Motion Estimation for Objects Analysis and Detection in Videos

  • Margarita FavorskayaEmail author
Part of the Intelligent Systems Reference Library book series (ISRL, volume 29)


The motion estimation methods are used for modeling of various physical processes, the behavior of objects, and prediction of events. In this chapter the moving objects in videos are generally considered. Such motion estimation methods are classified as comparative methods and gradient methods. The comparative motion estimation methods are usually used in real-time applications. Many aspects of block-matching modifications are discussed including Gaussian mixture model, Lie operators, bilinear deformations, multi-level motion model, etc. The gradient motion estimation methods assist to realize the motion segmentation in complex dynamic scenes because only they provide a required accuracy. Application of the 2D tensors (in spatial domain) or the 3D tensors (in spatio-temporal domain) depends from the solved problem. Development of the gradient motion estimation methods is necessary for intelligent recognition of objects and events in complex scenes, video indexing in multimedia databases.


Motion estimation block matching optical flow structural tensor flow tensor visual imagery infrared imagery 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alzoubi, H., Pan, W.: Fast and accurate global motion estimation algorithm using pixel subsampling. Information Sciences 178(17), 3415–3425 (2008), doi:10.1016/j.ins.2008.05.004CrossRefGoogle Scholar
  2. 2.
    Basarab, A., Liebgott, H., Morestin, F., Lyshchik, A., Higashi, T., Asato, R., Delachartre, P.: A method for vector displacement estimation with ultrasound images and its application for thyroid nodular disease. Med. Image Analysis 12(3), 259–274 (2008), doi:10.1016/ Scholar
  3. 3.
    Benmoussat, N., Belbachir, M.F., Benamar, B.: Motion estimation and compensation from noisy image sequences: A new filtering scheme. Image and Vision Computing 25(5), 686–694 (2007), doi:10.1016/j.imavis.2006.05.010CrossRefGoogle Scholar
  4. 4.
    Boudlal, A., Nsiri, B., Aboutajdine, D.: Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method . EURASIP J. on Advances in Signal Processing (2010), doi:10.1155/2010/210937Google Scholar
  5. 5.
    Bugeau, A., Perez, P.: Detection and segmentation of moving objects in complex scenes. Computer Vision and Image Understanding 113(4), 459–476 (2009), doi:10.1016/j.cviu.2008.11.005CrossRefGoogle Scholar
  6. 6.
    Denman, S., Fookes, C., Sridharan, S.: Improved simultaneous computation of motion detection and optical flow for object tracking. Digital Image Computing: Techniques and Applications (2009), doi:10.1109/DICTA.2009.35Google Scholar
  7. 7.
    Dikbas, S., Arici, T., Altunbasak, Y.: Fast motion estimation with interpolation-free sub-sample accuracy. IEEE Transactions on Circuits and Systems for Video Technology 20(7), 1047–1051 (2010), doi:10.1109/TCSVT.2010.2051283.CrossRefGoogle Scholar
  8. 8.
    Doshi, A., Bors, A.G.: Smoothing of optical flow using robustified diffusion kernels. Image and Vision Computing 28(12), 1575–1589 (2010), doi:10.1016/j.imavis.2010.04.001CrossRefGoogle Scholar
  9. 9.
    Favorskaya, M.: Estimation of Objects Motion Based on Tensor Approach. J. Digital Signal Processing 1, 2–9 (2010)Google Scholar
  10. 10.
    Favorskaya, M., Zotin, A., Damov, M.: Intelligent Inpainting System for Texture Reconstruction in Videos with Text Removal. In: International Congress on Ultra Modern Telecommunications and Control Systems (2010), doi:10.1109/ICUMT.2010.5676476Google Scholar
  11. 11.
    Favorskaya, M.: Recognition of dynamic visual patterns based on group transformations. In: International Conference on Pattern Recognition and Image Analysis: New Information Technologies, vol. 1, pp. 185–188 (2010)Google Scholar
  12. 12.
