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|a 10.1109/TPAMI.2013.242
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|a eng
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| 100 |
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|a Ochs, Peter
|e verfasserin
|4 aut
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| 245 |
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|a Segmentation of Moving Objects by Long Term Video Analysis
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|c 2014
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|a Text
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|a Date Completed 25.11.2015
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|a Date Revised 10.09.2015
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate that motion will be exploited most effectively, if it is regarded over larger time windows. Opposed to classical two-frame optical flow, point trajectories that span hundreds of frames are less susceptible to short-term variations that hinder separating different objects. As a positive side effect, the resulting groupings are temporally consistent over a whole video shot, a property that requires tedious post-processing in the vast majority of existing approaches. We suggest working with a paradigm that starts with semi-dense motion cues first and that fills up textureless areas afterwards based on color. This paper also contributes the Freiburg-Berkeley motion segmentation (FBMS) dataset, a large, heterogeneous benchmark with 59 sequences and pixel-accurate ground truth annotation of moving objects
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|a Journal Article
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|a Malik, Jitendra
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|a Brox, Thomas
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 36(2014), 6 vom: 01. Juni, Seite 1187-200
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