Motion estimation based on textural flow

被引：0

作者：

Qu Z.-S. ^{[1
]}

Feng X. ^{[1
]}

Wang C.-H. ^{[1
]}

机构：

[1] Space Control and Inertial Technology Research Center, Harbin Institute of Technology

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2010年 / 31卷 / 04期

关键词：

Gabor filter bank; Motion estimation; Optical flow; Textural flow;

D O I：

10.3969/j.issn.1006-7043.2010.04.006

中图分类号：

学科分类号：

摘要：

Motion estimation is one of the basic problems in computer vision. A novel method based on textural flow was proposed in order to allow estimation of dense motion from image sequences.compared with the conventional optical flow method using the assumption of constant brightness, it offers better robustness, higher accuracy, and faster computational speed. Based on robust invariant image textural features, a texture motion equation and motion vectors were derived. Difference levels, matrix determinant and condition numbers were defined and then integrated to judge the accuracy and robustness of motion estimation. In implementing the algorithm, a classic Gabor filter band was used. The algorithm was tested with many different kinds of image sequences and the results compared with those of typical methods. The results showed that the proposed method is best used when the moving targets have unique textural features or the background is complex.

引用

页码：438 / 443

页数：5

共 21 条

[1]

Bruhn A., Weickert J., Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods, International Journal of Computer Vision, 61, 3, pp. 211-231, (2005)

[2]

Baker S., Gross R., Matthews I., Lucas-Kanade 20 years on: a unifying framework, International Journal of Computer Vision, 56, 3, pp. 221-255, (2004)

[3]

Galbraith J.M., Kenyon G.T., Ziolkowski R.W., Time-to-collision estimation from motion based on primate visual processing, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 8, pp. 1279-1291, (2005)

[4]

Fleet D.J., Jespon A.D., Computation of component image velocity from local phase information, International Journal of Computer Vision, 5, 1, pp. 77-104, (1990)

[5]

Gautama T., Van Hulle M.M., A phase-based approach to the estimation of the optical flow field using spatial filtering, IEEE Transactions on Neural Networks, 13, 5, pp. 1127-1136, (2002)

[6]

Barron J.L., Fleet D.J., Beauchemin S., Performance of optical flow techniques, International Journal of Computer Vision, 12, 1, pp. 43-77, (1994)

[7]

Bouguet J.Y., Pyramidal implementation of the Lucas Kanade feature tracker

[8]

Alvarez L., Weickert J., Sanchez J., Reliable estimation of dense optical flow fields with large displacements, International Journal of Computer Vision, 39, 1, pp. 41-56, (2000)

[9]

Tal N., Alfred M., Bruckstein R.K., Over- parameterized variational optical flow, International Journal of Computer Vision, 76, 2, pp. 205-216, (2008)

[10]

Papenberg N., Bruhn A., Brox T., Et al., Highly accurate optic flow computation with theoretically justified warping, International Journal of Computer Vision, 67, 2, pp. 141-158, (2006)

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