Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional
  Assignment for Online Multiple Object Tracking   Data association-based multiple object tracking (MOT) involves multiple
separated modules processed or optimized differently, which results in complex
method design and requires non-trivial tuning of parameters. In this paper, we
present an end-to-end model, named FAMNet, where Feature extraction, Affinity
estimation and Multi-dimensional assignment are refined in a single network.
All layers in FAMNet are designed differentiable thus can be optimized jointly
to learn the discriminative features and higher-order affinity model for robust
MOT, which is supervised by the loss directly from the assignment ground truth.
We also integrate single object tracking technique and a dedicated target
management scheme into the FAMNet-based tracking system to further recover
false negatives and inhibit noisy target candidates generated by the external
detector. The proposed method is evaluated on a diverse set of benchmarks
including MOT2015, MOT2017, KITTI-Car and UA-DETRAC, and achieves promising
performance on all of them in comparison with state-of-the-arts.

10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
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The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn