Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Spatial-Temporal Regularized Correlation Filters for Visual
  Tracking   Discriminative Correlation Filters (DCF) are efficient in visual tracking but
suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has
been suggested to resolve this issue by enforcing spatial penalty on DCF
coefficients, which, inevitably, improves the tracking performance at the price
of increasing complexity. To tackle online updating, SRDCF formulates its model
on multiple training images, further adding difficulties in improving
efficiency. In this work, by introducing temporal regularization to SRDCF with
single sample, we present our spatial-temporal regularized correlation filters
(STRCF). Motivated by online Passive-Agressive (PA) algorithm, we introduce the
temporal regularization to SRDCF with single sample, thus resulting in our
spatial-temporal regularized correlation filters (STRCF). The STRCF formulation
can not only serve as a reasonable approximation to SRDCF with multiple
training samples, but also provide a more robust appearance model than SRDCF in
the case of large appearance variations. Besides, it can be efficiently solved
via the alternating direction method of multipliers (ADMM). By incorporating
both temporal and spatial regularization, our STRCF can handle boundary effects
without much loss in efficiency and achieve superior performance over SRDCF in
terms of accuracy and speed. Experiments are conducted on three benchmark
datasets: OTB-2015, Temple-Color, and VOT-2016. Compared with SRDCF, STRCF with
hand-crafted features provides a 5 times speedup and achieves a gain of 5.4%
and 3.6% AUC score on OTB-2015 and Temple-Color, respectively. Moreover, STRCF
combined with CNN features also performs favorably against state-of-the-art
CNN-based trackers and achieves an AUC score of 68.3% on OTB-2015.

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The categories of this paper are: physics
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The categories of this paper are: math-ph
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The categories of this paper are: eess
17
The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: math