Skip to main content
SpringerLink
Log in

Digital image stabilization based on adaptive motion filtering with feedback correction

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

For online digital image stabilization system, the camera usually moves with diverse and variable modes, which make the motion filtering process difficult to reserve the intentional fluctuations and remove unwanted jitters simultaneously. This paper presents an adaptive motion filtering algorithm with feedback correction. Firstly, based on the low frequency character of intentional motion in adjacent frames, the intentional velocity is regarded as the control variable, thus the modified one dimension Kalman filtering algorithm is proved to converge to a balance state of consistency and stabilization. Secondly, according to the mutual restraint of consistency and stabilization, this paper proposes two corresponding online feedback factors to reflect the immediate filtering performances. Hence, a motion filtering algorithm with improved Kalman filtering and parameter self-adjustment is realized, which can describe the real camera motion flexibly, as well as adapt to its changes. At last, an objective evaluation method for motion filtering is presented from the aspects of integral consistency, integral stabilization and integral robustness. Compared with other classical motion filtering algorithms, experimental results indicate that the proposed algorithm is more fast-computing and adaptive for different moving modes of the camera, which can reserve the intentional motions and remove the jitters steadily.

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. Aguilar WG, Angulo C (2014) Real-time video stabilization without phantom movements for micro aerial vehicles. Springer EURASIP J Image Video Process 46(1):1–13

    Google Scholar 

  2. Ahlem W, Ali W, Adel M (2014) A. Video stabilization with moving object detecting and tracking for aerial video surveillance. Springer Multimed Tools Appl 74(17):6745–6767

    Google Scholar 

  3. Amanatiadis A, Andreadis I, Gasteratos A, Kyriakoulis N (2007) A rotational and translational image stabilization system for remotely operated robots. IEEE International Workshop on Imaging System and Techniques, Krakow, pp 1–5

    Google Scholar 

  4. Dong J, Xia Y, Yu QF, Su A, Hou W (2014) Instantaneous video stabilization for unmanned aerial vehicles. SPIE J Electron Imaging 23(1):013002(1)–013002(10)

  5. Grundmann M, Kwatra V, Essa I (2011) Auto-directed video stabilization with robust L1 optimal camera paths. IEEE International Conference on Computer Vision and Pattern Recognition, Providence, pp 225–232

    Google Scholar 

  6. Hore A, Ziou D (2010) Image quality metrics: PSNR vs SSIM. IEEE International Conference on Pattern Recognition, Istanbul, pp 2366–2369

    Google Scholar 

  7. Hu CP, Xu Z, Liu YH et al (2014) Semantic link network based model for organizing multimedia big data. IEEE Trans Emerg Top Comput 2(3):376–387

    Article  Google Scholar 

  8. Hu CP, Xu Z, Liu YH et al (2015) Video structured description technology for the new generation video surveillance systems. Front Comput Sci 9(6):980–989

    Article  Google Scholar 

  9. Kim SW, Yin S, Yun K, Choi JY (2014) Spatio-temporal weighting in local patches for direct estimation of camera motion in video stabilization. ELSEVIER Comput Image Underst 118:71–83

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  11. Kwon O, Shin J, Paik J (2005) Video stabilization using kalman filter and phase correlation matching. Springer Image Anal Recognit 3656(3):141–148

    Article  Google Scholar 

  12. Lamza A, Wrobel Z (2012) New efficient method of digital video stabilization for in-car camera. Springer Multimed Commun Serv Secur Commun Comput Inf Sci 287:180–187

    Google Scholar 

  13. Lee TH, Lee YG, Song BC (2014) Fast 3D video stabilization using ROI-based warping. ELSEVIER J Vis Commun Image Represent 25(5):943–950

    Article  Google Scholar 

  14. Litvin A, Konrad J, Karl VC (2003) Probabilistic video stabilization using kalman filtering and mosaicking. IS&T/SPIE Symposium on Image and Video Communications and Processing, Santa Clara, pp 663–674

    Google Scholar 

  15. Liu F, Gleicher M, Jin H-L, Agarwala A (2009) Content preserving warps for 3D video stabilization. ACM Tran Graph 28(3):1–9

    Google Scholar 

  16. Monteiro E, Vizzotto B, Diniz C, Zatt B, Bampi S (2011) Applying CUDA architecture to accelerate full search block matching algorithm for high performance motion estimation in video encoding. IEEE International Symposium on Computer Architecture and High Perform, Brazil, pp 128–135

    Google Scholar 

  17. Mukesh S, Pradeep KA, Sharad M, Mohan K (2014) W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video. Springer Multimed Tools Appl 68(1):135–158

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Or EM, Pundik D (2007) Hand motion and image stabilization in hand-held devices. IEEE Transa Consum Electron 53(4):1508–1512

    Article  Google Scholar 

  20. Qu H, Song L (2013) Video stabilization with L1-L2 optimization. IEEE International Conference on Image Processing, Melbourne, pp 29–33

    Google Scholar 

  21. Qu H, Song L, Xue GJ (2013) Shaking video synthesis for video stabilization performance assessment. IEEE International Conference on Visual Communications and Image Processing, Kuching, pp 1–6

    Google Scholar 

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

    Article  Google Scholar 

  23. Schwertfeger S, Birk A, Bulow H (2011) Using iFMI spectral registration for video stabilization and motion detection by an Unmanned Aerial Vehicle (UAV). IEEE International Symposium on Safety, Security, and Rescue Robotics, Kyoto, pp 61–67

    Google Scholar 

  24. Song CH, Hai H, Jing W, Zhu HB (2012) Robust video stabilization based on particle filtering with weighted feature points. IEEE Trans Consum Electron 58(2):570–577

    Article  Google Scholar 

  25. Tanakian MJ, Rezaei M, Mohanna F (2011) Real-time video stabilization by adaptive fuzzy filtering. IEEE International Conference on Computer and Knowledge Engineering, Mashhad, pp 126–131

    Google Scholar 

  26. Tsai TH, Fang CL, Chuang HM (2012) 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

    Article  Google Scholar 

  27. Vasileios M, Ansgar S, Ramesh J, Mohan SK (2013) Real-life events in multimedia: detection, representation, retrieval, and applications. Springer Multimed Tools Appl 70(1):1–6

    Google Scholar 

  28. Wang CT, Kim JH, Byun KY, Ni JQ, Ko SJ (2009) Robust digital image stabilization using the kalman filter. IEEE Trans Consum Electron 55(1):6–14

    Article  Google Scholar 

  29. Xu Z, Liu YH, Mei L et al (2015) Semantic based representing and organizing surveillance big data using video structural description technology. Elsevier J Syst Softw 102:217–225

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Zhou ZH, Jin HL, Ma Y (2013) Plane-based content preserving warps for video stabilization. IEEE international conference on Computer Vision and Pattern Recognition, Portland, pp 2299–2306

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank all the anonymous reviewers for their helpful comments and suggestions. This work is supported by the National Science Foundation of China (No.61370124), the National Science Foundation of China for Distinguished Young Scholars (No.61125206), the China 863 Program (Project No. 2014AA015104) and China Scholarship Foundation (No. 201303070205).

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, China

    Bo Zhai, Jin Zheng & Bo Li

  2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Jin Zheng & Bo Li

Authors
  1. Bo Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jin Zheng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhai, B., Zheng, J. & Li, B. Digital image stabilization based on adaptive motion filtering with feedback correction. Multimed Tools Appl 75, 12173–12200 (2016). https://doi.org/10.1007/s11042-015-3183-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3183-3

Keywords

Search

Navigation