Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MixedPeds: Pedestrian Detection in Unannotated Videos using
  Synthetically Generated Human-agents for Training   We present a new method for training pedestrian detectors on an unannotated
set of images. We produce a mixed reality dataset that is composed of
real-world background images and synthetically generated static human-agents.
Our approach is general, robust, and makes no other assumptions about the
unannotated dataset regarding the number or location of pedestrians. We
automatically extract from the dataset: i) the vanishing point to calibrate the
virtual camera, and ii) the pedestrians' scales to generate a Spawn Probability
Map, which is a novel concept that guides our algorithm to place the
pedestrians at appropriate locations. After putting synthetic human-agents in
the unannotated images, we use these augmented images to train a Pedestrian
Detector, with the annotations generated along with the synthetic agents. We
conducted our experiments using Faster R-CNN by comparing the detection results
on the unannotated dataset performed by the detector trained using our approach
and detectors trained with other manually labeled datasets. We showed that our
approach improves the average precision by 5-13% over these detectors.

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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol