光学技术, 2015, 41 (3): 197, 网络出版: 2015-05-20   

铁路运行环境下的车载相机姿态估计

Attitude estimation of the train-borne camera from the railway-environment
作者单位
1 北京交通大学 交通数据分析与挖掘北京市重点实验室, 北京 100044
2 中国传媒大学 理学院应用数学系, 北京 100024
3 北京理工大学 光电学院, 北京 100081
摘要
基于对铁路运行环境中所拍摄的图像特征的分析, 提出了一种修正的线段检测子(MLSD), 并利用该线段检测子结合最小二乘拟合及交叉迭代优化方法对消失点进行快速检测, 进而利用消失点坐标实现了相机姿态的估计, 为后续调整机器视觉算法进行铁路运行环境的场景重建提供重要依据。运用该方法, 基于车载相机所拍摄的图像对车载相机姿态进行估计, 实验所得的估计值满足了车载检测系统的精度要求, 表明了此方法对于估计相机姿态有效的。
Abstract
The attitude of the train-bore camera is a set of important external parameters. It is very helpful to analyze the images using the machine vision methods. And adjusting the machine vision algorithms and understanding the scene can be guided by estimating the attitude of the camera. By analyzing the features of images taken from the railway environment, a method of estimating the attitude of the train-bore camera is proposed, i.e. using the detecting algorithm of vanishing points based on the proposed modified line segment detector (MLSD), least-square technique and cross iteration methods to obtain the attitude of the camera. The experimental results show that the validation and accuracy of method can meet the requirements of the precision of vehicle detection system.
参考文献

[1] 侯卫星.0号高速综合检测列车[M].北京: 中国铁道出版社, 2010.

    Hou W X. No. 0 high-speed integrative inspection train [M]. China Railway Press, 2010.

[2] 中国铁道科学研究院.高速综合检测列车技术交流总结[R]. 2006.

    Railway Science Research Institute of China. Technical summary of high-speed integrative inspection train china[R]. 2006.

[3] Chen S E, Quicktimevr. An image-based approach to environment navigation[C]∥ Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, ACM, 1995: 29-38.

[4] Li Q, Ren S. A visual detection system for rail surface defects, systems, man, and cybernetics, Part C: Applications and reviews[J]. IEEETransactions on, 2012, 42(6): 1531-1542.

[5] Deutschl E, Gasser C, Niel A, et al. Defect detection on rail surfaces by a vision based system[C]∥Intelligent Vehicles Symposium, IEEE, 2004: 507-511.

[6] Espino J C, Stanciulescu B. Turnout detection and classification using a modified hog and template matching, intelligent transportation systems[C]//2013 the 16th International IEEE Annual Conference on IEEE, 2013: 2045-2050.

[7] Wang S, Zheng J Y, Luo S. Route panorama acquisition and rendering for high-speed railway monitoring[C]∥ Multimedia and Expo (ICME), 2013 IEEE International Conference , 2013: 1-6.

[8] Alippi C, Casagrande E, Scotti F, et al. Composite real-time image processing for railways track profile measurement[J]. IEEE Transactions on Instrumentation and Measurement, 2000,49(3): 559-564.

[9] Horn B K, Negahdaripour S. Direct passive navigation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, (1): 168-176.

[10] Horn B K, Fang Y, Masaki I. Time to contact relative to a planar surface, intelligent vehicles symposium[C]. IEEE, 2007: 68-74.

[11] Thrun S, et al. Camera calibration[M]. Stanford CS223B Computer Vision, 2003.

[12] Tsai R Y. An efficient and accurate camera calibration technique for 3D machine vision[C].Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach FL, 1986: 364-374.

[13] 邱茂林, 马颂德, 李毅. 计算机视觉中相机标定综述[J]. 自动化学报, 2000, 26(1): 43-55.

    Qiu M L, Ma S D, Li Y. Overview of camera calibration for computer vision[J]. ACTA Automatica Sinica, 2000,26(1): 43-55.

[14] 徐杰.机器视觉中摄像机标定Tsai两步法的分析与改进[J]. 计算机工程与科学. 2010,32(04),45-48.

    Xu J. Analyzing and improving the Tsai camera calibration method in machine vision[J]. Computer Engineering & Science, 2010,32(04): 45-48.

[15] 张吴明,钟约先. 基于改进差分进化算法的相机标定研究[J]. 光学技术. 2004,30(6): 720-723.

    ZhangW M, ZhongY X. Camera calibration based on improved differential evolution algorithm[J]. Optical Technique, 2004, 30(6): 720-723.

[16] 陈钊,郭永彩. 体视2D-3cPIV相机标定方法研究[J]. 光学技术 2007,33(6): 881-888.

    Chen Z, GuoY C. Study on method of camera calibration of stereoscopic 2D-3cPIV technique[J].Optical Technique. 2007,33(6): 881-888.

[17] Willert C E. Assessment of camera models f or use in planar velocimetry calibration[J].Experiments in Fluids ,2006 , 41 (2): 135-143.

[18] Roger Y. Tsai a versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV camera and lenses[J] .IEEE Journal of Robotics and Automatuon , 1987 ,RA-3(4): 323-344 .

[19] Zhang Z Y. Aflexible new technique f or camera calib ration[ J] .IEEE Transact ions on Pattern Analysis and Machine Intelligence, 2000 , 22(11): 1330-1334.

[20] Wieneke B. Stereo-PIV using self-calibration on particle images[ J] .Experiments in Fluids , 2005 , 39(2): 267-280.

[21] 马颂德,张正友.计算机视觉计算理论与算法基础[M].北京: 科学出版社,1998.

    Ma S D, Zhang Z Y. Computation theory of computer vision and algorithms[M].Beijing: Science Press,1998.

[22] Westeon P, Ling C, Roberts C, et al. Monitoring vertical track irregularity from in-service railway vehicles[J]. Proceedings of the institution of mechanical engineers, Part F: Journal of Rail and Rapid Transit, 2007,221(1): 75-88.

[23] Strang G. Introduction to linear algebra[M]. Wellesley Cambridge Press, 2003.

[24] Horn B K P. Robot vision[M]. The MIT Press, 1986.

[25] Caprile B, Torre V. Using vanishing points for camera calibration[J].International Journal of Computer Vision, 1990,4(2): 127-139.

[26] Xu Y, Oh S, Hoogs A. A minimum error vanishing point detection approach for uncalibrated monocular images of man-made environments[C]∥Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference, 2013: 1376-1383.

[27] Duda R O, Hart P E. Use of the hough transformation to detect lines and curves in pictures[J]. Communicati- ons of Association for Computing Machinery. 1972,15(1): 11-15.

[28] von Gioi R G, Jakubowicz J, Morel J M, et al. Lsd: A fast line segment detector with a false detection control,pattern analysis andmachine intelligence[J]. IEEE Trans, 2010,32(4): 722-732.

[29] Albert A E, Albert A. Regression and the Moore-Penrose pseudo inverse[M ]. Academic Press NewYork, 1972, 3.

[30] Strang G. Computational science and engineering[M].Wellesley Cambridge Press, 2007.

于欣妍, 罗四维, 许廷发, 王亮, 王胜春. 铁路运行环境下的车载相机姿态估计[J]. 光学技术, 2015, 41(3): 197. YU Xinyan, LUO Siwei, XU Tingfa, WANG Liang, WANG Shengchun. Attitude estimation of the train-borne camera from the railway-environment[J]. Optical Technique, 2015, 41(3): 197.

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