Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Forecasting Interactive Dynamics of Pedestrians with Fictitious Play   We develop predictive models of pedestrian dynamics by encoding the coupled
nature of multi-pedestrian interaction using game theory, and deep
learning-based visual analysis to estimate person-specific behavior parameters.
Building predictive models for multi-pedestrian interactions however, is very
challenging due to two reasons: (1) the dynamics of interaction are complex
interdependent processes, where the predicted behavior of one pedestrian can
affect the actions taken by others and (2) dynamics are variable depending on
an individuals physical characteristics (e.g., an older person may walk slowly
while the younger person may walk faster). To address these challenges, we (1)
utilize concepts from game theory to model the interdependent decision making
process of multiple pedestrians and (2) use visual classifiers to learn a
mapping from pedestrian appearance to behavior parameters. We evaluate our
proposed model on several public multiple pedestrian interaction video
datasets. Results show that our strategic planning model explains human
interactions 25% better when compared to state-of-the-art methods.

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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: stat