Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FisheyeMODNet: Moving Object detection on Surround-view Cameras for
  Autonomous Driving   Moving Object Detection (MOD) is an important task for achieving robust
autonomous driving. An autonomous vehicle has to estimate collision risk with
other interacting objects in the environment and calculate an optional
trajectory. Collision risk is typically higher for moving objects than static
ones due to the need to estimate the future states and poses of the objects for
decision making. This is particularly important for near-range objects around
the vehicle which are typically detected by a fisheye surround-view system that
captures a 360{\deg} view of the scene. In this work, we propose a CNN
architecture for moving object detection using fisheye images that were
captured in autonomous driving environment. As motion geometry is highly
non-linear and unique for fisheye cameras, we will make an improved version of
the current dataset public to encourage further research. To target embedded
deployment, we design a lightweight encoder sharing weights across sequential
images. The proposed network runs at 15 fps on a 1 teraflops automotive
embedded system at accuracy of 40% IoU and 69.5% mIoU.

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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys