Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rejection-Cascade of Gaussians: Real-time adaptive background
  subtraction framework   Background-Foreground classification is a well-studied problem in computer
vision. Due to the pixel-wise nature of modeling and processing in the
algorithm, it is usually difficult to satisfy real-time constraints. There is a
trade-off between the speed (because of model complexity) and accuracy.
Inspired by the rejection cascade of Viola-Jones classifier, we decompose the
Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We
achieve a good improvement in speed without compromising the accuracy with
respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and
17 percent average improvement in accuracy over Wallflowers surveillance
datasets. The CoG is then demonstrated to over the latent space representation
of images of a convolutional variational autoencoder(VAE). We provide initial
results over CDW-2014 dataset, which could speed up background subtraction for
deep architectures.

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The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph