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Segmentation of Moving Objects by Long Term Video Analysis
June 2014 (vol. 36 no. 6)
pp. 1-1
Jitendra Malik, Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA
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.
Index Terms:
Trajectory,Motion segmentation,Tracking,Computer vision,Optical imaging,Noise,Adaptive optics,variational methods,Computer vision,motion segmentation,point trajectories
Citation:
Jitendra Malik, "Segmentation of Moving Objects by Long Term Video Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 6, pp. 1-1, June 2014, doi:10.1109/TPAMI.2013.242
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