Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Siamese Anchor Proposal Network for High-Speed Aerial Tracking   In the domain of visual tracking, most deep learning-based trackers highlight
the accuracy but casting aside efficiency. Therefore, their real-world
deployment on mobile platforms like the unmanned aerial vehicle (UAV) is
impeded. In this work, a novel two-stage Siamese network-based method is
proposed for aerial tracking, \textit{i.e.}, stage-1 for high-quality anchor
proposal generation, stage-2 for refining the anchor proposal. Different from
anchor-based methods with numerous pre-defined fixed-sized anchors, our
no-prior method can 1) increase the robustness and generalization to different
objects with various sizes, especially to small, occluded, and fast-moving
objects, under complex scenarios in light of the adaptive anchor generation, 2)
make calculation feasible due to the substantial decrease of anchor numbers. In
addition, compared to anchor-free methods, our framework has better performance
owing to refinement at stage-2. Comprehensive experiments on three benchmarks
have proven the superior performance of our approach, with a speed of around
200 frames/s.

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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc