Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sensing accident-prone features in urban scenes for proactive driving
  and accident prevention   In urban cities, visual information along and on roadways is likely to
distract drivers and leads to missing traffic signs and other accident-prone
features. As a solution to avoid accidents due to missing these visual cues,
this paper proposes a visual notification of accident-prone features to
drivers, based on real-time images obtained via dashcam. For this purpose,
Google Street View images around accident hotspots (areas of dense accident
occurrence) identified by accident dataset are used to train a family of deep
convolutional neural networks (CNNs). Trained CNNs are able to detect
accident-prone features and classify a given urban scene into an accident
hotspot and a non-hotspot (area of sparse accident occurrence). For given
accident hotspot, the trained CNNs can classify it into an accident hotspot
with the accuracy up to 90%. The capability of detecting accident-prone
features by the family of CNNs is analyzed by a comparative study of four
different class activation map (CAM) methods, which are used to inspect
specific accident-prone features causing the decision of CNNs, and pixel-level
object class classification. The outputs of CAM methods are processed by an
image processing pipeline to extract only the accident-prone features that are
explainable to drivers with the help of visual notification system. To prove
the efficacy of accident-prone features, an ablation study is conducted.
Ablation of accident-prone features taking 7.7%, on average, of total area in
each image sample causes up to 13.7% more chance of given area to be classified
as a non-hotspot.

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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
18
The categories of this paper are: econ
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: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math