Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Background Reconstruction and Foreground Segmentation via A
  Two-stage Convolutional Neural Network   Foreground segmentation in video sequences is a classic topic in computer
vision. Due to the lack of semantic and prior knowledge, it is difficult for
existing methods to deal with sophisticated scenes well. Therefore, in this
paper, we propose an end-to-end two-stage deep convolutional neural network
(CNN) framework for foreground segmentation in video sequences. In the first
stage, a convolutional encoder-decoder sub-network is employed to reconstruct
the background images and encode rich prior knowledge of background scenes. In
the second stage, the reconstructed background and current frame are input into
a multi-channel fully-convolutional sub-network (MCFCN) for accurate foreground
segmentation. In the two-stage CNN, the reconstruction loss and segmentation
loss are jointly optimized. The background images and foreground objects are
output simultaneously in an end-to-end way. Moreover, by incorporating the
prior semantic knowledge of foreground and background in the pre-training
process, our method could restrain the background noise and keep the integrity
of foreground objects at the same time. Experiments on CDNet 2014 show that our
method outperforms the state-of-the-art by 4.9%.

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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
3
The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph