红外图象序列的运动目标检测与运动分析
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摘要
目前，国内外对光流计算方法研究的比较多，但都是局限于可见光图象，图象质量一般来说比较好，而且对于光流的研究也处于算法研究的阶段。对于红外图象序列的光流计算方法，目前国内外研究的很少，也很少用于工程实际中。论文的主要意义在于将光流计算方法应用到实地拍摄的红外图象序列上，通过对各种算法的比较，希望找到一种能够在空空导弹上的实现方案。
论文的主要工作是红外图象序列的运动目标检测与运动分析。运动目标检测主要采用光流法。本文比较了各种光流计算方法，并对大量红外图象进行了仿真。通过对仿真结果的分析，得到了如下结论：
1) 对于红外图象来说，Horn&Schunck算法具有比较好的性能，Anandan算法性能最差；
2) 从计算量的角度来说，Uras算法和Lucas&Kanade算法最小，Anandan算法和Nagel算法最大，Horn&Schunck算法居中；
3) 基于二阶导数的方法（Uras算法，Nagel算法）效果不是很理想，原因是红外图象噪声很大，求导会进一步放大噪声；
4) Lucas&Kanade算法效果不是很理想的原因可能是该算法的基本假设不成立。Lucas&Kanade算法假定光流在一个小区域内是相同的，实际情况并非如此；
然后，对这些结论进行了分析，并对Horn&Schunck算法的参数选择进行了详细的讨论；分析了Horn&Schunck算法的运算量，给出了算法实时实现的原理框图，为硬件实现打下了基础。
论文的第二部分讨论了红外图象序列的运动分析，介绍了两类运动分析的方法---基于特征的方法和基于光流的方法，并对可见光图象序列和红外图象序列的碰撞时间进行了仿真，并根据Subbarao的方法，提出了对碰撞时间进行门限均值处理的方法。
Much research has been done on the estimation of optical flow, however, most of the research is tested using the image sequences formed by visible light, and seldom infrared images sequences. In addition, most of the research is focused on the performance of the algorithm, ignoring the feasibility in practice.
This paper addresses the problem of testing all kinds of algorithms for estimation of optical flow on infrared image sequences by considering the performance and computational cost. The main purpose is to find an approach suitable for the missiles.
Moving target detection and motion analysis from infrared image sequences is discussed in this paper. Optical flow is used for moving target detection. Much simulation is done on infrared images using all kinds of algorithms. The results show that the approach proposed by Horn&Schunck is the better for infrared image sequences. This result is different from the result on image sequences formed by visible light. The reason why other approaches fail is discussed. The computational cost of the approach proposed by Horn&Schunck is estimated and the hardware architecture for implementing it in real time is presented.
The second part of this paper deals with motion analysis which is called SFM (structure from motion) problem, that is, recovering the structure and motion of the object, Two types of SFM algorithm are introduced-feature based and optical flow based. Simulations for time to collision are done on image sequences formed by visible light and infrared image sequences. A new method called gate-average method based on the approach supposed by Subbarao for the estimation of time to collision is presented.
论文的主要工作是红外图象序列的运动目标检测与运动分析。运动目标检测主要采用光流法。本文比较了各种光流计算方法，并对大量红外图象进行了仿真。通过对仿真结果的分析，得到了如下结论：
1) 对于红外图象来说，Horn&Schunck算法具有比较好的性能，Anandan算法性能最差；
2) 从计算量的角度来说，Uras算法和Lucas&Kanade算法最小，Anandan算法和Nagel算法最大，Horn&Schunck算法居中；
3) 基于二阶导数的方法（Uras算法，Nagel算法）效果不是很理想，原因是红外图象噪声很大，求导会进一步放大噪声；
4) Lucas&Kanade算法效果不是很理想的原因可能是该算法的基本假设不成立。Lucas&Kanade算法假定光流在一个小区域内是相同的，实际情况并非如此；
然后，对这些结论进行了分析，并对Horn&Schunck算法的参数选择进行了详细的讨论；分析了Horn&Schunck算法的运算量，给出了算法实时实现的原理框图，为硬件实现打下了基础。
论文的第二部分讨论了红外图象序列的运动分析，介绍了两类运动分析的方法---基于特征的方法和基于光流的方法，并对可见光图象序列和红外图象序列的碰撞时间进行了仿真，并根据Subbarao的方法，提出了对碰撞时间进行门限均值处理的方法。
Much research has been done on the estimation of optical flow, however, most of the research is tested using the image sequences formed by visible light, and seldom infrared images sequences. In addition, most of the research is focused on the performance of the algorithm, ignoring the feasibility in practice.
This paper addresses the problem of testing all kinds of algorithms for estimation of optical flow on infrared image sequences by considering the performance and computational cost. The main purpose is to find an approach suitable for the missiles.
Moving target detection and motion analysis from infrared image sequences is discussed in this paper. Optical flow is used for moving target detection. Much simulation is done on infrared images using all kinds of algorithms. The results show that the approach proposed by Horn&Schunck is the better for infrared image sequences. This result is different from the result on image sequences formed by visible light. The reason why other approaches fail is discussed. The computational cost of the approach proposed by Horn&Schunck is estimated and the hardware architecture for implementing it in real time is presented.
The second part of this paper deals with motion analysis which is called SFM (structure from motion) problem, that is, recovering the structure and motion of the object, Two types of SFM algorithm are introduced-feature based and optical flow based. Simulations for time to collision are done on image sequences formed by visible light and infrared image sequences. A new method called gate-average method based on the approach supposed by Subbarao for the estimation of time to collision is presented.
引文
[1] 刘永昌, 陈洪印. 红外制导与红外对抗技术分析. 红外技术, 1997, 19(1):15~20
[2] 施德恒, 黄宜军. 红外成象导引头发展综述. 激光与红外, 1997, 27(6):351~354
[3] 冯炽焘. 红外制导技术发展综述(I). 红外技术, 1994, 16(2):1~4
[4] 冯炽焘. 红外制导技术发展综述(II). 红外技术, 1994, 16(3):1~4
[5] 付伟. 红外制导武器的现状及发展趋势. 红外技术, 1999, 21(3):8-13
[6] 王润生. 图象理解. 长沙: 国防科技大学出版社, 1995
[7] 吴立德. 计算机视觉. 上海: 复旦大学出版社, 1993
[8] Horn, B.K.P. Robot Vision. Cambridge, Mass: The MIT Press, 1986
[9] 高文, 陈熙霖. 计算机视觉--算法与系统原理. 北京:清华大学出版社, 桂林:广西科学技术出版社, 1999
[10] 马颂德, 张正友. 计算机视觉:计算理论与算法基础. 北京: 科学出版社, 1998
[11] 章毓晋. 图象工程（下）. 北京: 清华大学出版社, 1999
[12] 郑南宁. 计算机视觉与模式识别 . 北京: 国防工业出版社, 1998
[13] Barron. J.L., Fleet, D.J., Beauchemin, S.S. Systems and Experiment Performance of Optical Flow Techniques. International Journal of Computer Vision, 1994, 12(1): 43-77
[14] 刘国锋,诸昌钤. 光流的计算技术. 西南交通大学学报, 1997, 32(6):656-662
[15] Horn, B.K.P., Schunck, B.G. Determining Optical flow. Artificial Intelligence, 1981, 17:185-203
[16] Nagel, H.H. Constraints for the estimation of displacement vector fields from image sequences. Proceedings of the 8th International Joint Conference on Artificial Intelligence, 1983, 2: 945-951
[17] Nagel, H.H. On the estimation of Optical flow: Relations between different approaches and some new results. Artificial Intelligence, 1987, 33(3): 299-324
[18] Nagel, H.H. Optical Flow Estimation: Advances and Comparisons. In: Jan-Olof Eklundh, eds. Lecture Notes in Computer Science, 1994, 800: 51-60
[19] Nagel, H.H. Image Sequences- Ten (octal) Years- From Phenomenology towards a Theoretical Foundation. Eighth International Conference on Pattern Recognition, 1986, 1174-1185
[20] Lucas, B., Kanade, T. An iterative image registration technique with an application to stereo vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence, 1981, 2: 121-130
[21] Adelson, E.H., Bergen, J.R. Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A, 1985, 2:284-299
[22] Kearney, J.K., Thompson, W.B. Optical Flow Estimation: An error analysis of gradient-based
methods with local optimization. IEEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(2): 229-244
[23] Simoncelli, E.P., Adelson, E.H., Heeger, D.J. Probability Distributions of Optical Flow. Proceedings of the 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991, 310-315
[24] Uras, S., Girosi, F., Verri, A., Torre, V. A Computational Approach to Motion Perception. Biological Cybernetics, 1988, 60:79-97
[25] Shijun Sun, Haynor, D., Yongmin Kim. Motion Estimation Based on Optical Flow with adaptive Gradients. IEEE International Conference on Image Processing, 2000, 1: 852-855
[26] Randriantsoa, A. Berthoumieu, Y. Optical Flow Estimation using forward-backward constraint equation. IEEE International Conference on Image Processing, 2000, 2: 578 - 581
[27] 陈海峰, 陈维南. 基于区域平滑约束的光流场计算方法. 高技术通讯, 1998, 9:16-20
[28] 吴新根, 罗立民. 一种改进的光流场计算方法. 电子学报, 2000, 28(1):117-119
[29] Anandan, P. A Computational Framework and an Algorithm for the Measurement of Visual Motion. International Journal of Computer Vision, 1989, 2: 283-310
[30] Singh, A. An Estimation-Theoretic Framework for image-flow computation. Proceedings 3rd International Conference on Computer Vision, 1990, 168-177
[31] Heeger, D.J. Model for the extraction of image flow. Journal of the Optical Society of America A , 1987, 4: 1455-1471
[32] Heeger, D.J. Optical flow using spatiotemporal filters. International Journal of Computer Vision, 1988, 1:279-302
[33] Barman, H. Haglund, L., Knutsson, H., et al. Estimation of velocity, acceleration and disparity in time sequences. Proceedings of the IEEE Workshop on Visual Motion, 1991, 44-51
[34] Waxman, A.M., Wu, J., Bergholm, F. Convected activation profiles and the measurement of visual motion. Proceedings - CVPR '88: Computer Society Conference on Computer Vision and Pattern Recognition, 1988, 717-723
[35] Fleet, D.J. Measurement of Image Velocity. Boston: Kluwer Academic, 1992
[36] Fleet, D.J., Jepson, A.D., Computation of component image velocity from local phase information. International Journal of Computer Vision, 1990, 5:77-104
[37] Fleet, D.J., Jepson, A.D. Stability of phase Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(12): 1253 - 1268
[38] Li-Fen Chen, Ja-Chen Lin, Hong-Yuan Mark Liao. Wavelet-based optical Flow Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12(1): 1-12
[39] Zhou, Y.T. Chellappa, R. Computation of optical flow using a neural network. IEEE International Conference on Neural Networks, 1988, 2: 71 - 78
[40] Burt, P.J., Adelson, E.H. The Laplacian Pyramid as a compact Image code. IEEE
Transactions on Communications, 1983, 31(4):532-540
[41] Verri, A., Girosi, F., Torre, V. Differential techniques for optical flow. Journal of the Optical Society of America A, 1990, 7(5): 912-922
[42] Little, J.J. Verri, A. Analysis of differential and matching methods for optical flow. Proceedings: Workshop on Visual Motion, 1989, 173 - 180
[43] Shaogang Gong, Michael Brady. Parallel computation of Optical Flow. Lecture Notes in Computer Science, 1990, 427: 124-133
[44] Zuloaga, A. Martin, J.L. Ezquerra, J. Hardware architectural for optical flow estimation in real time. IEEE International Conference on Image Processing, 1998, 3: 972-976
[45] Jebara, T. Azarbayejani, A. Pentland, A. 3D structure from 2D Motion. IEEE Signal Processing Magazine, 1999, 16(3): 66 - 84
[46] Aggarwal, J.K. Nandhakumar, N. On the computation of Motion from Sequences of Images-A Review. Proceedings of the IEEE, 1988, 76(8): 917 - 935
[47] Huang, T.S. Netravali, A.N. Motion and Structure from Feature Correspondences: A Review. Proceedings of the IEEE, 1994, 82(2): 252 - 268
[48] Taylor, C.J. Kriegman, D.J. Structure and Motion from Line Segments in Multiple Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(11): 1021 - 1032
[49] Roger Y.Tsai. Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(1): 13-27
[50] Zhengyou Zhang. Motion and Structure from two perspective views: from essential parameters to Euclidean motion through the fundamental matrix. Journal of the Optical Society of America A, 1997, 14(11): 2938-2950
[51] Waxman, A.M., Ullman, S. Surface Structure and Three-Dimensional Motion from Image Flow Kinematics. The International Journal of Robotics Research, 1985, 4(3): 72-94
[52] Waxman, A.M., Wohn, K. Contour Evolution, Neighborhood Deformation and Global Image Flow: Planar surfaces in Motion. The International Journal of Robotics Research, 1985, 4(3):95-108
[53] Subbarao, M., Waxman, A.M. Closed Form solutions to Image Flow Equations for Planar Surfaces in Motion. Computer Vision, Graphics, And Image Processing, 1986, 36:208-228.
[54] Negahdaripour, S., Lee, S. Motion Recovery from Image Sequences Using Only First Order Optical Flow Information. International Journal of Computer Vision, 1992, 9(3): 163-184
[55] Negahdaripour, S. Interpretation of Image Flow: A Spatio-Temporal Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(3): 266-278
[56] Juyang Weng, Thomas Huang, Narendra Ahuja. Motion and structure from two perspective views: Algorithms, Error Analysis, and Error Estimation. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 1989, 11(5): 451-475
[57] Gilad Adiv. Inherent Ambiguities in Recovering 3-D motion and Structure from a Noise Flow Field. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(5):477-489
[58] Jerian, C.P., Jain, Ramesh. Structure from Motion- A Critical Analysis of Methods. IEEE Transactions on Systems and Cybernetics, 1991, 21(3): 572-588
[59] Meyer, F.G. Time-to-Collision from First-Order Models of the Motion Field. IEEE Transactions on Robotics and Automation, 1994, 10(6): 792-798
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[2] 施德恒, 黄宜军. 红外成象导引头发展综述. 激光与红外, 1997, 27(6):351~354
[3] 冯炽焘. 红外制导技术发展综述(I). 红外技术, 1994, 16(2):1~4
[4] 冯炽焘. 红外制导技术发展综述(II). 红外技术, 1994, 16(3):1~4
[5] 付伟. 红外制导武器的现状及发展趋势. 红外技术, 1999, 21(3):8-13
[6] 王润生. 图象理解. 长沙: 国防科技大学出版社, 1995
[7] 吴立德. 计算机视觉. 上海: 复旦大学出版社, 1993
[8] Horn, B.K.P. Robot Vision. Cambridge, Mass: The MIT Press, 1986
[9] 高文, 陈熙霖. 计算机视觉--算法与系统原理. 北京:清华大学出版社, 桂林:广西科学技术出版社, 1999
[10] 马颂德, 张正友. 计算机视觉:计算理论与算法基础. 北京: 科学出版社, 1998
[11] 章毓晋. 图象工程（下）. 北京: 清华大学出版社, 1999
[12] 郑南宁. 计算机视觉与模式识别 . 北京: 国防工业出版社, 1998
[13] Barron. J.L., Fleet, D.J., Beauchemin, S.S. Systems and Experiment Performance of Optical Flow Techniques. International Journal of Computer Vision, 1994, 12(1): 43-77
[14] 刘国锋,诸昌钤. 光流的计算技术. 西南交通大学学报, 1997, 32(6):656-662
[15] Horn, B.K.P., Schunck, B.G. Determining Optical flow. Artificial Intelligence, 1981, 17:185-203
[16] Nagel, H.H. Constraints for the estimation of displacement vector fields from image sequences. Proceedings of the 8th International Joint Conference on Artificial Intelligence, 1983, 2: 945-951
[17] Nagel, H.H. On the estimation of Optical flow: Relations between different approaches and some new results. Artificial Intelligence, 1987, 33(3): 299-324
[18] Nagel, H.H. Optical Flow Estimation: Advances and Comparisons. In: Jan-Olof Eklundh, eds. Lecture Notes in Computer Science, 1994, 800: 51-60
[19] Nagel, H.H. Image Sequences- Ten (octal) Years- From Phenomenology towards a Theoretical Foundation. Eighth International Conference on Pattern Recognition, 1986, 1174-1185
[20] Lucas, B., Kanade, T. An iterative image registration technique with an application to stereo vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence, 1981, 2: 121-130
[21] Adelson, E.H., Bergen, J.R. Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A, 1985, 2:284-299
[22] Kearney, J.K., Thompson, W.B. Optical Flow Estimation: An error analysis of gradient-based
methods with local optimization. IEEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(2): 229-244
[23] Simoncelli, E.P., Adelson, E.H., Heeger, D.J. Probability Distributions of Optical Flow. Proceedings of the 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991, 310-315
[24] Uras, S., Girosi, F., Verri, A., Torre, V. A Computational Approach to Motion Perception. Biological Cybernetics, 1988, 60:79-97
[25] Shijun Sun, Haynor, D., Yongmin Kim. Motion Estimation Based on Optical Flow with adaptive Gradients. IEEE International Conference on Image Processing, 2000, 1: 852-855
[26] Randriantsoa, A. Berthoumieu, Y. Optical Flow Estimation using forward-backward constraint equation. IEEE International Conference on Image Processing, 2000, 2: 578 - 581
[27] 陈海峰, 陈维南. 基于区域平滑约束的光流场计算方法. 高技术通讯, 1998, 9:16-20
[28] 吴新根, 罗立民. 一种改进的光流场计算方法. 电子学报, 2000, 28(1):117-119
[29] Anandan, P. A Computational Framework and an Algorithm for the Measurement of Visual Motion. International Journal of Computer Vision, 1989, 2: 283-310
[30] Singh, A. An Estimation-Theoretic Framework for image-flow computation. Proceedings 3rd International Conference on Computer Vision, 1990, 168-177
