Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Detection of Objects under Periodic Motion with Gaussian Process
  Filtering   Object Detection (OD) is an important task in Computer Vision with many
practical applications. For some use cases, OD must be done on videos, where
the object of interest has a periodic motion. In this paper, we formalize the
problem of periodic OD, which consists in improving the performance of an OD
model in the specific case where the object of interest is repeating similar
spatio-temporal trajectories with respect to the video frames. The proposed
approach is based on training a Gaussian Process to model the periodic motion,
and use it to filter out the erroneous predictions of the OD model. By
simulating various OD models and periodic trajectories, we demonstrate that
this filtering approach, which is entirely data-driven, improves the detection
performance by a large margin.

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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: stat
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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
2
The categories of this paper are: math
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg