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Bottom-up spatiotemporal visual attention model for video analysis

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4 Author(s)
Rapantzikos, K. ; Sch. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Zografou ; Tsapatsoulis, N. ; Avrithis, Y. ; Kollias, S.

The human visual system (HVS) has the ability to fixate quickly on the most informative (salient) regions of a scene and therefore reducing the inherent visual uncertainty. Computational visual attention (VA) schemes have been proposed to account for this important characteristic of the HVS. A video analysis framework based on a spatiotemporal VA model is presented. A novel scheme has been proposed for generating saliency in video sequences by taking into account both the spatial extent and dynamic evolution of regions. To achieve this goal, a common, image-oriented computational model of saliency-based visual attention is extended to handle spatiotemporal analysis of video in a volumetric framework. The main claim is that attention acts as an efficient preprocessing step to obtain a compact representation of the visual content in the form of salient events/objects. The model has been implemented, and qualitative as well as quantitative examples illustrating its performance are shown.

Published in:

Image Processing, IET  (Volume:1 ,  Issue: 2 )