    Fernandez-Caballero, A., Castillo, J.C., Martínez-Cantos, J., Martinez-Tomas, R.: Optical flow or image subtraction in human detection from infrared camera on mobile robot. Robotics and Autonomous Systems (2011), doi:10.1016/j.robot.2010.06.002Google Scholar
  13. 13.
    Gao, X., Li, X., Feng, J., Tao, D.: Shot-based video retrieval with optical flow tensor and HMMs. Pattern Recognition Letters 30(2), 140–147 (2009), doi:10.1016/j.patrec.2008.02.009CrossRefGoogle Scholar
  14. 14.
    Gao, X., Yang, Y., Tao, D., Li, X.: Discriminative optical flow tensor for video semantic analysis. Computer Vision and Image Understanding 113(3), 372–383 (2009), doi:10.1016/j.cviu.2008.08.007CrossRefGoogle Scholar
  15. 15.
    Hannuksela, J., Sangi, P., Heikkila, J.: Vision-based motion estimation for interaction with mobile devices. Computer Vision and Image Understanding 108(1/2), 188–195 (2007), doi:10.1016/j.cviu.2006.10.014CrossRefGoogle Scholar
  16. 16.
    Jang, S.W., Pomplun, M., Kim, G.Y., Choi, H.I.: Adaptive robust estimation of affine parameters from block motion vectors. Image and Vision Computing 23(14), 1250–1263 (2005), doi:10.1016/j.imavis.2005.09.003CrossRefGoogle Scholar
  17. 17.
    Jayaswal, D.J., Zaveri, M.A., Chaudhari, R.E.: Multi step motion estimation algorithm. In: International Conference and Workshop on Emerging Trends in Technology (2010), doi:10.1145/1741906.1742012Google Scholar
  18. 18.
    Kemouche, M.S., Aouf, N.: A Gaussian mixture based optical flow modeling for object detection. In: International Conference on Crime Detection and Prevention, vol. 2, pp. 1–6 (2009), doi:10.1049/ic.2009.0256Google Scholar
  19. 19.
    Kim, B.G., Song, S.K., Mah, P.S.: Enhanced block motion estimation based on distortion-directional search patterns. Pattern Recognition Letters 27(12), 1325–1335 (2006), doi:10.1016/j.patrec.2006.01.004CrossRefGoogle Scholar
  20. 20.
    Klappstein, J., Vaudrey, T., Rabe, C., Wedel, A., Klette, R.: Moving object segmentation using optical flow and depth information. LNCS (2009), doi:10.1007/978-3-540-92957-4_53Google Scholar
  21. 21.
    Lee, H., Jeong, J.: Content adaptive binary block matching motion estimation algorithm. In: Midwest Symposium on Circuits and Systems (2010), doi:10.1109/MWSCAS.2010.5548850Google Scholar
  22. 22.
    Lee, K.J., Kwon, D., Yun, I.D., Lee, S.U.: Optical flow estimation with adaptive convolution kernel prior on discrete framework. Computer Vision and Pattern Recognition (2010), doi:0.1109/CVPR.2010.5539953Google Scholar
  23. 23.
    Liao, B., Du, M., Hu, J.: Color optical flow estimation based on gradient fields with extended constraints. In: International Conference on Networking and Information Technology (2010), doi:10.1109/ICNIT.2010.5508511Google Scholar
  24. 24.
    Liawa, Y.C., Lai, J.Z.C., Hong, Z.C.: Fast block matching using prediction and rejection criteria. Signal Processing 89(6), 1115–1120 (2009), doi:10.1016/j.sigpro.2008.12.012CrossRefGoogle Scholar
  25. 25.
    Lin, D., Grimson, E., Fisher, J.: Modeling and estimating persistent motion with geometric flows. Computer Vision and Pattern Recognition (2010), doi:10.1109/CVPR.2010.5539848Google Scholar
  26. 26.
    Lindeberg, T., Akbarzadeh, A., Laptev, I.: Galilean-diagonalized spatio-temporal interest operators. International Conference on Pattern Recognition 1, 57–62 (2004), doi:10.1109/ICPR.2004.1334004Google Scholar
  27. 27.
    Liu, K., Qian, J., Yang, R.: Block matching algorithm based on RANSAC algorithm. In: International Conference on Image Analysis and Signal Processing (2010), doi:10.1109/IASP.2010.5476127Google Scholar
  28. 28.
    Liu, X., Cong, W.: Hybrid-template adaptive motion estimation algorithm based on block matching. In: International Conference on Computer and Communication Technologies in Agriculture Engineering (2010), doi:10.1109/CCTAE.2010.5543459Google Scholar
  29. 29.
    Liu, P.R., Meng, M.Q.H., Liu, P.X., Tong, F.F.L., Wang, X.: Optical flow and active contour for moving object segmentation and detection in monocular robot. In: International Conference on Robotics and Automation (2006), doi:10.1109/ROBOT.2006.1642328Google Scholar
  30. 30.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004), doi:10.1023/B:VISI.0000029664.99615.94CrossRefGoogle Scholar
  31. 31.
    Lu, X., Manduchi, R.: Fast image motion segmentation for surveillance applications. Image and Vision Computing (2010), doi:10.1016/j.imavis.2010.08.001Google Scholar
  32. 32.
    Mahmoud, H.A., Muhaya, F.B., Hafez, A.: Lip reading based surveillance system. In: International Conference on Future Information Technology (2010), doi:10.1109/FUTURETECH.2010.5482688Google Scholar
  33. 33.
    Mercer, J.: Functions of positive and negative type and their connection with the theory of integral equations. Phil. Trans. R Soc. Lond. 209(441-4589), 415–446 (1909), doi:10.1098/rsta.1909.0016zbMATHCrossRefGoogle Scholar
  34. 34.
    Moreno-Garcia, J., Rodriguez-Benitez, L., Fernandez-Caballero, A., Lopez, M.T.: Video sequence motion tracking by fuzzification techniques. Applied Soft Computing 10(1), 318–331 (2010), doi:10.1016/j.asoc.2009.08.002CrossRefGoogle Scholar
  35. 35.
    Nisar, H., Choi, T.S.: Multiple initial point prediction based search pattern selection for fast motion estimation. Pattern Recognition 42(3), 475–486 (2009), doi:10.1016/j.patcog.2008.08.010zbMATHCrossRefGoogle Scholar
  36. 36.
    Nsiri, B., Boudlal, A., Aboutajdine, D.: Modeling of video sequences by Gaussian mixture: Application in motion estimation by block matching method. Eurasip J. on Advances in Signal Processing (2010), doi:10.1155/2010/210937Google Scholar
  37. 37.
    Pan, W.D., Yoo, S.M., Park, C.H.: Complexity accuracy tradeoffs of Lie operators in motion estimation. Pattern Recognition Letters 28(7), 778–787 (2007), doi:10.1016/j.patrec.2006.11.006CrossRefGoogle Scholar
  38. 38.
    Park, H., Martin, G.R., Bhalerao, A.: Local affine image matching and synthesis based on structural patterns. IEEE Transactions on Image Processing 19(8), 1968–1977 (2010), doi:10.1109/TIP.2010.2045704MathSciNetCrossRefGoogle Scholar
  39. 39.
    Park, S.J., Jeon, G., Kim, H., Jeong, J., Kim, S.N., Lim, J.: Adaptive partial block matching algorithm for fast motion estimation. In: Digest of Technical Papers International Conference on Consumer Electronics (2010), doi:10.1109/ICCE.2010.5418765Google Scholar
  40. 40.
    Parrilla, E., Riera, J., Torregrosa, J.R.: Fuzzy control for obstacle detection in object tracking. Mathematical and Computer Modelling 52(7/8), 1228–1236 (2010), doi:10.1016/j.mcm.2010.02.014zbMATHCrossRefGoogle Scholar
  41. 41.
