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

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Intelligent Interactive Multimedia Systems and Services 2016

Part of the book series: Smart Innovation, Systems and Technologies ((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.

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Acknowledgments

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

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Authors and Affiliations

  1. Institute of Informatics and Telecommunications, Siberian State Aerospace University, 31 Krasnoyarsky Rabochy av., Krasnoyarsk, 660037, Russian Federation

    Margarita Favorskaya & Vladimir Buryachenko

Authors
  1. Margarita Favorskaya
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  2. Vladimir Buryachenko
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Corresponding author

Correspondence to Margarita Favorskaya .

Editor information

Editors and Affiliations

  1. Institute for High-Perfor Computing Netw, National Research Council of Italy, Naples, Italy

    Giuseppe De Pietro

  2. Institute for High-Perfo Computing Netwo, National Research Council of Italy, Naples, Italy

    Luigi Gallo

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

  4. University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

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Favorskaya, M., Buryachenko, V. (2016). Fast Salient Object Detection in Non-stationary Video Sequences Based on Spatial Saliency Maps. In: Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2016. Smart Innovation, Systems and Technologies, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-39345-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-39345-2_11

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-39345-2

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