Skip to main content
SpringerLink
Log in

Combination of local feature detection methods for digital video stabilization

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Technological advances in compact and portable cameras have enabled the generation of large volumes of video sequences. However, videos captured by amateurs are subject to unwanted vibrations due to camera shaking. To overcome such problem, video stabilization aims to remove undesired motion from videos to enhance visual quality, improving applications such as detection and tracking of objects. In this work, we develop and analyze a consensual method for combining a set of local feature techniques for camera motion estimation. Several video sequences are used to evaluate the proposed methodology. Experimental results demonstrate the effectiveness of the combination method over individual local feature approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Amanatiadis, A.A., Andreadis, I.: Digital image stabilization by independent component analysis. IEEE Trans. Instrum. Meas. 59(7), 1755–1763 (2010)

    Article  Google Scholar 

  2. Chang, J.-Y., Hu, W.-F., Cheng, M.-H., Chang, B.-S.: Digital image translational and rotational motion stabilization using optical flow technique. IEEE Trans. Consum. Electron. 48(1), 108–115 (2002)

    Article  Google Scholar 

  3. Ertürk, S.: Real-Time digital image stabilization using kalman filters. Real-Time Imaging 8(4), 317–328 (2002)

    Article  MATH  Google Scholar 

  4. Jia, R., Zhang, H., Wang, L., Li, J.: Digital image stabilization based on phase correlation. In: International Conference on Artificial Intelligence and Computational Intelligence, vol. 3, pp. 485–489. IEEE (2009)

  5. Ko, S.-J., Lee, S.-H., Lee, K.-H.: Digital image stabilizing algorithms based on bit-plane matching. IEEE Trans. Consum. Electron. 44(3), 617–622 (1998)

    Article  Google Scholar 

  6. Kumar, S., Azartash, H., Biswas, M., Nguyen, T.: Real-time affine global motion estimation using phase correlation and its application for digital image stabilization. IEEE Trans. Image Process. 20(12), 3406–3418 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Lin, C.-T., Hong, C.-T., Yang, C.-T.: Real-time digital image stabilization system using modified proportional integrated controller. IEEE Trans. Circuits Syst. Video Technol. 19(3), 427–431 (2009)

    Article  Google Scholar 

  8. Marcenaro, L., Vernazza, G., Regazzoni, C.S.: Image stabilization algorithms for video-surveillance applications. In: International Conference on Image Processing, vol. 1, pp. 349–352. IEEE (2001)

  9. Morimoto, C., Chellappa, R.: Fast electronic digital image stabilization. In: 13th International Conference on Pattern Recognition, vol. 3, pp. 284–288. IEEE (1996)

  10. Ryu, Y.G., Chung, M.J.: Robust online digital image stabilization based on point-feature trajectory without accumulative global motion estimation. IEEE Signal Process. Lett. 19(4), 223–226 (2012)

    Article  Google Scholar 

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

  12. 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 

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

    Google Scholar 

  14. Zheng, Q., Yang, M.: A video stabilization method based on inter-frame image matching score. Glob. J. Comput. Sci. Technol. 17, 41–46 (2017)

  15. Zheng, X., Shaohui, C., Gang, W., Jinlun, L.: Video stabilization system based on speeded-up robust features. In: International Industrial Informatics and Computer Engineering Conference, (2015)

  16. Kumar, R., Azam, A., Gupta, S., Venkatesh, K.S.: Video stabilization using regularity of energy flow. Signal Image Video Process. 11(8), 1519–1526 (2017)

    Article  Google Scholar 

  17. 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 

  18. Umnyashkin, S., Sharonov, I.: Motion compensation in video compression using hexagonal blocks. Signal Image Video Process. 9(1), 213–223 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  21. 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 

  22. Agrawal, M., Konolige, K., Blas, M.R.: Censure: center surround extremas for realtime feature detection and matching. In: European Conference on Computer Vision, pp. 102–115. Springer, (2008)

  23. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. 9th European Conference on Computer Vision, pp. 430–443 (2006)

  24. Szeliski, R.: Computer vision: algorithms and applications. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  25. Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–517. IEEE (2012)

  26. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  27. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, p. 50. Citeseer, (1988)

  28. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: International Conference on Computer Vision, pp. 2548–2555. IEEE (2011)

  29. Lowe, D.G.: Object recognition from local scale-invariant features. In: Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)

  30. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, pp. 2564–2571. IEEE (2011)

  31. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2008)

    Article  Google Scholar 

  32. Gales, G., Crouzil, A., Chambon, S.: Complementarity of feature point detectors. In: International Conference on Computer Vision Theory and Applications, pp. 334–339, Angers, France, (2010)

  33. Bhowmik, N., Gouet-Brunet, V., Wei, L., Bloch, G.: Adaptive and optimal combination of local features for image retrieval. In: International Conference on Multimedia Modeling, pp. 76–88. Springer, (2017)

  34. Li, S., Yuan, L., Sun, J., Quan, L.: Dual-feature warping-based motion model estimation. In: IEEE International Conference on Computer Vision, pp. 4283–4291 (2015)

  35. 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 

  36. Grundmann, M., Kwatra, V., Essa, I.: Auto-Directed video stabilization with robust l1 optimal camera paths. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)

  37. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  38. Morimoto, C., Chellappa, R.: Evaluation of image stabilization algorithms, In: DARPA Image Understanding, Workshop pp. 295–302 (1997)

Download references

Author information

Authors and Affiliations

  1. Institute of Computing, University of Campinas, Campinas, SP, 13083-852, Brazil

    Marcos Roberto e Souza & Helio Pedrini

Authors
  1. Marcos Roberto e Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Helio Pedrini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Helio Pedrini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

e Souza, M.R., Pedrini, H. Combination of local feature detection methods for digital video stabilization. SIViP 12, 1513–1521 (2018). https://doi.org/10.1007/s11760-018-1307-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1307-8

Keywords

Search

Navigation