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Regular Paper

Geometry of Motion for Video Shakiness Detection

School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
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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.

Keywords

video shakinesskinematic modelmotion perception

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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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref
[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.
Crossref 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.
Crossref
[9]
Sibiryakov A. Hand jitter descriptor for mobile video identification. In Proc. Int. Conf. Consumer Electronics, January 2011, pp.77-78.
Crossref
[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.
Crossref 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.
Crossref Google Scholar
[12]

Yan B, Yuan B H, Yang B. Effective video retargeting with jittery assessment. IEEE Trans. Multimedia, 2014, 16(1): 272-277.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
[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.
Crossref 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.
Crossref
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[25]

Wolpert D M, Ghahramani Z. Computational principles of movement neuroscience. Nature Neuroscience, 2000, 3(Suppl): 1212-1217.
Crossref Google Scholar
[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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref
[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.
Crossref Google Scholar
[32]

Peli E, García-Pérez M A. Motion perception during involuntary eye vibration. Experimental Brain Research, 2003, 149(4): 431-438.
Crossref 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.
Crossref Google Scholar
[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.
Crossref 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.
Crossref
[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.
Journal of Computer Science and Technology
Volume 33 Issue 3,
May 2018
Pages 475-486
DOI: 10.1007/s11390-018-1832-5
Cite this article:
Wu X-Q, Li H-S, Cao J, et al. Geometry of Motion for Video Shakiness Detection. Journal of Computer Science and Technology, 2018, 33(3): 475-486. https://doi.org/10.1007/s11390-018-1832-5

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Received: 04 January 2018
Revised: 23 March 2018
Published: 11 May 2018
©2018 LLC & Science Press, China
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