Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor
  Repressed Dynamic Regression   Visual tracking has yielded promising applications with unmanned aerial
vehicle (UAV). In literature, the advanced discriminative correlation filter
(DCF) type trackers generally distinguish the foreground from the background
with a learned regressor which regresses the implicit circulated samples into a
fixed target label. However, the predefined and unchanged regression target
results in low robustness and adaptivity to uncertain aerial tracking
scenarios. In this work, we exploit the local maximum points of the response
map generated in the detection phase to automatically locate current
distractors. By repressing the response of distractors in the regressor
learning, we can dynamically and adaptively alter our regression target to
leverage the tracking robustness as well as adaptivity. Substantial experiments
conducted on three challenging UAV benchmarks demonstrate both excellent
performance and extraordinary speed (~50fps on a cheap CPU) of our tracker.

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The categories of this paper are: hep-th
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex