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Video stabilization using regularity of energy flow

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Abstract

Jitter and shaky movements of camera are primarily responsible for video destabilization. Such movements usually produce an irregularity in the flow vectors of frames. Video stabilization technique aims to regularize the irregularity of flow vectors. In this paper, an energy-based motion smoothing approach is proposed to smooth the flow vectors using energy of frames. Energy regularity of frames assures a stabilized video while their irregularity causes a destabilized video. Flow vector estimation, motion smoothing and motion compensation are the three primary steps needed for video stabilization. Performance of the stabilization technique depends on each of the above steps, and an optimal method is sought to enhance the performance. In the proposed method, we estimate both the translational and affine flow vectors of a frame using the spatio-temporal regularity flow model. This model provides the approximated flow vectors of all pixels in a frame by minimizing its flow energy function. In the proposed approach, we estimate the flow vectors of the feature points of the maximally stable extremal region of each frame rather than all the pixels of a frame. The proposed video stabilization method is compared with existing state of art methods on the basis of inter-frame transform fidelity, correlation coefficient, regularity and energy of frames. The stability results achieved validate the robustness of the proposed algorithm.

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Notes

  1. Available at http://www.junlanyang.net/VideoStabilization.html, http://www.facweb.iitkgp.ernet.in/~pkb/Videostabilization.html.

References

  1. Shukla, D., Jha, R.K.: A robust video stabilization technique using integral frame projection warping. Signal Image Video Process. 9(6), 1287–1297 (2015)

    Article  Google Scholar 

  2. Chen, G.-R., Yeh, Y.-M., Wang, S.-J., Chiang, H.-C.: A novel structure for digital image stabilizer. In: IEEE APCCAS, pp. 101–104. Tianjin (2000)

  3. Yang, J., Schonfeld, D., Mohamed, M.: Robust video stabilization based on particle filter tracking of projected camera motion. IEEE Trans. Circuits Syst. Video Technol. 19(7), 945–954 (2009)

    Article  Google Scholar 

  4. Shen, Y., Guturu, P., Damarla, T., Buckles, B.P., Namuduri, K.R.: Video stabilization using principal component analysis and scale invariant feature transform in particle filter framework. IEEE Trans. Consum. Electron. 55(3), 1714–1721 (2009)

    Article  Google Scholar 

  5. Wang, J.M., Chou, H.P., Chen, S.W., Fuh, C.S.: Video stabilization for a hand-held camera based on 3D motion model. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 3477–3480. Cairo (2009)

  6. Li, J., Xu, T., Zhang, K.: Real-time feature-based video stabilization on FPGA. In: IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 4, pp. 907–919 (2017)

  7. Battiato, S., Gallo, G., Puglisi, G., Scellato, S.: SIFT features tracking for video stabilization. In: 14th International Conference on Image Analysis and Processing (ICIAP), pp. 825–830. Modena (2007)

  8. Yang, J., Schonfeld, D., Chen, C., Mohamed, M.: Online video stabilization based on particle filters. In: 2006 International Conference on Image Processing, pp. 1545–1548. Atlanta (2006)

  9. Okade, M., Patel, G., Biswas, P.K.: Robust learning-based camera motion characterization scheme with applications to video stabilization. IEEE Trans. Circuits Syst. Video Technol. 26(3), 453–466 (2016)

    Article  Google Scholar 

  10. Dong, J., Liu, H.: Video stabilization for strict real-time applications. In: IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 4, pp. 716–724 (2017)

  11. Walha, A., Wali, A., Alimi, A.M.: Video stabilization with moving object detecting and tracking for aerial video surveillance. Multimed. Tools Appl. 74(17), 6745–6767 (2015)

    Article  Google Scholar 

  12. Tsai, T.H., Fang, C.L., Chuang, H.M.: Design and implementation of efficient video stabilization engine using maximum a posteriori estimation and motion energy smoothing approach. IEEE Trans. Circuits Syst. Video Technol. 22(6), 817–830 (2012)

    Article  Google Scholar 

  13. Zhang, L., Xu, Q.K., Huang, H.: A global approach to fast video stabilization. IEEE Trans. Circuits Syst. Video Technol. 27(2), 225–235 (2017)

    Article  Google Scholar 

  14. Liu, Y., Zou, M., Cao, Y., Lu, X.: Dynamic displacement field model used as a new camera motion model in video stabilization. In: International Conference on Consumer Electronics, pp. 1–2. Las Vegas (2007)

  15. Liu, S., Xu, B., Deng, C., Zhu, S., Zeng, B., Gabbouj, M.: A hybrid approach for near-range video stabilization. In: IEEE Transactions on Circuits and Systems for Video Technology, vol. PP, no. 99, p. 1 (2016)

  16. Xu, Z.: Consistent image alignment for video mosaicing. Signal Image Video Process. 7(1), 129–135 (2013)

    Article  Google Scholar 

  17. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)

    Article  Google Scholar 

  18. Alatas, O., Yan, P., Shah, M.: Spatio-temporal regularity flow (SPREF): its estimation and applications. IEEE Trans. Circuits Syst. Video Technol. 17(5), 584–589 (2007)

    Article  Google Scholar 

  19. Okade, M., Biswas, P.K.: Video stabilization using maximally stable extremal region features. Multimed. Tools Appl. 68(3), 947–968 (2014)

    Article  Google Scholar 

  20. Chen, H.H., Liang, C.K., Peng, Y.C., Chang, H.A.: Integration of digital stabilizer with video codec for digital video cameras. IEEE Trans. Circuits Syst. Video Technol. 17(7), 801–813 (2007)

    Article  Google Scholar 

  21. Sam and Cocoa. https://www.youtube.com/watch?v=627MqC6E5Yo

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Authors and Affiliations

  1. Department of Electrical Engineering, IIT Kanpur, Kanpur, India

    Rupesh Kumar, Afaque Azam, S. Gupta & K. S. Venkatesh

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  1. Rupesh Kumar
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  2. Afaque Azam
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  3. S. Gupta
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  4. K. S. Venkatesh
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Correspondence to Rupesh Kumar.

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Kumar, R., Azam, A., Gupta, S. et al. Video stabilization using regularity of energy flow. SIViP 11, 1519–1526 (2017). https://doi.org/10.1007/s11760-017-1115-6

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  • DOI: https://doi.org/10.1007/s11760-017-1115-6

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