Skip to main content

Advertisement

SpringerLink
Log in

Geometry of Motion for Video Shakiness Detection

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

This paper presents a novel algorithm for automatically detecting global shakiness in casual videos. Perframe amplitude is computed by the geometry of motion, based on the kinematic model defined by inter-frame geometric transformations. Inspired by motion perception, we investigate the just-noticeable amplitude of shaky motion perceived by the human visual system. Then, we use the thresholding contrast strategy on the statistics of per-frame amplitudes to determine the occurrence of perceived shakiness. For testing the detection accuracy, a dataset of video clips is constructed with manual shakiness label as the ground truth. The experiments demonstrate that our algorithm can obtain good detection accuracy that is in concordance with subjective judgement on the videos in the dataset.

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

Similar content being viewed by others

References

  1. Abdollahian G, Taskiran C M, Pizlo Z, Delp E J. Camera motion-based analysis of user generated video. IEEE Trans. Multimedia, 2010, 12(1): 28-41.

    Article  Google Scholar 

  2. Hu S M, Chen T, Xu K, Cheng M M, Martin R R. Internet visual media processing: A survey with graphics and vision applications. The Visual Computer, 2013, 29(5): 393-405.

    Article  Google Scholar 

  3. Zhang L, Zhou L, Huang H. Bundled kernels for nonuniform blind video deblurring. IEEE Trans. Circuits and Systems for Video Technology, 2017, 27(9): 1882-1894.

    Article  Google Scholar 

  4. Yan F, Iliyasu A M, Yang H M, Hirota K. Strategy for quantum image stabilization. Science China Information Sciences, 2016, 59(5): 052102.

    Article  Google Scholar 

  5. Kakar P, Sudha N, Ser W. Exposing digital image forgeries by detecting discrepancies in motion blur. IEEE Trans. Multimedia, 2011, 13(3): 443-452.

    Article  Google Scholar 

  6. Su B L, Lu S J, Tan C L. Blurred image region detection and classification. In Proc. the 19th ACM Int. Conf. Multimedia, November 2011, pp.1397-1400.

  7. Yu X, Xu F, Zhang S L, Zhang L. Efficient patch-wise non-uniform deblurring for a single image. IEEE Trans. Multimedia, 2014, 16(6): 1510-1524.

    Article  Google Scholar 

  8. Visentini-Scarzanella M, Dragotti P L. Video jitter analysis for automatic bootleg detection. In Proc. the 14th Int. Workshop on Multi-Media Signal Processing, September 2012, pp.101-106.

  9. Sibiryakov A. Hand jitter descriptor for mobile video identification. In Proc. Int. Conf. Consumer Electronics, January 2011, pp.77-78.

  10. 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 and Systems for Video Technology, 2007, 17(7): 801-813.

    Article  Google Scholar 

  11. Xue Y Y, Erkin B, Wang Y. A novel no-reference video quality metric for evaluating temporal jerkiness due to frame freezing. IEEE Trans. Multimedia, 2015, 17(1): 134-139.

    Article  Google Scholar 

  12. Yan B, Yuan B H, Yang B. Effective video retargeting with jittery assessment. IEEE Trans. Multimedia, 2014, 16(1): 272-277.

    Article  Google Scholar 

  13. 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 Processing, 2015, 24(12): 5982-5994.

    Article  MathSciNet  Google Scholar 

  14. Zhang L, Xu Q K, Huang H. A global approach to fast video stabilization. IEEE Trans. Circuits and Systems for Video Technology, 2017, 27(2): 225-235.

    Article  Google Scholar 

  15. Huang H Z, Fang X N, Ye Y F, Zhang S H, Rosin P L. Practical automatic background substitution for live video. Computational Visual Media, 2017, 3(3): 273-284.

    Article  Google Scholar 

  16. Hasegawa K, Saito H. Synthesis of a stroboscopic image from a hand-held camera sequence for a sports analysis. Computational Visual Media, 2016, 2(3): 277-289.

    Article  Google Scholar 

  17. Joshi N, Kienzle W, Toelle M, Uyttendaele M, Cohen M F. Real-time hyperlapse creation via optimal frame selection. ACM Trans. Graphics, 2015, 34(4): Article No. 63.

