Abstract
The global motion estimation is a cornerstone of successful video stabilization. In current research, the stabilization task of non-stationary video sequence with static scenes is solved using saliency maps and the trajectories of feature descriptors. First, the feature descriptors are built in keyframes with the removed regions of moving foreground objects, which are considered the salient objects. The tracking results of feature descriptors form the feature trajectories. Second, a distinctiveness of a short-term trajectory is evaluated by histogram approach. Third, a temporal coherence of a long-term trajectory is exploited to verify the global motion through a whole video sequence. In this research, the constant flow and the affine flow are considered. The proposed algorithm permits to increase the peak signal to noise ratio up 4–7 dB on the average comparing with conventional stabilization methods in video sequences with static scenes.
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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., Tomilina, A. (2016). Global Motion Estimation Using Saliency Maps in Non-stationary Videos with Static Scenes. 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_12
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DOI: https://doi.org/10.1007/978-3-319-39345-2_12
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