Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Robust Background Initialization Algorithm with Superpixel Motion
  Detection   Scene background initialization allows the recovery of a clear image without
foreground objects from a video sequence, which is generally the first step in
many computer vision and video processing applications. The process may be
strongly affected by some challenges such as illumination changes, foreground
cluttering, intermittent movement, etc. In this paper, a robust background
initialization approach based on superpixel motion detection is proposed. Both
spatial and temporal characteristics of frames are adopted to effectively
eliminate foreground objects. A subsequence with stable illumination condition
is first selected for background estimation. Images are segmented into
superpixels to preserve spatial texture information and foreground objects are
eliminated by superpixel motion filtering process. A low-complexity
density-based clustering is then performed to generate reliable background
candidates for final background determination. The approach has been evaluated
on SBMnet dataset and it achieves a performance superior or comparable to other
state-of-the-art works with faster processing speed. Moreover, in those complex
and dynamic categories, the algorithm produces the best results showing the
robustness against very challenging scenarios.

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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org