  18. Wang M, Liang J B, Zhang S H, Lu S P, Shamir A, Hu S M. Hyper-lapse from multiple spatially-overlapping videos. IEEE Trans. Image Processing, 2018, 27(4): 1735-1747.

    Article  MathSciNet  Google Scholar 

  19. Tong H H, Li M J, Zhang H J, Zhang C S. Blur detection for digital images using wavelet transform. In Proc. Int. Conf. Multimedia and Expo., June 2004, pp.17-20.

  20. Tico M, Trimeche M, Vehvilainen M. Motion blur identification based on differently exposed images. In Proc. Int. Conf. Image Processing, October 2006, pp.2021-2024.

  21. Liu R T, Li Z R, Jia J Y. Image partial blur detection and classification. In Proc. Conf. Computer Vision and Pattern Recognition, June 2008.

  22. YanW Q, Kankanhalli M S. Detection and removal of lighting & shaking artifacts in home videos. In Proc. ACM Int. Conf. Multimedia, December 2002, pp.107-116.

  23. Liu F, Gleicher M, Jin H L, Agarwala A. Content-preserving warps for 3D video stabilization. ACM Trans. Graphics, 2009, 28(3): Article No. 44.

  24. Zhang L, Chen X Q, Kong X Y, Huang H. Geodesic video stabilization in transformation space. IEEE Trans. Image Processing, 2017, 26(5): 2219-2229.

    Article  MathSciNet  Google Scholar 

  25. Wolpert D M, Ghahramani Z. Computational principles of movement neuroscience. Nature Neuroscience, 2000, 3(Suppl): 1212-1217.

  26. Murray R M, Li Z X, Sastry S S. A Mathematical Introduction to Robotic Manipulation. CRC Press, 1994.

  27. Zacur E, Bossa M, Olmos S. Left-invariant Riemannian geodesics on spatial transformation groups. SIAM Journal on Imaging Sciences, 2014, 7(3): 1503-1557.

    Article  MathSciNet  MATH  Google Scholar 

  28. Duan L Y, Jin J S, Tian Q, Xu C S. Nonparametric motion characterization for robust classification of camera motion patterns. IEEE Trans. Multimedia, 2006, 8(2): 323-340.

    Article  Google Scholar 

  29. Afonso M V, Nascimento J C, Marques J S. Automatic estimation of multiple motion fields from video sequences using a region matching based approach. IEEE Trans. Multimedia, 2013, 16(1): 1-14.

    Article  Google Scholar 

  30. Nishi K, Onda T. Evaluation system for camera shake and image stabilizers. In Proc. Int. Conf. Multimedia and Expo., July 2010, pp.926-931.

  31. Albright T D, Stoner G R. Visual motion perception. Proceedings the National Academy of Sciences of the United States of America, 1995, 92(7): 2433-2440.

    Article  Google Scholar 

  32. Peli E, García-Pérez M A. Motion perception during involuntary eye vibration. Experimental Brain Research, 2003, 149(4): 431-438.

    Article  Google Scholar 

  33. Healey C G, Sawant A P. On the limits of resolution and visual angle in visualization. ACM Trans. Applied Perception, 2012, 9(4): Article No. 20.

  34. Martins A J, Kowler E, Palmer C. Smooth pursuit of small-amplitude sinusoidal motion. Journal of the Optical Society of America A, 1985, 2(2): 234-242.

    Article  Google Scholar 

  35. He K M, Chang H W, Sun J. Content-aware rotation. In Proc. Int. Conf. Computer Vision, December 2013, pp.553-560.

  36. Shi J B, Tomasi C. Good features to track. In Proc. Computer Society Conf. Computer Vision and Pattern Recognition, June 1994, pp.593-600.

Download references

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China

    Xiao-Qun Wu, Hai-Sheng Li, Jian Cao & Qiang Cai

  2. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China

    Xiao-Qun Wu, Hai-Sheng Li, Jian Cao & Qiang Cai

Authors
  1. Xiao-Qun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hai-Sheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao-Qun Wu.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 306 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, XQ., Li, HS., Cao, J. et al. Geometry of Motion for Video Shakiness Detection. J. Comput. Sci. Technol. 33, 475–486 (2018). https://doi.org/10.1007/s11390-018-1832-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-018-1832-5

Keywords

Search

Navigation