Saliency Driven Video Motion Magnification

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 841)


The main goal of the proposed work is to detect certain spatial and temporal changes in videos that are not visible to the human eye and magnify them in order to make them perceptible while making sure that the background noise is not amplified. We apply Eulerian motion magnification on only the salient area of each frame of the video. The salient object is processed independent of the rest of the image using alpha matting aided by scribbles. We demonstrate the need to isolate the salient object from background motions and propose a simple and efficient way to do so. The proposed algorithm is tested on videos with imperceptible motion along with background motion to illustrate the significance of the proposed method. We compare the proposed method with linear and phase based Eulerian motion magnification techniques.



The authors would like to thank SERB-DST for support through Young Scientists Startup Research Grant.


  1. 1.
    Liu, C., Torralba, A., Freeman, W.T., Durand, F., Adelson, E.H.: Motion magnification. ACM Trans. Graph. 24(3), 519–526 (2005)CrossRefGoogle Scholar
  2. 2.
    Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4), 1–8 (2012)CrossRefGoogle Scholar
  3. 3.
    Peng, H., Li, B., Ling, H., Hu, W., Xiong, W., Maybank, S.J.: Salient object detection via structured matrix decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 818–832 (2017)CrossRefGoogle Scholar
  4. 4.
    Wang, J., Drucker, S.M., Agrawala, M., Cohen, M.F.: The cartoon animation filter. ACM Trans. Graph. 25(3), 1169–1173 (2006)CrossRefGoogle Scholar
  5. 5.
    Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.T.: Phase-based video motion processing. ACM Trans. Graph. 32(4), 80 (2013)CrossRefGoogle Scholar
  6. 6.
    Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.T.: Riesz pyramids for fast phase-based video magnification. In: Proceedings of IEEE International Conference on Computational Photography, pp. 1–10 (2014)Google Scholar
  7. 7.
    Balakrishnan, G., Durand, F., Guttag, J.: Detecting pulse from head motions in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3430–3437 (2013)Google Scholar
  8. 8.
    Hong, K.: Classification of emotional stress and physical stress using facial imaging features. J. Opt. Technol. 83(8), 508–512 (2016)CrossRefGoogle Scholar
  9. 9.
    Bharadwaj, S., Dhamecha, T.I., Vatsa, M., Singh, R.: Computationally efficient face spoofing detection with motion magnification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 105–110 (2013)Google Scholar
  10. 10.
    Park, S.Y., Lee, S.H., Ro, Y.M.: Subtle facial expression recognition using adaptive magnification of discriminative facial motion. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 911–914. ACM (2015)Google Scholar
  11. 11.
    He, X., Goubran, R.A., Liu, X.P.: Using Eulerian video magnification framework to measure pulse transit time. In: 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–4. IEEE (2014)Google Scholar
  12. 12.
    Raghavendra, R., Avinash, M., Marcel, S., Busch, C.: Finger vein liveness detection using motion magnification. In: 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–7. IEEE (2015)Google Scholar
  13. 13.
    Elgharib, M., Hefeeda, M., Durand, F., Freeman, W.T.: Video magnification in presence of large motions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4119–4127 (2015)Google Scholar
  14. 14.
    Kooij, J.F.P., van Gemert, J.C.: Depth-aware motion magnification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 467–482. Springer, Cham (2016). Scholar
  15. 15.
    Zhang, Y., Pintea, S.L., van Gemert, J.C.: Video acceleration magnification. arXiv preprint arXiv:1704.04186 (2017)
  16. 16.
    Sonane, B., Ramakrishnan, S., Raman, S.: Automatic video matting through scribble propagation. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, vol. 87, no. (1–87), p. 8 (2016)Google Scholar
  17. 17.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. Technical report (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Electrical EngineeringIIT GandhinagarGandhinagarIndia
  2. 2.Computer Science EngineeringPES UniversityBengaluruIndia

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