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A Low Rank Regularization Method for Motion Adaptive Video Stabilization

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Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

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Abstract

Hand-held video cameras usually suffer from undesirable video jitters due to unstable camera motion. Although path optimization methods have been successfully employed to produce stabilized videos, the methods generally result in unintended large void areas in fast motion video sequences. To overcome this limitation, in this paper, we present a novel video stabilization algorithm which is derived from an optimization model consisting of a motion data fidelity term and two regularization terms: motion adaptive smoothness term and low rank term. Particularly, we design a motion adaptive kernel to measure neighbor motion similarity by exploiting local derivative information of dominant motion parameter, which is incorporated into the local weighted smoothness term to guide a motion aware regularization. Besides, the low rank property of neighbor motions is utilized to further improve the performance of stabilization. Experimental results show that the proposed method noticeably stabilizes a video, and it suppresses void areas effectively in fast motion frames.

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References

  1. Morimoto, C., Chellappa, R.: Evaluation of image stabilization algorithms. In: ICASSP (1998)

    Google Scholar 

  2. Buehler, C., Bosse, M., McMillan, L.: Non-metric image-based rendering for video stabilization. In: CVPR (2001)

    Google Scholar 

  3. Zhang, G., Hua, W., Qin, X., Shao, Y., Bao, H.: Video stabilization based on a 3D perspective camera model. Vis. Comput. 25(11), 997–1008 (2009)

    Article  Google Scholar 

  4. Liu, F., Gleicher, M., Jin, H., Agarwala, A.: Content preserving warps for 3D video stabilization. ACM Trans. Graph. 28(3), 44:2–44:10 (2009)

    Google Scholar 

  5. Zhou, Z., Jin, H., Ma, Y.: Plane-based content preserving warps for video stabilization. In: CVPR (2013)

    Google Scholar 

  6. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  7. Litvin, A., Konrad, J., Karl, W.: Probabilistic video stabilization using Kalman filtering and mosaicking. In: SPIE Image and Video Communications and Processing (2003)

    Google Scholar 

  8. Chang, H.C., Lai, S.H., Lu, K.R.: A robust real-time video stabilization algorithm. J. Vis. Commun. Image Represent. 17(3), 659–673 (2006)

    Article  Google Scholar 

  9. Matsushita, Y., Ofek, E., Ge, W., Tang, X., Shum, H.Y.: Full-frame video stabilization with motion inpainting. IEEE Trans. PAMI 28(7), 1150–1163 (2006)

    Article  Google Scholar 

  10. Grundmann, M., Kwatra, V., Essa, I.: Auto-directed video stabilization with robust l1 optimal camera paths. In: CVPR (2011)

    Google Scholar 

  11. Zhang, F.L., Wang, J., Zhao, H., Martin, R.R., Hu, S.M.: Simultaneous camera path optimization and distraction removal for improving amateur video. IEEE Trans. Image Process. 24(12), 5982–5994 (2015)

    Article  MathSciNet  Google Scholar 

  12. Liu, F., Gleicher, M., Wang, J., Jin, H., Agarwala, A.: Subspace video stabilization. ACM Trans. Graph. 30(1), 1–10 (2011)

    Article  Google Scholar 

  13. Wang, Y.S., Liu, F., Hsu, P.S., Lee, T.Y.: Spatially and temporally optimized video stabilization. IEEE Trans. Vis. Comput. Graphics 19(8), 1354–1361 (2013)

    Article  Google Scholar 

  14. Koh, Y.J., Lee, C., Kim, C.S.: Video stabilization based on feature trajectory augmentation and selection and robust mesh grid warping. IEEE Trans. Image Process. 24(12), 5260–5273 (2015)

    Article  MathSciNet  Google Scholar 

  15. Liu, S., Yuan, L., Tan, P., Sun, J.: Bundled camera paths for video stabilization. ACM Trans. Graph. 32(4), 78:1–78:10 (2013)

    Google Scholar 

  16. Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Process. 16(2), 349–366 (2007)

    Article  MathSciNet  Google Scholar 

  17. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006 Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32

    Chapter  Google Scholar 

  18. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  19. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 1–37 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  20. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)

    Article  MATH  Google Scholar 

  21. Deshaker. http://www.guthspot.se/video/deshaker.htm

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Acknowledgments

The research is supported in part by the National Key Research and Development Program of China under Grant No. 2016YFF0103604, by the National Natural Science Foundation (NSF) of China under Grant No. 61571230; and by the NSF of Jiangsu Province under Grant No. BK20161500.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Huicong Wu, Hiuk Jae Shim & Liang Xiao

Authors
  1. Huicong Wu
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  2. Hiuk Jae Shim
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  3. Liang Xiao
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Corresponding author

Correspondence to Liang Xiao .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

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Wu, H., Shim, H.J., Xiao, L. (2017). A Low Rank Regularization Method for Motion Adaptive Video Stabilization. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_48

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  • DOI: https://doi.org/10.1007/978-3-319-67777-4_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

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