利用共线点求解多摄像机内外参数

摘要/Abstract

摘要： 文中利用运动中共线点的几何特性来获得多摄像机的内外参数。首先,由空间中共线点与图像点之间的对应矩阵来得到对内参数的线性约束,获得了多个摄像机的内参数;然后,根据共线点在摄像机组中各个摄像机下运动前后的坐标,获得摄像机相对于基准摄像机的旋转矩阵和平移向量,以求出摄像机的外参数;最后,进行模拟数据实验和真实图像实验,结果表明了该方法的可行性和有效性。

关键词: 单应矩阵, 多摄像机, 共线点

Abstract: The thesis used the geometric characteristics of collinear points to get the intrinsic parameters of the came-ras.Firstly,the homographic matrix between space collinear points and its image points is used to get the linear constraints of the intrinsic parameters and the intrinsic parameters for multiple cameras.Then,according to the coordinates of collinear points before and after movement in each camera,the rotation matrix and translation vector of the camera relative to the reference camera are obtained,and the outside parameters of the cameras are solved.Finally,simulation data and real image experiments show the feasibility and effectiveness of this method.

Key words: Collinear points, Homographic matrix, Multiple cameras

中图分类号:

TP391.41

罗欢. 利用共线点求解多摄像机内外参数[J]. 计算机科学, 2019, 46(11A): 255-259. https://doi.org/

LUO Huan. Using Collinear Points Solving Intrinsic and External Parameters of Multiple Cameras[J]. Computer Science, 2019, 46(11A): 255-259. https://doi.org/

参考文献

[1]贾云得.机器视觉[M].北京:科学出版社,2000.
[2]SONKA M,HLAVAC V,BOYLE R.图像处理,分析与机器视觉(第二版)[M].艾海舟,等译.北京:人民邮电出版社,2003.
[3]SNYDER W E,QI H.机器视觉教程[M].林学訚,等译.北京:机械工业出版社,2005.
[4]马颂德.计算机视觉[M].北京:科学出版社,1999.
[5]吴福朝.计算机视觉中的数学方法[M].北京:高等教育出版社,2008.
[6]AZIZ Y,KARARA H.Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates [C]∥ASP Symposium on Close-Range Photogrammetric.1971:1-18.
[7]FAIG W.Calibration of Close-range Photogrammetric Systems:Mathematical Formulation [J].Photogrammetric Engineering and Remote Sensing,1975,41:1479-1486.
[8]ZHANG Z Y.Flexible Camera Calibration by Viewing a Plane from Unknown Orientation [C]∥International Conference on Computer Vision (ICCV).1999:666-673.
[9]STURM P,MAYBANK S J.On Plane-Based Camera Calibration:A General Algorithm,Singularities Applications [C]∥International Conference on Computer Vision and Pattern Recognition (CVPR).1999:432-437.
[10]ZHANG Z Y.Camera Calibration with One-dimensional Objects [J].IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),2004,26(7):892-899.
[11]WU F C,HU Z Y,ZHU J.Camera Calibration with MovingOne-dimensional Objects [J].Pattern,V12(7):465-484.
[12]WANG L,WU F C,HU Z Y.Multi-Camera Calibration with One-dimensional Objects under General Motions [C]∥International Conference on Computer Vision (ICCV).2007:1-7.
[13]SUN Q,XU D.Coverage performance analysis of multi-camera networks based on observing reliability model[C]∥Optik.2014:2220-2224.
[14]FAUGERAS O D,LUONG Q T,MAYBANK S.Camera Self-Calibration:Theory and Experiments [C]∥European Conference on Computer Vision (ECCV).1992:321-334.
[15]MAYBANK S J,FAUGERAS O D.A Theory of Self-Calibration of a Moving Camera [J].International Journal of Computer Vision (IJCV),1992,8(2):123-152.
[16]HEYDEN A,ASTROM K.Euclidean Reconstruction from Image Sequences with Varying and Unknown Focal Length and Principal Point [C]∥International Conference on Computer Vision and Pattern Recognition (CVPR).1997:438-443.
[17]POLLEYFEYS M,KOCH R,GOOL L V.Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters [C]∥International Conference on Computer Vision (ICCV).1998:90-95.
[18]UESHIBA T,TOMITA F.Plane-Based Calibration Algorithmfor Multi-Camera Systems via Factorization of Homography Matrices [C]∥International Conference on Computer Vision (ICCV).2003:996-973.
[19]AGRAWAL M,DAVIS L.Camera Calibration using Spheres:A Semi-definite Programming Approach [C]∥International Conference on Computer Vision (ICCV).2003:782-791.
[20]VAISH V,WILBURN B,LEVOY M.Using Plane＋Parallaxfor Calibrating Dense Camera Arrays [C]∥International Conference on Computer Vision and Pattern Recognition (CVPR).2004:2-9.
[21]SINHA S,POLLEFEYS M,MCMILLAN L.Camera Network Calibration from Dynamic Silhouettes[C]∥International Conference on Computer Vision and Pattern Recognition (CVPR).2004:195-202.
[22]WANG B,SUN F M.1-D Camera Calibration Revisited[J].Journal of Computer-Aided Design & Computer Graphics,2014,26(3):452-456.
[23]SUN Q,XU D.self-calibration of multi-camera networks without feature correspondence between different cameras[J].Optic,2014,125:3331-3336.
[24]LIU Z,LI F,ZHANG G.An external parameter calibrationmethod for multiple cameras based on laser rangefinder[J].Measurement,2014,47:954-962.
[25]ZOU J C,TIAN N N.An easy and high-precision camera calibration method based on planar five point[J].Optics and Precision Engineering,2017,25:786-791.

相关文章 15

[1]	张洪博, 董力嘉, 潘玉彪, 萧宗志, 张惠臻, 杜吉祥. 视频理解中的动作质量评估方法综述 Survey on Action Quality Assessment Methods in Video Understanding 计算机科学, 2022, 49(7): 79-88. https://doi.org/10.11896/jsjkx.210600028
[2]	武霖, 孙静宇. 多分支RA胶囊网络及在图像分类中的应用 Multi-branch RA Capsule Network and Its Application in Image Classification 计算机科学, 2022, 49(6): 224-230. https://doi.org/10.11896/jsjkx.210400087
[3]	吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039
[4]	杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236
[5]	宗迪迪, 谢益武. 基于法线迭代的模型中轴生成方法 Model Medial Axis Generation Method Based on Normal Iteration 计算机科学, 2022, 49(6A): 764-770. https://doi.org/10.11896/jsjkx.210400050
[6]	魏勤, 李瑛娇, 娄平, 严俊伟, 胡辑伟. 基于边云协同的人脸识别方法研究 Face Recognition Method Based on Edge-Cloud Collaboration 计算机科学, 2022, 49(5): 71-77. https://doi.org/10.11896/jsjkx.210300222
[7]	邢云冰, 龙广玉, 胡春雨, 忽丽莎. 基于SVM的类别增量人体活动识别方法 Human Activity Recognition Method Based on Class Increment SVM 计算机科学, 2022, 49(5): 78-83. https://doi.org/10.11896/jsjkx.210400024
[8]	瞿中, 陈雯. 基于空洞卷积和多特征融合的混凝土路面裂缝检测 Concrete Pavement Crack Detection Based on Dilated Convolution and Multi-features Fusion 计算机科学, 2022, 49(3): 192-196. https://doi.org/10.11896/jsjkx.210100164
[9]	左杰格, 柳晓鸣, 蔡兵. 基于图像分块与特征融合的户外图像天气识别 Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion 计算机科学, 2022, 49(3): 197-203. https://doi.org/10.11896/jsjkx.201200263
[10]	冷佳旭, 谭明圮, 胡波, 高新波. 基于隐式视角转换的视频异常检测 Video Anomaly Detection Based on Implicit View Transformation 计算机科学, 2022, 49(2): 142-148. https://doi.org/10.11896/jsjkx.210900266
[11]	温啸林, 李长林, 张馨艺, 刘尚松, 朱敏. 基于DPoS共识机制的区块链社区演化的可视分析方法 Visual Analysis Method of Blockchain Community Evolution Based on DPoS Consensus Mechanism 计算机科学, 2022, 49(1): 328-335. https://doi.org/10.11896/jsjkx.201200118
[12]	张倩, 肖丽. 基于流线的流场可视化绘制方法综述 Review of Visualization Drawing Methods of Flow Field Based on Streamlines 计算机科学, 2021, 48(12): 1-7. https://doi.org/10.11896/jsjkx.201200108
[13]	刘遵雄, 朱成佳, 黄稷, 蔡体健. 多跳连接残差注意网络的图像超分辨率重建 Image Super-resolution by Residual Attention Network with Multi-skip Connection 计算机科学, 2021, 48(11): 258-267. https://doi.org/10.11896/jsjkx.201000033
[14]	刘彦, 秦品乐, 曾建朝. 基于YOLOv3与分层数据关联的多目标跟踪算法 Multi-object Tracking Algorithm Based on YOLOv3 and Hierarchical Data Association 计算机科学, 2021, 48(11A): 370-375. https://doi.org/10.11896/jsjkx.201000115
[15]	栾晓, 李晓双. 基于多特征融合的人脸活体检测算法 Face Anti-spoofing Algorithm Based on Multi-feature Fusion 计算机科学, 2021, 48(11A): 409-415. https://doi.org/10.11896/jsjkx.210100181

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed