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In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular , we contribute two novel methods that constitute a unified parsing framework. (1) Predictive feature learning from nearly unlimited unlabeled video data. Different from existing(More)
The applicability and performance of motion detection methods dramatically degrade with the increasing noise. In this paper, we propose a robust dictionary-based background subtraction approach, which formulates background modeling as a linear and sparse combination of atoms in a pre-learned dictionary. Motion detection is then implemented to compare the(More)
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