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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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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