Hand Held Mobile Video Stabilization Using Differential Motion Estimation

  • Paresh Rawat
  • Jyoti Singhai
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 131)


The hand held mobile cameras suffer from different undesired slow motions during the scene capturing time. It is required to stabilize the video sequence by removing the undesired motion between the successive frames. Most of the existing methods are either very complex or does not perform well for slow and smooth motion of hand held mobile videos. In this paper a modified video stabilization algorithm for hand held camera videos is proposed which uses bicubic interpolation with Taylor series expansion to improve the estimation efficiency of the hierarchical differential global motion estimation. After motion estimation Gaussian kernel filtering is used to smoothen out estimated motion parameters. Then Inverse rotation smoothening is applied to remove the rotation effect from the stabilized transform chain. This reduces the accumulation error and minimizes missing image area significantly. The performance of the proposed algorithm is tested on various real time videos and also compared with existing algorithm.


Video stabilization differential motion estimation Interpolation Taylor series expansion Gaussian kernel filtering motion smoothing 


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  1. 1.
    Matsushita, Y., Ofek, E., Ge, W., Tang, X., Shum, H.Y.: Full frame video stabilization with motion inpainting. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1163–1178 (2006)CrossRefGoogle Scholar
  2. 2.
    Chang, H.C., Lai, S.H., Lu, K.R.: A robust and efficient video stabilization algorithm. In: ICME 2004, International Conference on Multimedia and Expo, June 2004, vol. 1, pp. 29–32 (2004)Google Scholar
  3. 3.
    Hu, R., Shi, R., Shen, I.F., Chen, W.: Video Stabilization Using Scale Invariant Features. In: 11th International Conference on Information Visualization (IV 2007). IEEE (2007)Google Scholar
  4. 4.
    Yang, J., Schonfeld, D., Mohamed, M.: Robust Video Stabilization based on particle filter racking of projected camera motion. IEEE Trans. on Circuits and Systems for Video Technology 19(7), 945–954 (2009)CrossRefGoogle Scholar
  5. 5.
    Pang, D., Chen, H., Halawa, S.: Efficient Video Stabilization with Dual-Tree Complex Wavelet Transform, EE368 Project Report, Spring (2010)Google Scholar
  6. 6.
    Szeliski, R.: Image Alignment and Stitching: A Tutorial. Technical Report MSR-TR, 2004-92, Microsoft Corp. (2004)Google Scholar
  7. 7.
    Farid, H., Woodward, J.B.: Video stabilization and Enhancement, TR 2007-605, Dartmouth College, Computer Science (1997)Google Scholar
  8. 8.
    Adda, O., Cottineau, N., Kadoura, M.: A Tool for Global Motion Estimation and Compensation for Video Processing. LEC/COEN 490, Concordia University, May 5 (2003)Google Scholar
  9. 9.
    Feng, L., Gleicher, M., Jin, H., Agarwala, A.: Content Preserving Warps for 3D Video Stabilization. In: Int. Conf. Proc. ACM SIGGRAPH 2009 papers, pp. 1–9. ACM, New York (2001)Google Scholar
  10. 10.
    Buehler, C., Bosse, M., Mcmillian, L.: Non-metric image based rendering for video stabilization. In: Proc. Computer Vision and Pattern Recognition, vol. 2, pp. 609–614 (2001)Google Scholar
  11. 11.
    Litvin, A., Konrad, J., Karl, W.: Probabilistic video stabilization using Kalman filtering and mosaicking. In: Proc. of IS&T/SPIE Symposium on Electronic Imaging, Image and Video Communications, vol. 1, pp. 663–674 (2003)Google Scholar
  12. 12.
    Jin, J.S., Zhu, Z., Xu, G.: Digital video sequence stabilization based on 2.5d motion estimation and inertial motion filtering. Real- Time Imaging 7(4), 357–365 (2001)MATHCrossRefGoogle Scholar
  13. 13.
    Takacs, G., Chandrasekhar, V., Chen, D., Tsai, S., Grzeszczuk, R., Girod, B.: Unified real time tracking and recognition with rotation invariant fast features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Fransisco, June 2010, vol. 1, pp. 217–222 (2010)Google Scholar
  14. 14.
    Pilu, M.: Video Stabilization as a Variational Problem and Numerical Solution with the Viterbi Method. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 625–630 (2004)Google Scholar
  15. 15.
    Bergen, J.R., Anandan, P., Hanna, K.J., Hingorani, R.: Hierarchical Model based Motion Estimation. In: Proc. of Second European Conf. on Computer Vision, pp. 237–252 (1992)Google Scholar
  16. 16.
    Anandan, P.: A Computational Framework and an Algorithm for the Measurement of Visual Motion. Int. Journal of Computer Vision 2(3), 283–310 (1989)CrossRefGoogle Scholar

Copyright information

© Springer India Pvt. Ltd. 2012

Authors and Affiliations

  1. 1.Deptt. of Electronics & Communication Engg.Truba I.E.I.TBhopalIndia
  2. 2.Deptt. of Electronics EngineeringMANITBhopalIndia

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