IEEE Trans Pattern Anal Mach Intell - Segmentation of Moving Objects by Long Term Video Analysis.

Tópicos

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Resumo

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.

Resumo Limpo

motion strong cue unsupervis objectlevel group paper demonstr motion will exploit effect regard larger time window oppos classic twofram optic flow point trajectori span hundr frame less suscept short term variat hinder separ differ object posit side effect result group tempor consist whole video shot properti requir tedious postprocess vast major exist approach suggest work paradigm start semidens motion cue first fill textureless area afterward base color paper also contribut freiburgberkeley motion segment fbms dataset larg heterogen benchmark sequenc pixelaccur ground truth annot move object

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