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A Combined Local-Global Match for Optical Flow

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Image and Graphics Technologies and Applications (IGTA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 875))

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Abstract

Optical flow estimation is still an open question in computer vision. Matching is the initialization of the final optical flow results. A good matching is important for the flow. In this paper, a combined local-global matching method is proposed. The local matching method and the global method are integrated together to make a trade-off between the large displacement and local consistency of optical flow. Extensive experiments on state-of-art challenging datasets MPI-Sintel show that the proposed method is efficient and effective.

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References

  1. Ochs, P., Malik, J., et al.: Segmentation of moving objects by long term video analysis. TPAMI 36(6), 1187–1200 (2014)

    Article  Google Scholar 

  2. **zhou, Z., Wang, Y. et al.: Flow-guided feature aggregation for video object detection. In: ICCV (2017)

    Google Scholar 

  3. Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS, vol. 1, no. 4, pp. 568–576 (2014)

    Google Scholar 

  4. Urtasun, R., Lenz, P., Geiger, A.: Are we ready for autonomous driving? The Kitti vision benchmark suite. In: CVPR, pp. 3354–3361 (2012)

    Google Scholar 

  5. Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: CVPR 2015, pp. 3061–3070 (2015)

    Google Scholar 

  6. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1), 185–203 (1981)

    Article  Google Scholar 

  7. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI, pp. 674–679 (1981)

    Google Scholar 

  8. Xu, J., Ranftl, R., et. al.: Accurate optical flow via direct cost volume processing. In: CVPR (2017)

    Google Scholar 

  9. Hu, Y., Song, R., et. al.: Efficient coarse-to-fine patch match for large displacement optical flow. In: CVPR, pp. 5704–5712 (2016)

    Google Scholar 

  10. Baker, S., Scharstein, D.: Lewis, J.P., et. al.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92(1), 1–31 (2011)

    Article  Google Scholar 

  11. Sun, D., Roth, S., Black, M.J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. 106(2), 115–137 (2014)

    Article  Google Scholar 

  12. Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. TPAMI. 33(3), 500–513 (2011)

    Article  Google Scholar 

  13. Xu, L., Jia, J., Matsushita, Y.: Motion detail preserving optical flow estimation. In: Computer Vision and Pattern Recognition, pp. 1293–1300 (2010)

    Google Scholar 

  14. Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: Deepflow: large displacement optical flow with deep matching. In: ICCV, pp. 1385–1392 (2013)

    Google Scholar 

  15. Timofte, R., Gool, L.V.: Sparse flow: sparse matching for small to large displacement optical flow. In: WACV, pp. 1100–1106 (2015)

    Google Scholar 

  16. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  17. Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The generalized patchmatch correspondence algorithm. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 29–43. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15558-1_3

    Chapter  Google Scholar 

  18. Revaud. J, Weinzaepfel, P., et al.: Epicflow: edge-preservinginterpolation of correspondences for optical flow. In: CVPR, pp. 1164–1172 (2015)

    Google Scholar 

  19. Hu, Y. Li, Y.: Robust interpolation of correspondences for large displacement optical flow. In: CVPR (2017)

    Google Scholar 

  20. Niu, Y., Dick, A., Brooks, M.J.: Linking local and global optical flow computation by subspace regularization. Opt. Eng. 52(3) (2013)

    Google Scholar 

  21. Jara-Wilde, J., Cerda, M.: An implementation of combined local-global optical flow. Image Processing On Line. pp. 139–158 (2015)

    Google Scholar 

  22. Liu, C., Yuen, J., Torralba, A.: Sift flow: dense correspondence across scenes and its applications. TPAMI 33(5), 978–994 (2011)

    Article  Google Scholar 

  23. Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. JMLR (2016)

    Google Scholar 

  24. Ilg, E., Mayer, N., et al.: Flownet 2.0 evolution of optical flow estimation with deep networks. In: CVPR (2017)

    Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China(No. 51475025), Bei**g Municipal Science and Technology Commission Project Z171100000117010, the National Key Research and Development Plan (Nos. 2016YFB0801203, 2016YFB0801200).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang Universtiy, Bei**g, 100191, China

    Yueran Zu & Wenzhong Tang

  2. The National Computer Network Emergency Response Technical Team, Coordination Center of China, Bei**g, 100029, China

    **uguo Bao & Mingdong Zhang

  3. Institute of Computing Technology Chinese Academy of Sciences, Bei**g, 100080, China

    Ke Gao

Authors
  1. Yueran Zu
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  2. Wenzhong Tang
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  3. **uguo Bao
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  4. Ke Gao
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  5. Mingdong Zhang
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Corresponding author

Correspondence to **uguo Bao .

Editor information

Editors and Affiliations

  1. Bei**g Institute of Technology, Bei**g, China

    Yongtian Wang

  2. Beihang University, Bei**g, China

    Zhiguo Jiang

  3. Peking University, Bei**g, China

    Yuxin Peng

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Zu, Y., Tang, W., Bao, X., Gao, K., Zhang, M. (2018). A Combined Local-Global Match for Optical Flow. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_19

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  • DOI: https://doi.org/10.1007/978-981-13-1702-6_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1701-9

  • Online ISBN: 978-981-13-1702-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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