    Pers, J., Sulic, V., Kristan, M., Perse, M., Polanec, K., Kovacic, S.: Histograms of optical flow for efficient representation of body motion. Pattern Recognition Letters 31(11), 1369–1376 (2010), doi:10.1016/j.patrec.2010.03.024CrossRefGoogle Scholar
  42. 42.
    Quan, H.: A new method of dynamic texture segmentation based on optical flow and level set combination. In: International Conference on Information Science and Engineering (2009), doi:10.1109/ICISE.2009.95Google Scholar
  43. 43.
    Saha, A., Mukherjee, J., Sural, S.: New pixel-decimation patterns for block matching in motion estimation. Signal Processing: Image Communication 23(10), 725–738 (2008), doi:10.1016/j.image.2008.08.004CrossRefGoogle Scholar
  44. 44.
    Scharr, H.: Optimal Filters for Extended Optical Flow. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, pp. 14–29. Springer, Heidelberg (2007), doi:10.1007/978-3-540-69866-1_2CrossRefGoogle Scholar
  45. 45.
    Soroushmehr, S.M.R., Samavi, S., Shirani, S.: Block matching algorithm based on local codirectionality of blocks. In: IEEE International Conference on Multimedia and Expo., pp. 201–204 (2009), doi:10.1109/ICME.2009.5202471Google Scholar
  46. 46.
    Touil, B., Basarab, A., Delachartre, P., Bernard, O., Friboulet, D.: Analysis of motion tracking in echocardiographic image sequences: Influence of system geometry and point-spread function. Ultrasonics 50(3), 373–386 (2010), doi:10.1016/j.ultras.2009.09.001CrossRefGoogle Scholar
  47. 47.
    Wang, X., Tang, Z.: Modified particle filter-based infrared pedestrian tracking. Infrared Physics & Technology 53(4), 280–287 (2010), doi:10.1016/j.infrared.2010.04.002CrossRefGoogle Scholar
  48. 48.
    Werlberger, M., Pock, T., Bischof, H.: Motion estimation with non-local total variation regularization. Computer Vision and Pattern Recognition (2010), doi:10.1109/CVPR.2010.5539945Google Scholar
  49. 49.
    Yu, F., Hui, M., Han, W., Wang, P., Dong, L., Zhao, Y.: The application of improved block-matching method and block search method for the image motion estimation. Optics Communications 283(23), 4619–4625 (2010), doi:10.1016/j.optcom.2010.07.006CrossRefGoogle Scholar
  50. 50.
    Yupeng, X., Xin, W., Feng, H.: Application of optical flow field for intelligent tracking system. In: International Symposium on Intelligent Information Technology Application (2008), doi:10.1109/IITA.2008.475Google Scholar
  51. 51.
    Zappella, L., Llado, X., Provenzi, E., Salvi, J.: Enhanced Local Subspace Affinity for feature-based motion segmentation. Pattern Recognition 44(2), 454–470 (2011), doi:10.1016/j.patcog.2010.08.015CrossRefGoogle Scholar
  52. 52.
    Zhang, W., Fang, X., Yang, X., Wu, Q.M.J.: Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes. J. Electron Imaging 16(2) (2007), doi:10.1117/1.2731329Google Scholar
  53. 53.
    Zhang, W., Wua, Q.M.J., Yin, H.: Moving vehicles detection based on adaptive motion histogram. Digital Signal Processing 20(3), 793–805 (2010), doi:10.1016/j.dsp.2009.10.006CrossRefGoogle Scholar
  54. 54.
    Zinbi, Y., Chahir, Y., Elmoataz, A.: Moving object segmentation using optical flow with active contour model. In: International Conference on Information and Communication Technologies: From Theory to Applications (2008), doi:10.1109/ICTTA.2008.4530112Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Siberian State Aerospace UniversityKrasnoyarskRussia

Personalised recommendations