[31] Heeger, D.J. Model for the extraction of image flow. Journal of the Optical Society of America A , 1987, 4: 1455-1471
[32] Heeger, D.J. Optical flow using spatiotemporal filters. International Journal of Computer Vision, 1988, 1:279-302
[33] Barman, H. Haglund, L., Knutsson, H., et al. Estimation of velocity, acceleration and disparity in time sequences. Proceedings of the IEEE Workshop on Visual Motion, 1991, 44-51
[34] Waxman, A.M., Wu, J., Bergholm, F. Convected activation profiles and the measurement of visual motion. Proceedings - CVPR '88: Computer Society Conference on Computer Vision and Pattern Recognition, 1988, 717-723
[35] Fleet, D.J. Measurement of Image Velocity. Boston: Kluwer Academic, 1992
[36] Fleet, D.J., Jepson, A.D., Computation of component image velocity from local phase information. International Journal of Computer Vision, 1990, 5:77-104
[37] Fleet, D.J., Jepson, A.D. Stability of phase Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(12): 1253 - 1268
[38] Li-Fen Chen, Ja-Chen Lin, Hong-Yuan Mark Liao. Wavelet-based optical Flow Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12(1): 1-12
[39] Zhou, Y.T. Chellappa, R. Computation of optical flow using a neural network. IEEE International Conference on Neural Networks, 1988, 2: 71 - 78
[40] Burt, P.J., Adelson, E.H. The Laplacian Pyramid as a compact Image code. IEEE
Transactions on Communications, 1983, 31(4):532-540
[41] Verri, A., Girosi, F., Torre, V. Differential techniques for optical flow. Journal of the Optical Society of America A, 1990, 7(5): 912-922
[42] Little, J.J. Verri, A. Analysis of differential and matching methods for optical flow. Proceedings: Workshop on Visual Motion, 1989, 173 - 180
[43] Shaogang Gong, Michael Brady. Parallel computation of Optical Flow. Lecture Notes in Computer Science, 1990, 427: 124-133
[44] Zuloaga, A. Martin, J.L. Ezquerra, J. Hardware architectural for optical flow estimation in real time. IEEE International Conference on Image Processing, 1998, 3: 972-976
[45] Jebara, T. Azarbayejani, A. Pentland, A. 3D structure from 2D Motion. IEEE Signal Processing Magazine, 1999, 16(3): 66 - 84
[46] Aggarwal, J.K. Nandhakumar, N. On the computation of Motion from Sequences of Images-A Review. Proceedings of the IEEE, 1988, 76(8): 917 - 935
[47] Huang, T.S. Netravali, A.N. Motion and Structure from Feature Correspondences: A Review. Proceedings of the IEEE, 1994, 82(2): 252 - 268
[48] Taylor, C.J. Kriegman, D.J. Structure and Motion from Line Segments in Multiple Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(11): 1021 - 1032
[49] Roger Y.Tsai. Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(1): 13-27
[50] Zhengyou Zhang. Motion and Structure from two perspective views: from essential parameters to Euclidean motion through the fundamental matrix. Journal of the Optical Society of America A, 1997, 14(11): 2938-2950
[51] Waxman, A.M., Ullman, S. Surface Structure and Three-Dimensional Motion from Image Flow Kinematics. The International Journal of Robotics Research, 1985, 4(3): 72-94
[52] Waxman, A.M., Wohn, K. Contour Evolution, Neighborhood Deformation and Global Image Flow: Planar surfaces in Motion. The International Journal of Robotics Research, 1985, 4(3):95-108
[53] Subbarao, M., Waxman, A.M. Closed Form solutions to Image Flow Equations for Planar Surfaces in Motion. Computer Vision, Graphics, And Image Processing, 1986, 36:208-228.
[54] Negahdaripour, S., Lee, S. Motion Recovery from Image Sequences Using Only First Order Optical Flow Information. International Journal of Computer Vision, 1992, 9(3): 163-184
[55] Negahdaripour, S. Interpretation of Image Flow: A Spatio-Temporal Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(3): 266-278
[56] Juyang Weng, Thomas Huang, Narendra Ahuja. Motion and structure from two perspective views: Algorithms, Error Analysis, and Error Estimation. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 1989, 11(5): 451-475
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