Details

Autor(en) / Beteiligte

• Pengfei Wan
• Yunlong Feng
• Cheung, Gene
• Bajic, I. V.
• Au, O. C.

Titel

3-D Motion Estimation for Visual Saliency Modeling

Ist Teil von

• IEEE signal processing letters, 2013-10, Vol.20 (10), p.972-975

Ort / Verlag

IEEE

Erscheinungsjahr

2013

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• 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.