Fast Salient Object Detection in Non-stationary Video Sequences Based on Spatial Saliency Maps

Conference paper
First Online:
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 55)

Abstract

In recent years, a number of methods of salient object detection in images have been proposed in the field of computer vision. However, sometimes the shooting conditions are far from the ideal, and the unpredicted camera jitters significantly impair the quality of video sequences. In this paper, the salient objects are roughly detected from the keyframes of non-stationary video sequences with two main purposes. First, the removal of salient objects helps to estimate a motion in background more accurately. Second, a visibility of salient objects can be improved after stabilization of video sequence. In this sense, the fast generation of multi-feature approximate saliency map is required. Various fast techniques suitable to extract intensity, color, contrast, edge, angle, and symmetry features from the keyframes are discussed. Some of them are based on Gaussian pyramid decomposition. The Law’s 2D convolution kernels are applied for fast estimation of texture energy contrast and texture gradient contrast in particular. The experiments show the acceptable spatial saliency maps in order to obtain good background motion model of non-stationary video sequence.

Keywords

Visual saliency Salient object Spatial saliency map Background detection Motion estimation Video stabilization 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This work was supported by the Russian Fund for Basic Researches, grant no. 16-07-00121 A, Russian Federation.

References

  1. 1.
    Borji, A., Cheng, M.-M., Jiang, H., Li, J.: Salient Object Detection: A Survey. http://arxiv.org/pdf/1411.5878.pdf (2014)
  2. 2.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  3. 3.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. J. Latex Class Files 60(1), 1–8 (2011)Google Scholar
  4. 4.
    Li, X., Li, Y., Shen, C., Dick, A., van den Hengel, A.: Contextual hypergraph modelling for salient object detection. In: IEEE International Conference on Computer Vision (ICCV’2013), pp. 3328–3335. IEEE (2013)Google Scholar
  5. 5.
    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2007), pp. 1–8. IEEE (2007)Google Scholar
  6. 6.
    Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Le Meur, O., Le Callet, P., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 802–817 (2006)CrossRefGoogle Scholar
  8. 8.
    Zhuang, L., Tang, K., Yu, N., Qian, Y.: Fast salient object detection based on segments. In: International Conference on Measuring Technology and Mechatronics Automation (ICMTMA’2009), vol. 1, pp. 469–472. IEEE Computer Society (2009)Google Scholar
  9. 9.
    Liu, Z., Zou, W., Le Meur, O.L.: Saliency tree: a novel saliency detection framework. IEEE Trans. Image Process. 23(5), 1937–1952 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Favorskaya, M., Jain, L.C., Buryachenko, V.: Digital video stabilization in static and dynamic scenes. In: Favorskaya, M.N., Jain, L.C. (eds.) Computer Vision in Control Systems-1, ISRL, vol. 73, pp. 261–309. Springer International Publishing Switzerland (2015)Google Scholar
  11. 11.
    Ejaz, N., Mehmood, I., Baik, S.W.: Efficient visual attention based framework for extracting key frames from videos. Sig. Process. Image Commun. 28(1), 34–44 (2013)CrossRefGoogle Scholar
  12. 12.
    Katramados, I., Breckon T.P.: Real-time visual saliency by division of Gaussians. In: IEEE International Conference on Image Processing, pp. 1741–1744. IEEE Press, New York (2011)Google Scholar
  13. 13.
    Laws, K.I.: Rapid texture identification. SPIE Image Process. Missile Guidance 238, 376–380 (1980)CrossRefGoogle Scholar
  14. 14.
    Fu, K., Gong, C., Yang, J., Zhou, Y., Gu, I.Y.-H.: Superpixel based color contrast and color distribution driven salient object detection. Sig. Process. Image Commun. 28(10), 1448–1463 (2013)CrossRefGoogle Scholar
  15. 15.
    Rosin, P.L.: A simple method for detecting salient regions. Pattern Recogn. 42(11), 2363–2371 (2009)CrossRefMATHGoogle Scholar
  16. 16.
    Favorskaya, M., Buryachenko, V.: Fuzzy-based digital video stabilization in static scenes. In: Tsihrintzis, G.A., Virvou, M., Jain, L.C., Howlett, R.J., Watanabe, T. (eds.) Intelligent Interactive Multimedia Systems and Services in Practice, SIST, vol. 36, pp. 63–83. Springer International Publishing Switzerland (2015)Google Scholar
  17. 17.
    Favorskaya, M., Buryachenko, V.: Video stabilization of static scenes based on robust detectors and fuzzy logic. Frontiers Artific Intellig Appl 254, 11–20 (2013)Google Scholar
  18. 18.
    MSRA10K Salient Object Database. http://mmcheng.net/msra10k/. Accessed 22 Dec 2015
  19. 19.
    IVRG—Images and Visual Representation Grow. http://ivrlwww.epfl.ch/supplementary_material/RK_CVPR09/. Accessed 22 Dec 2015
  20. 20.
    Auto-Detected Video Stabilization with Robust L1 Optimal Camera Paths. http://cpl.cc.gatech.edu/projects/videostabilization/. Accessed 22 Dec 2015
  21. 21.
    Cheng, M.-M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.-M.: Global contrast based salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2011), pp. 409–416. IEEE Press, New York (2011)Google Scholar
  22. 22.
    Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned salient region detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’2009), pp. 1597–1604. IEEE Press, New York (2009)Google Scholar
  23. 23.
    Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2012), pp. 733–740. IEEE Press, New York (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Informatics and Telecommunications, Siberian State Aerospace UniversityKrasnoyarskRussian Federation

Personalised recommendations