Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Background Subtraction by Generative Neural Networks   Background subtraction is a significant task in computer vision and an
essential step for many real world applications. One of the challenges for
background subtraction methods is dynamic background, which constitute
stochastic movements in some parts of the background. In this paper, we have
proposed a new background subtraction method, called DBSGen, which uses two
generative neural networks, one for dynamic motion removal and another for
background generation. At the end, the foreground moving objects are obtained
by a pixel-wise distance threshold based on a dynamic entropy map. The proposed
method has a unified framework that can be optimized in an end-to-end and
unsupervised fashion. The performance of the method is evaluated over dynamic
background sequences and it outperforms most of state-of-the-art methods. Our
code is publicly available at https://github.com/FatemeBahri/DBSGen.

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The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph