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- W2076756823 abstract "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." @default.
- W2076756823 created "2016-06-24" @default.
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- W2076756823 date "2014-06-01" @default.
- W2076756823 modified "2023-10-18" @default.
- W2076756823 title "Segmentation of Moving Objects by Long Term Video Analysis" @default.
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- W2076756823 doi "https://doi.org/10.1109/tpami.2013.242" @default.
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