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3-D Motion Estimation for Visual Saliency Modeling
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DOI：10.1109/LSP.2013.2277595.png)
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摘要
En 中文
Visual saliency is a probabilistic estimate of how likely a spatial area in an image or video frame is to attract human visual attention relative to other areas. When existing bottom-up saliency models aggregate low-level features to construct a plausible saliency map, only 2-D motion cues are used as motion features, even though videos typically capture dynamic 3-D scenes. In this paper, we introduce 3-D motion into bottom-up saliency modeling for texture-plus-depth videos. We first propose an efficient 3-D motion estimation algorithm, which computes a 3-D motion vector (3DMV) for each sub-block in the frame. Using the computed 3DMVs, we then derive several saliency channels (called 3DMV channels), which are incorporated into a bottom-up saliency model to obtain enhanced saliency maps. Experiments tracking human gaze show that incorporating our 3DMV channels into bottom-up saliency model significantly improves the accuracy of derived saliency maps.
Keyword:
Visual saliency modeling
3-D motion estimation
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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