Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-end Learning Improves Static Object Geo-localization in Monocular
  Video   Accurately estimating the position of static objects, such as traffic lights,
from the moving camera of a self-driving car is a challenging problem. In this
work, we present a system that improves the localization of static objects by
jointly-optimizing the components of the system via learning. Our system is
comprised of networks that perform: 1) 5DoF object pose estimation from a
single image, 2) association of objects between pairs of frames, and 3)
multi-object tracking to produce the final geo-localization of the static
objects within the scene. We evaluate our approach using a publicly-available
data set, focusing on traffic lights due to data availability. For each
component, we compare against contemporary alternatives and show
significantly-improved performance. We also show that the end-to-end system
performance is further improved via joint-training of the constituent models.

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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin