Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Similarity Mapping with Enhanced Siamese Network for Multi-Object
  Tracking   Multi-object tracking has recently become an important area of computer
vision, especially for Advanced Driver Assistance Systems (ADAS). Despite
growing attention, achieving high performance tracking is still challenging,
with state-of-the- art systems resulting in high complexity with a large number
of hyper parameters. In this paper, we focus on reducing overall system
complexity and the number hyper parameters that need to be tuned to a specific
environment. We introduce a novel tracking system based on similarity mapping
by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance
and geometric information, and is trainable end-to-end. Our system achieves
competitive performance in both speed and accuracy on MOT16 challenge, compared
to known state-of-the-art methods.

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The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin