要

摄像机标定在机器视觉系统研究中占据十分重要的部分,为更好更快地标定摄像机,提出一种基于混合粒子群算法的摄像机参数优化方法。用最小二乘法求出摄像机的内外参数,并以此作为待优化参数的初始值;然后以最小距离准则建立目标函数,再利用混合粒子群算法对摄像机参数进一步优化计算,最终获得误差小的摄像机参数。实验结果表明混合粒子群优化算法能够快速、高精度地收敛,可在一定程度上提高摄像机的标定精度。

Abstract

Camera calibration is a very important part in machine-vision-system research. To calibrate a camera better and faster, we propose a camera-parameter-optimization method based on hybrid particle-swarm optimization. First, we obtain the internal and external parameters of the camera by the least-squares method and use them as the initial values of the parameters to be optimized. Then, we establish the objective function using the minimum-distance criterion. Next, we use a hybrid particle-swarm optimization algorithm to further optimize the camera parameters, and finally we obtain the camera parameters with only small errors. Our experimental results show that the optimization algorithm can converge quickly and accurately. Therefore, this method is able to improve camera-calibration accuracy of the camera to some extent.

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中图分类号:TP391

DOI:10.3788/LOP56.211506

所属栏目:机器视觉

基金项目:国家自然科学基金;

收稿日期:2019-03-25

修改稿日期:2019-04-30

网络出版日期:2019-11-01

作者单位    点击查看

雷阳:新疆大学电气工程学院, 新疆 乌鲁木齐 830047
张宏立:新疆大学电气工程学院, 新疆 乌鲁木齐 830047
王聪:新疆大学电气工程学院, 新疆 乌鲁木齐 830047

联系人作者:张宏立(1831701512@qq.com)

备注:国家自然科学基金;

【1】Ma S D and Zhang Z Y. Computer vision: computational theory and algorithmic basis. 52-53(1998).
马颂德, 张正友. 计算机视觉: 计算理论与算法基础. 52-53(1998).

【2】Tsai R Y. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal on Robotics and Automation. 3(4), 323-344(1987).

【3】Zhang Z Y. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(11), 1330-1334(2000).

【4】Zhang Z, Zhao R J, Liu E H et al. A single-image linear calibration method for camera. Measurement. 130, 298-305(2018).

【5】Huang H Y and Qi F H. A genetic algorithm approach to accurate calibration of camera. Journal of Infrared and Millimeter Waves. 19(1), 1-6(2000).
黄海贇, 戚飞虎. 一种精确标定摄像机的遗传算法方案. 红外与毫米波学报. 19(1), 1-6(2000).

【6】Xie Z X and Zhang A Q. Simultaneous calibration of the intrinsic and extrinsic parameters of ultra-large-scale line structured-light sensor. Acta Optica Sinica. 38(3), (2018).
解则晓, 张安祺. 超大尺度线结构光传感器内外参数同时标定. 光学学报. 38(3), (2018).

【7】Liu S L, Sun C, Liu H B et al. A new two-step method for tilt/shift camera self-calibration. Scientia Sinica Technologica. 48(8), 836-844(2018).
柳升龙, 孙聪, 刘海波 等. 一种新的移轴相机两步标定方法. 中国科学. 48(8), 836-844(2018).

【8】Yu J, Chen C, Gao N et al. Camera calibration based on phase target. Laser & Optoelectronics Progress. 55(11), (2018).
于瑾, 陈超, 高楠 等. 基于相位标靶的相机标定. 激光与光电子学进展. 55(11), (2018).

【9】Lu J, Sun H B and Chang Z Y. A novel method for camera calibration with orthogonal vanishing points. Chinese Journal of Lasers. 41(2), (2014).
卢津, 孙惠斌, 常智勇. 新型正交消隐点的摄像机标定方法. 中国激光. 41(2), (2014).

【10】Sun C, Liu H B, Chen S Y et al. A general imaging model based method for Scheimpflug camera calibration. Acta Optica Sinica. 38(8), (2018).
孙聪, 刘海波, 陈圣义 等. 基于广义成像模型的Scheimpflug相机标定方法. 光学学报. 38(8), (2018).

【11】Li J, Yang Y M and Fu G P. Camera self-calibration method based on GA-PSO algorithm. [C]∥2011 IEEE International Conference on Cloud Computing and Intelligence Systems, September 15-17, 2011, Beijing, China. New York: IEEE. 149-152(2011).

【12】Xu S, Sun X X, Liu X et al. Geometry method of camera self-calibration based on a rectangle. Acta Optica Sinica. 34(11), (2014).
徐嵩, 孙秀霞, 刘希 等. 基于矩形的摄像机自标定几何方法. 光学学报. 34(11), (2014).

【13】Hong Y, Sun X X, Cai M et al. An intrinsic parameters self-calibration technique based on infinite homography between orthogonal vanishing points. Chinese Journal of Lasers. 42(12), (2015).
洪洋, 孙秀霞, 蔡鸣 等. 基于正交消隐点无穷单应的摄像机内参数自标定方法. 中国激光. 42(12), (2015).

【14】Wang D L and Hu S. Optimization method of camera calibration based on quantum-behaved particle swarm optimization algorithm. Laser & Optoelectronics Progress. 55(12), (2018).
王道累, 胡松. 基于量子粒子群优化算法的摄像机标定优化方法. 激光与光电子学进展. 55(12), (2018).

【15】Chen S X. -04-06)[2019-03-10]. http:∥www.paper.edu.cn/releasepaper/content/201804-74. (2018).
陈甦欣, 张晓峰. -04-06)[2019-03-10]. http:∥www.paper.edu.cn/releasepaper/content/201804-74. (2018).

【16】Guo T Y, Li N N and Liu Y. Optimization of camera internal parameters based on particle swarm algorithm. Laser & Optoelectronics Progress. 54(11), (2017).
郭彤颖, 李宁宁, 刘雍. 基于粒子群算法的摄像机内参数优化方法. 激光与光电子学进展. 54(11), (2017).

【17】Huang W G and Dong A G. Camera self-calibration based on particle swarm optimisation. Computer Applications and Software. 32(5), 216-219, 233(2015).
黄伟光, 董安国. 基于粒子群算法的摄像机自标定. 计算机应用与软件. 32(5), 216-219, 233(2015).

【18】Jiang X K, Fan Y Q and Wang W. BP neural network camera calibration based on particle swarm optimization genetic algorithm. Journal of Frontiers of Computer Science & Technology. 8(10), 1254-1262(2014).
江祥奎, 范永青, 王婉. 基于粒子群遗传算法的BP神经网络摄像机标定. 计算机科学与探索. 8(10), 1254-1262(2014).

【19】Chatterjee C and Roychowdhury V P. Chong E K P. A nonlinear Gauss-Seidel algorithm for noncoplanar and coplanar camera calibration with convergence analysis. Computer Vision and Image Understanding. 67(1), 58-80(1997).

【20】Weng J, Cohen P and Herniou M. Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 14(10), 965-980(1992).

【21】Kennedy J and Eberhart R. Particle swarm optimization. [C]∥Proceedings of the IEEE International Conference on Neural Networks, November 27-December 1, 1995, The University of Western Australia, Perth, Western Australia. New York: IEEE. 1941-1948(1995).

引用该论文

Lei Yang,Zhang Hongli,Wang Cong. Hybrid Particle-Swarm-Optimization Method for Accurately Calibrating Cameras[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211506

雷阳,张宏立,王聪. 准确标定摄像机的混合粒子群优化方法[J]. 激光与光电子学进展, 2019, 56(21): 211506

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